Draft Final Report - Rhythm Engineering...SW 8th Street Adaptive Signal Control Evaluation Draft...

SW 8th Street Adaptive Signal Control Evaluation

Draft Final Report

By

Mohammed Hadi, Ph.D., P.E. Tao Wang, Ph.D.

Lehman Center for Transportation Research Florida International University

Prepared for Florida Department of Transportation District 6

September 2019

Page 1

Quality Assurance Statement

This document was originally written by Dr. Tao Wang. It has been reviewed and

edited by Dr. Mohammed Hadi, PE.

Page 2

Executive Summary

An adaptive signal control technology (ASCT) system has the potential to improve transportation

system mobility, reliability, safety, and environmental impacts by accommodating changing traffic

demands at signalized intersections. The Florida Department of Transportation (FDOT) District

6 has deployed an adaptive signal control system between SW 67th Avenue and SW 142nd Avenue

along SW 8th Street in Miami-Dade County, as shown in Figure E - 1.

Figure E - 1: Study Area and Signalized Intersections with ASCT

The installed ASCT is the InSync Fusion adaptive traffic control system developed by Rhythm

Engineering. It is an intelligent transportation system that enables traffic signals to adapt to actual

traffic demand. At the site level, the InSync installation involved installing the InSync processor,

an industrial-grade micro controller that integrates with the traffic cabinets and controllers via

industry standard connections. It also involved installing video image detection (VID) cameras

for traffic detection.

The goal of this project was agreed on in a stakeholder workshop conducted as part of an effort to

develop an evaluation plan for the project, as follows: “The project goal is to improve the

efficiency of SW 8th Street, between SW 142nd Avenue and SW 67th Avenue, using sustainable

signal technology to minimize congestion and increase throughput where possible, without

compromising safety for all users.”

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This document presents the evaluation of the system according to a previously developed

evaluation plan, developed by the evaluation team, as part of the project. Please note that although

the evaluation was done to determine the impact of the installed technology, FDOT District 6 has

been effectively involved in the day-to-day management and operations of the facility. Thus, the

benefits presented in this report reflected these activities also. The evaluation of the system was

done for the before and after period, and this report clearly shows the changes of performances

between the two periods. It is expected that the changes in performance are due to the deployed

system.

Table E - 1: Boundaries and Distances of Main Street Segments

Segment Boundary

Average Distance

between Intersections

(miles)

Length

(miles)

I between SW 67th Avenue and SR

826 NB On-Ramp 0.17 1.0

II between SR 826 NB On-Ramp and

SR 826 SB On-Ramp 0.2 0.2

III between SW 82nd Avenue and

Turnpike NB On-Ramp 0.49 3.4

IV between Turnpike NB On-Ramp

and SW 142th Avenue 0.45 2.7

ALL between SW 67th Avenue and SW

142th Avenue 0.36 7.5

To facilitate the estimation of the system performance of the corridor, the main street was divided

into four segments, which are referred to as Segments I, II, III, and IV, respectively as shown in

Table E - 1. These segments correspond to the coordinated sub-systems as previously set by

Miami-Dade County Department of Transportation & Public Works (MDC DTPW) in the time of

day operations of the corridor. In addition to these four segments, the entire study corridor along

SW 8th Street is assessed and referred to as Segment ALL in this document. To estimate how the

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adaptive signal control system affects the cross streets, the performance measures for four cross

streets were assessed in the before-conditions. However, due to the construction activities and lack

of data for SW 107th Ave in the after conditions, the comparison results are only presented for the

three cross streets (SW 87th Ave, SW 97th Ave, and SW 137th Ave) in this report.

Data from multiple sources were collected and processed to determine the impacts of deployments

on various performance measures. Three sources of travel time estimates were utilized to assess

the mobility performance: Wi-Fi reader data and two third-party-based travel time data, which are

HERE and the National Performance Management Research Data Set (NPMRDS) data. All three

data sources show a decrease in travel time on the main street and cross streets after ASCT

operations compared to the before condition. For the main street on the whole segment, the

evaluation based on HERE data showed the highest improvement ranging from 7.3% to 20.2%,

depending on the direction of travel and the peak period with a median improvement of 12.5%.

The corresponding values based on the NPMRDS was a range of improvement between 1.7% and

7.9%, with a median of 5.85%. The evaluation based on the Wi-Fi data showed a range of

improvement between -3.4% and 8.3%, with a median value of 3.65%. The only negative impact

value obtained in the evaluation was the -3.4% for the eastbound direction in the AM peak. This

small change is acceptable considering the improvements in other movement performance and the

increase in throughput, as described below. In terms of total delay in veh-hr aggregated over the

whole day, the improvements were 4.3%, 24.2%, and 6.9% based on the Wi-Fi data, HERE, and

NPMRDS, with an average of 11.8% among the three data sources.

The analysis showed significant improvements in cross street delays in terms of veh-hr. When

considering both directions of the cross streets and aggregating overall periods of the day, the

analysis based on Wi-Fi data showed improvements of 20.6% and 9.8% for SW 87th Avenue and

SW 97th Avenue, respectively. The analysis based on HERE data showed improvements of 23.5%,

15.4%, and 17.2% for SW 87th Avenue, SW 97th Avenue, and SW 137th Avenue, respectively.

The Wi-Fi data for SW 137th Avenue had quality issues that prevented it from being used in the

analysis. Data from the NPMRDS are not available for the evaluated cross streets since they are

not on the National Highway System.

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In general, the evaluation showed a reduction in the percentage of time in congestion on the

corridor. In particular, the analysis based on HERE data show a very high reduction in the

congestion percentage. As examples, the HERE data analysis results show that the percentage of

time in congestion for the WB during the PM peak decreased from 78.6% to 50.1% and the

percentage for the EB during the PM peak decreased from 24.4% to 4.73%. In addition, the

throughput analysis indicates that there is an increase in the throughput of the system by an average

of about 5% to 6%.

For safety analysis, this study utilized the Observational Before/After Evaluation using Safety

Performance Functions (the Empirical Bayes Method) recommended in the Highway Safety

Manual (HSM). This method showed a reduction in crash frequency on the main street by 9.73%.

For the cross streets, the estimated reductions in the crash frequency were 11.6%, 4.05%, and

9.74% for SW 87th Avenue, SW 97th Avenue, and SW 137th Avenue; respectively.

While reliability analysis results based on Wi-Fi data showed that there was no significant change

in the reliability of the main street, the 80th percentile Travel Time Rate (TTR), a measure of

reliability, improved between 4.8% and 20.1% based on HERE data and between 3.7% and 9.4%

based on NPMRDS data. In addition, there were significant reliability improvements for both

directions of the evaluated cross streets.

Various hypothesis testing was also conducted to verify the effectiveness of the ASCT, considering

the stochasticity of the measured variables. The hypothesis testing results were found to be

consistent with the findings mentioned above. A return on investment analysis was also conducted

by calculating the benefit-cost ratio (B-C Ratio) for 5-year project life. The results show that the

B-C Ratios calculated based on Wi-Fi data and HERE data are 3.7 and 8.4, respectively. It’s worth

noting that the benefits include the operational benefits only. If the safety benefits were included,

the B-C Ratio would be even higher. The safety benefits will be added when three year of crash

data becomes available for the after conditions.

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E.1 Mobility Measures

Mobility measures, including travel times, speeds, delays, and percentage of time in congestion,

were estimated based on data collected utilizing the Wi-Fi readers and the third-party-based travel

time data from two sources, which are HERE and the National Performance Management Research

Data Set (NPMRDS) data.

The adaptive signal control technology (ASCT) system along SW 8th Street was first activated on

April 22, 2017, but it was suspended on Sep 7, 2017 due to Hurricane Irma and then reactivated

on May 16, 2018, so there are two before-after phases to compare in this study. The phase before

Hurricane Irma is named “Phase I”, and the before and after periods in this phase are named

“Before-I” and “After-I”, respectively. The phase after the hurricane is named “Phase II”, and the

before and after periods in this phase are named “Before-II” and “After-II”, respectively. The date

ranges of the before and after conditions selected for this study are listed in Table E - 2. For Phase-

I, August 2015 and August 2017 were selected so that the volume related measures can be

estimated, since 7-day tube counts were performed in these months. In this study, three time

periods were selected for the analysis: the AM peak (7:00 AM to 9:00 AM), the midday (9:00 AM

to 3:30 PM), and the PM peak (3:30 PM to 7:00 PM).

Table E - 2: Date Ranges of Before and After Conditions

Phase Before/After Date Range

Phase I Before-I August 2015

After-I August 2017

Phase II Before-II from October 2017 to April 2018

After-II from October 2018 to April 2019

E.1.1 Main Street Travel Time

Figure E - 2 shows a comparison between the travel times in the before and after conditions for

Phase I based on travel time data collected using Wi-Fi data. The results show that, for all four

study segments, there was a reduction in the average travel times for the eastbound (EB) direction

Page 7

during the PM peak period and westbound (WB) direction during the AM peak period. These are

considered as the off-peak travel directions in the two peaks, although they still carry relatively

high volumes. Specifically, the average travel time of the WB during the AM peak for Segment

ALL decreased by about 5.8 minutes (about 30%). The corresponding reduction in travel time in

the EB direction in the PM peak was 2.5 Minutes (10.4%). For the EB direction during the AM

peak (the peak direction during the AM peak), the average travel times of Segment I and III

decreased, but the travel times of Segment II (a short segment that is very congested in this peak)

and Segment IV increased. The overall change for Segment ALL (the whole corridor) in the EB

direction in the AM peak was an increase of 1.5 minutes (about 5% increase). For the WB direction

during the PM peak, the average travel times of Segments I and III increased slightly, but the travel

times for Segments II and IV decreased. The travel time for the ALL Segment in the WB direction

increased 0.43 minutes (1.3%) in the PM peak.

Figure E - 2: Segment ALL Travel Time - Phase I Analysis based on Wi-Fi Data

Figure E - 3 shows the results of the assessment of the travel time for Segment ALL according to

Phase II analysis based on Wi-Fi data. The comparison between the before and after conditions

shows that, there was a reduction in the average travel time for all four segments and all time

periods and directions, except the EB (peak direction) during the AM peak. The travel time for

the EB during the AM peak increased by an average of 0.8 minutes (3.4%), while the travel times

of the EB during the PM peak and the WB during the AM peak decreased by 1.2 minutes (5.5%)

and 1 minute (5.2%), respectively. The peak direction during the PM, which is the WB, had a

Page 8

slight reduction in travel time of 0.1 minute (0.5%). As for the mid-day period, the travel time

for the EB and WB periods decreased by 0.4 and 1.5 minutes (2.1% and 8.3%), respectively.

Figure E - 3: Segment ALL Travel Time - Phase II Analysis based on Wi-Fi Data

For phase II, the assessment of the travel time impacts was also repeated with third-party travel

time data. Figure E - 4 and Figure E - 5 show the travel time comparison results based on HERE

data and NPMRDS data, respectively. The results show reductions in the travel times of all main

street directions and time periods. In particular, the analysis based on HERE data shows the

highest improvements among the three utilized data sources. The reductions in travel times for

the EB direction according to HERE data for the AM, midday, and PM time periods were 1.9

minutes (7.3%), 2.1 minutes (10.2%), and 2.8 minutes (13.1%), respectively. For the WB

direction, the corresponding values were 2.3 minutes (11.9%), 3.1 minutes (15.8%), and 5.9

minutes (20.2%), respectively. The analysis based on NPMRDS data shows less improvements

compared to the HERE data, with the degree of improvements being closer to the results from the

analysis based on Wi-Fi data, except that the analysis based on NPMRDS data also indicated

reduced travel times for the EB direction during the AM peak, meaning that both directions of

travel had reductions in travel times for all time periods according to this analysis. The travel time

reduction based on NPMRDS data are 2.1 minutes (7.0%), 0.9 minutes (4.1%), and 1.3 minutes

(5.1%) for the AM, midday, and PM periods in EB direction and 1.7 minutes (7.9%), 1.4 minutes

(6.6%), and 0.5 minutes (1.7%) for the three time periods in the WB direction, respectively.

05

101520253035

AM MD PM AM MD PM

EB WB

Trav

el T

ime

(M

inu

tes)

After-II Before-II

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Segment ALL

Figure E - 4: Segment ALL Travel Time - Phase II Analysis based on HERE Data

Segment ALL

Figure E - 5: Segment ALL Travel Time - Phase II Analysis based on NPMRDS Data

The above shows that all three data sources show a decrease in travel time on the main street. For

the main street on the whole segment, the evaluation based on HERE data showed the highest

improvement ranging from 7.3% to 20.2%, depending on the direction of travel and the peak period

with a median improvement of 12.5%. The corresponding values based on the NPMRDS was a

range of improvement between 1.7% and 7.9%, with a median of 5.85%. The evaluation based on

the Wi-Fi data showed a range of improvement between -3.4% and 8.3%, with a median value of

3.65%. The only negative impact value obtained in the evaluation was the -3.4% for the eastbound

05

101520253035

AM MD PM AM MD PM

EB WB

Trav

el T

ime

(M

inu

tes)

After-II Before-II

05

101520253035

AM MD PM AM MD PM

EB WB

Trav

el T

ime

(M

inu

tes)

After-II Before-II

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direction in the AM peak. This small change is acceptable considering the improvements in other

movement performance and the increase in throughput, as described in the sections below.

E.1.2 Cross Street Speed

Figure E - 6 shows the results of Phase I analysis of cross street speeds based on Wi-Fi data. It

can be seen that there were no significant speed changes for SW 97th Ave. However, the speeds

of SW 87th improved in both directions and all three time periods. The improvement was

particularly high for the SW 87th Ave SB. Figure E - 7 shows the speeds for the cross streets

according to Phase II analysis based on Wi-Fi data. For most directions and time periods, SW 87th

Avenue and SW 97th Avenue had improved speed. The results for phase II based on HERE data

as provided in Figure E - 8 show that all cross streets improved speeds for all directions and time

periods. This is further reflected in the delay analysis results reported next. No analysis based on

NPMRDS data was conducted for the cross streets since this data is not available for the cross

streets. The results for SW 137th Avenue are provided based on only HERE data, because it was

found that the quality of the Wi-Fi data for SW 137th Avenue was too poor to include in the

analysis, as it shows almost constant travel times for all peak periods and scenarios.

.

SW 87th Ave

SW 97th Ave

Figure E - 6: Cross Streets Speed based on Wi-Fi Data - Phase I Analysis

0

5

10

15

20

25

30

35

AM MD PM AM MD PM

SB NB

Spe

ed

(M

PH

)

After-I Before-I

0

5

10

15

20

25

30

35

AM MD PM AM MD PM

SB NB

Spe

ed

(M

PH

)

After-I Before-I

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SW 87th Ave

SW 97th Ave

Figure E - 7: Cross Streets Speed based on Wi-Fi Data - Phase II Analysis

0

5

10

15

20

25

30

35

AM MD PM AM MD PM

SB NB

Spe

ed

(M

PH

)

After-II Before-II

0

5

10

15

20

25

30

35

AM MD PM AM MD PM

SB NB

Spe

ed

(M

PH

)

After-II Before-II

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SW 87th Ave

SW 97th Ave

SW 137th Ave

Figure E - 8: Cross Streets Speed based on HERE Data – Phase II Analysis

E.1.3 Total Delay

Segment delay is defined as the difference between the time it takes to travel along a given segment

and the time it would have taken to travel along that segment at the free-flow speed without stops.

In this study, the speed limits of the corridor were used as the free-flow speeds. Two types of

delays were calculated and compared. The first is the average delay in minutes per vehicle and

the second is the hourly total delay in veh-hr/hr, which is the delay in minute per vehicle multiplied

by the average hourly volume. The average delay is the measure considered from a traveler point

of view, while the total delay is the measure considered from the system point of view and is used

in benefit-cost analysis, as discussed later in this document.

0

5

10

15

20

25

30

35

AM MD PM AM MD PM

SB NB

Spe

ed

(M

PH

)

After-II Before-II

0

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AM MD PM AM MD PM

SB NB

Spe

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(M

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After-II Before-II

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AM MD PM AM MD PM

SB NB

Spe

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(M

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After-II Before-II

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Table E - 3 shows the change in total delay in veh-hr/hr of the main street movements. The results

of Phase 1 assessment of the main street total delay based on Wi-Fi data indicate significant

improvements in the non-peak directions during the AM and PM peak periods. The total delay of

the WB direction in the AM peak and the EB direction in the PM peak decreased by 31.2% and

22.7% respectively. The delay of the WB direction in the PM peak (the peak direction in the PM)

increased by 3.8%, and the EB direction in the AM peak (the peak direction in the AM) had a 0.8%

increase in total delay. The total delay of the main street in the midday improved by 5.3%.

Overall, there was a 6.4% reduction in total delay of the main street for the analyzed 12 hours of

the day. Phase II analysis based on Wi-Fi data showed less total delay benefits to the main street

compared to Phase I analysis. Again, the non-peak direction in the peak hour had more positive

results than the peak direction but the reductions in travel time for the non-peak directions (the EB

in the PM and WB in the AM) were lower than those based on Phase I analysis, ranging from 7.7%

to 9.1%. While the total delay of the EB in the AM period increased by 7.0%, the total delay of

the WB in the PM period decreased by 0.8%, and the total delay of the main street decreased by

8.0% in the midday period. Phase II analysis based on Wi-Fi data indicates there was a 4.3%

reduction in total delay of the main street for the analyzed 12 hours of the analysis.

Page 14

Table E - 3: Total Delay of Main Street Traffic Based on Different Data Sources

Phase Data Peak Direction Main Street Delay veh-hr/hr

Before After Difference % Difference

I Wi-Fi

AM

EB 488 464 25 5.1

WB 233 157 76 32.4

Both 721 621 100 13.9

MD

EB 336 316 20 6.1

WB 245 235 10 4.1

Both 582 551 31 5.2

PM

EB 385 301 84 21.8

WB 645 682 -36 -5.6

Both 1030 983 48 4.6

All 8831 8265 566 6.4

II

Wi-Fi

AM

EB 463 496 -32 -7.0

WB 206 187 19 9.1

Both 669 683 -14 -2.1

MD

EB 277 270 7 2.4

WB 221 188 33 14.9

Both 498 458 40 8.0

PM

EB 328 303 25 7.7

WB 602 597 5 0.8

Both 930 900 30 3.2

All 7832 7495 336 4.3

HERE

AM

EB 548 480 69 12.5

WB 231 180 51 22.2

Both 779 659 120 15.4

MD

EB 317 255 62 19.6

WB 265 185 80 30.3

Both 582 440 143 24.5

PM

EB 305 234 71 23.2

WB 682 477 205 30.0

Both 987 712 275 27.9

All 8798 6667 2131 24.2

NPMRDS

AM

EB 705 663 42 6.0

WB 269 246 23 8.7

Both 974 909 66 6.8

MD

EB 377 351 26 6.9

WB 282 246 35 12.5

Both 659 597 61 9.3

PM

EB 395 362 33 8.3

WB 674 663 11 1.7

Both 1069 1025 44 4.1 All 9972 9287 685 6.9

Page 15

The Phase II analysis based on HERE data in Table E - 3 shows significant improvements in total

delay for all main street movements. The improvements in delay are significantly higher than what

was estimated based on Wi-Fi data. The assessed improvement percentage in veh-hr for the two

directions of main street travel for the three analysis periods based on HERE data ranges from

15.4% to 27.9%, with an overall improvement for the 12-hour analysis period of 24.2%. The

analysis based on NPMRDS data also shows that both directions had lower total delay in the after

period but to a much less extent than what the analysis based on HERE data indicates and more in

line with the results of the analysis based on Wi-Fi data. The improvement in delay of the EB

direction in the AM, midday, and PM peak periods according to NPMRDS analysis are 6.0%,

6.9%, and 8.3%; respectively. The improvement for the westbound direction in the AM, midday,

and PM peak periods according to NPMRDS analysis are 8.7%, 12.5%, and 1.7%; respectively.

The reduction in delay of the main street when considering the 12 hours of the analysis is 6.9%.

It can be concluded based on the above results that the improvements in the total delay in veh-hr.

when aggregated over the whole day, are 4.3%, 24.2%, and 6.9% based on the Wi-Fi data, HERE,

and NPMRDS, with an average of 11.8% among the three data sources.

Table E - 4 shows the change in total delay in veh-hr/hr of the cross street movements. The cross

street analysis based on Wi-Fi data and HERE data conducted in Phase II, showed improvements

in cross street total delays. No analysis based on NPMRDS data was conducted for the cross

streets since this data is not available for the cross streets. The results for SW 137th Avenue are

provided based on only HERE data, because it’s found that the quality of the Wi-Fi data for SW

137th Avenue was too poor to provide reasonable results. Phase I analysis based on Wi-Fi data

showed that the total delay benefits for the 12-hour period for NW 87th Avenue and NW 97th

Avenue were 43.4% and 4.0%, respectively. Phase II analysis based on Wi-Fi data showed that

the benefits for the 12-hour period for NW 87th Avenue and NW 97th Avenue were 20.6% and

9.8%, respectively. Phase II analysis based on HERE data showed that the benefits for the 12 hour

period for NW 87th Avenue, NW 97th Avenue, and NW 137th Avenue were 23.5%, 15.4%, and

17.2%; respectively.

Page 16

Table E - 4: Total Delay of the Cross Street Traffic

Street Data Peak Direction Delay veh-hr/hr - Phase I Delay veh-hr/hr - Phase II

Before After Difference % Difference Before After Difference % Difference

87th Av.

Wi-Fi

AM

SB 20 14 6 31.3 29 32 -3 -9.9

NB 70 29 41 59.0 60 50 10 17.0

Both 90 43 48 52.7 89 81 7 8.3

MD

SB 33 22 11 33.3 39 35 4 10.9

NB 39 30 9 23.8 45 35 10 22.6

Both 72 52 20 28.2 84 70 14 17.2

PM

SB 86 30 56 65.0 82 56 26 31.6

NB 48 30 18 37.0 50 37 13 25.9

Both 133 60 73 55.0 131 93 39 29.4

All 1115.8 632.1 484 43.4 1183.8 939.7 244 20.6

HERE

AM

SB 62 53 8.2 13.3

NB 80 65 14.8 18.6

Both 141 118 23.0 16.3

MD

SB 68 58 10.1 14.9

NB 63 52 10.7 17.2

Both 130 109 20.9 16.0

PM

SB 133 73 60.3 45.2

NB 61 52 9.2 15.1

Both 195 125 69.5 35.7

All 1810.3 1385.6 424.8 23.5

97th Av.

Wi-Fi

AM

SB 9 9 0 0.0 14 15 -1 -9.9

NB 20 18 2 9.5 42 40 2 4.6

Both 29 27 2 6.5 56 55 1 1.0

MD

SB 15 16 -1 -5.9 14 15 -1 -9.4

NB 17 16 2 9.5 20 18 2 10.0

Both 32 31 1 2.5 34 33 1 1.9

PM

SB 32 32 0 0.0 41 33 8 19.2

NB 19 17 3 13.0 27 21 7 24.1

Both 51 48 3 5.0 68 54 14 21.1

All 444.8 427.1 18 4.0 568.1 512.4 56 9.8

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Street Data Peak Direction Delay veh-hr/hr - Phase I Delay veh-hr/hr - Phase II


97th Av. HERE

AM

SB 32 26 5.6 17.6

NB 62 53 9.6 15.3

Both 94 79 15.2 16.1

MD

SB 27 25 2.6 9.6

NB 28 27 1.4 5.0

Both 55 51 4.0 7.3

PM

SB 65 46 18.9 29.1

NB 33 28 4.1 12.6

Both 98 75 23.0 23.6

All 889.8 752.7 137.2 15.4

137th Av. HERE

AM

SB 11 10 1.2 11.0

NB 146 100 45.7 31.3

Both 157 110 46.9 29.8

MD

SB 17 14 3.1 17.9

NB 22 17 5.5 24.4

Both 39 31 8.5 21.6

PM

SB 127 119 8.1 6.4

NB 17 15 2.0 11.8

Both 144 133 10.1 7.0

All 1073.8 889.3 184.6 17.2

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E.1.4 Percentage of Time in Congestion

The percentage of time in congestion was used to determine the proportion of time that the main

street traffic operates under unacceptable levels of service based on travel time/speed

measurements. In this study, a speed of 20 mph was used as the speed threshold to calculate this

measure, since the speed range for Level of Service (LOS) D is between 17 mph and 22 mph for

the study segments based on the Highway Capacity Manual (HCM) procedure. The results for

Phase I based on Wi-Fi data show that the percentage of time in congestion decreased for all

segments in both directions during all time periods, except for the EB during the AM peak at

Segment II (a very short segment) and the WB during the midday period of Segment III. Phase II

analysis results based on Wi-Fi data show that the percentage of time in congestion for the EB

during the PM peak decreased significantly. There was also a slight reduction in the percentage

in congestion for the WB during the AM peak and mid-day. The EB during the AM peak and WB

during PM peak had a slightly increased percentage time in congestion. However, the results based

on HERE data and NPMRDS data show that all directions and time periods had a reduction in the

percentage in congestion. In particular, the analysis based on HERE data shows a very high

reduction in the congestion percentage. As examples, the HERE data shows that the percentage

of time in congestion for the WB during the PM peak decreased from 78.6% to 50.1% and the

percentage for the EB during the PM peak decreased from 24.4% to 4.73%.

E.2 Throughput

The throughput is reflected by the average hourly volume on all through-lanes of a roadway

segment in one direction of travel for each time period. Seven-day volume tube counts were

collected in the Phase I evaluation (August of 2015 and 2017). The analysis of this data showed

that the main differences between the before and after conditions are the increase in volumes in

the eastbound in the AM peak (by 5.8%) and the westbound in the PM peak (by 5.2%). These are

the heavy directions in the two peak periods. The research team did not collect data from the field

for Phase II analysis. However, the analysis of the change in throughput was still possible by using

permanent count station data from the Florida Traffic Online database and temporary (portable)

count station data obtained from FDOT District 6. The permanent and portable counts are

collected and maintained by the FDOT Forecasting and Trends Offices. The analysis of this data

shows that all hourly traffic counts for the after period were higher than those for the before period

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except for few location/time period combinations. When averaged over the main street count

station locations, the increase in volume in the eastbound direction was estimated at 5.78%, 6.66%,

and 3.76% for the AM peak, PM peak, and Mid-Day periods; respectively. For the westbound

directions, the corresponding estimates were 5.95%, 2.63%, and 7.47%. The median of the six

values listed above is 5.86%. Combining this result with the result from Phase I analysis, it can

be concluded that the system throughput increased by an average of about 5% to 6% in the after

period.

E.3 Reliability

The reliability measures utilized in this study are the Travel Time Indices (TTIs) and the plots of

the travel time rate cumulative distribution function (CDF). Different percentiles of the TTIs,

including the 95th percentile, 80th percentile, and 50th percentile (median) were calculated for both

the before and after study periods. The reliability analysis should include the same months in the

before and after periods and should be compared for at least a six month period, and preferably for

a whole year. While seven months of Wi-Fi data was used for Phase II analysis, only one month

of Wi-Fi data was used for Phase I analysis. Thus, the results from the reliability analysis based

on Phase I should be viewed with caution.

The CDF of the travel time rate were plotted and assessed to allow the evaluation of reliability.

The travel time rate is defined as the average number of seconds a vehicle spends to travel one

mile along the corridor. Thus, it is the inverse of speed. The CDF chart displays the cumulative

probability of a certain travel time rate value (the probability of having travel time rate less than

or equal to certain value). As a general note, the CDF indicates a worse reliability if it is to the

right and to the bottom of a plot compared to another CDF.

The travel time rate CDF results for EB of the whole study corridor according to Phase I and Phase

II analysis are shown in Figure E - 9 and Figure E - 10, respectively. If the 80th percentile TTR is

taken as a reference, then the Phase I results shown in Figure E-8 indicate deterioration of 5.1%

(297 vs. 312 seconds per mile) in the AM peak, 1.9% deterioration in the midday (207 vs. 211

seconds per mile), but 5.3% improvement in the PM (226 vs. 214 seconds per mile). According

to Phase II results, shown in Figure E - 10, these values are 4.5% deterioration (231 vs. 241 seconds

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per mile), 0% (164 seconds per mile in both cases), and 4.5% improvement (190 vs. 181 seconds

per mile), respectively. Figure E - 11 and Figure E - 12 show the CDF plots for the WB direction

based on Phase I and Phase II analyses, respectively. The changes in the 80th percentile TTR

according to Phase I shown in Figure E - 11 for the AM, midday, and PM peak are 22.1%

improvement (234 vs. 182 seconds per mile), 1.9% improvement (182 vs. 179 seconds per mile),

and 6.8% deterioration (297 vs. 317 seconds per mile). The Phase II analysis results, shown in

Figure E - 12 indicate the corresponding changes for AM, MD, and PM are 4.2% improvement

(159 vs. 153 seconds per mile), 7.7% improvement (156 vs. 144 seconds per mile), and 2.8%

deterioration (243 vs. 250 seconds per mile). In addition, the 80th percentile TTR results based on

HERE data and NPMRDS data for Phase II showed that the reliability improvement were between

4.8% and 20.1% (HERE data) and between 3.7% and 9.4% (NPMRDS data). Considering that

Phase II reliability analysis is more dependable. It can be concluded that the improvement in

reliability is not significant.

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Figure E - 9: Segment ALL EB Travel Time Rate CDF (Phase I)

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Figure E - 10: Segment ALL EB Travel Time Rate CDF (Phase II)

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Figure E - 11: Segment ALL WB Travel Time Rate CDF (Phase I)

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Figure E - 12: Segment ALL WB Travel Time Rate CDF (Phase II)

E.4 Safety

The safety of SW 8th Street was assessed by calculating the crash rate (number of crashes per year,

per mile) based on the Signal Four Analytics crash data. To get a longer period of time for the

after condition, the before and after conditions of Phase I were combined with those of Phase II

for safety analysis. So there was a total of 16 months (from May 2017 to August 2017 and from

June 2018 to May 2019) of crash data for the after conditions with adaptive control. The study

used a total of 3 years and 10 months (from January 2014 to March 2017 and from October 2017

to April 2018) of crash data for the before conditions (time-of-day control). It should be pointed

out that a before-after safety analysis should be based on a minimum of three years for the before

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Page 25

period and a minimum of three years for the after period. Thus, the presented results should be

viewed with caution and the analysis should be repeated when three year of crash data becomes

available for the after conditions.

This study utilized the Observational Before/After Evaluation using Safety Performance Functions

(the Empirical Bayes Method) recommended in the Highway Safety Manual (HSM). The

Empirical Bayes Method considers the stochastic nature of crashes by compensating for the

potential bias resulting from regression-to-the mean. In addition, the method accounts for the

change in traffic volumes since a change in volume is expected to result in a natural change in

crash frequency independent of any treatment. This method showed a reduction in crash frequency

on the main street by 9.73%. For the cross streets, the estimated reductions in the crash frequency

were 11.6%, 4.05%, and 9.74% for SW 87th Avenue, SW 97th Avenue, and SW 137th Avenue;

respectively

E.5 Hypothesis Testing

Various hypotheses testing was conducted to test the effectiveness of the ASCT. This type of

testing is normally used to compare parameters between two conditions such as the means,

standard deviations, proportions, and data distributions. This is necessary to prove that the

difference in the parameters between the two conditions is statistically significant. For example,

the difference in the means of the data in the before and after conditions could be due to statistical

random variation in the data. Thus, hypotheses testing is conducted to ensure that this is not the

case. The hypotheses testing was conducted in this study according to the evaluation plan. The

results from statistical hypothesis testing were found to be consistent with the results of the

percentage change in the mean of the mobility measures reported earlier in the mobility analysis

section.

E.6 Benefit-Cost Analysis

An important criterion in the selection and adoption of a technology is the return on investment of

the technology. The return on investment analysis is conducted by calculating estimates of the

net present value (NPV) or benefit-cost ratio (B-C Ratio) of the analyzed solution. This involves

Page 26

estimating of the present values of the current and future benefits and costs over the project’s

economic life. A discount rate is used to calculate the present values of the cash flows.

The travel time reduction for each direction and time periods according to Phase II analysis and

the corresponding hourly volume estimated based on all the volume counts were used to calculate

the saving of vehicle hours travelled (VHT) per day along both main street and cross streets. The

saved VHT were multiplied by the number of weekdays per year (260 days) and the dollar value

($15 per vehicle-hour) to get the annual benefit. To get the total 5-year benefits, the annual benefit

was multiplied by 4.1 which is the factor value for an interest rate of 7% and project life of 5 years

to convert benefits to present worth. For the cross streets benefit, it’s further multiplied by the

number of cross streets (8 major cross streets) to get the total benefits for all cross streets. The

sum of the 5-year benefits of both main street and cross streets were divided by the total cost of

the system including one time investment in the ASCT system including hardware and software

and construction costs and recurrent operation and maintenance costs to get the B-C Ratio. Table

E - 5 and Table E - 6 show the procedure of calculating the B-C Ratio based on Wi-Fi data and

HERE data, respectively. There were no NPMRDS data for the cross streets, so this data was not

used for the analysis. The results show that the B-C Ratios calculated based on Wi-Fi data and

HERE data are 3.7 and 8.4, respectively. It can be concluded that the ASCT system is cost-

effective.

Page 27

Table E - 5: B-C Ratio Calculation based on Wi-Fi data

Main Street Cross Streets

EB WB NB SB

AM MD PM AM MD PM AM MD PM AM

MD PM

Travel Time Reduction (Minutes)

-0.8* 0.4 1.2 0.9 1.5 0.1 0.4 0.4 0.8 -0.2 0.04 1.1

Hourly Volume 1812 1632 1566 1109 1459 1988 918 932 879 514 879 1203

Hours 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5

VHT Saved (Vehicle-hours)

-48.3 70.7 109.6 33.3 237.1 11.6 12.2 40.4 41 -3.4 3.8 77.2

VHT Saved per Day (Vehicle-hours)

414.0 171.2

5-Year Benefits 414.0 * 260 (weekdays) * 15 (dollar value)

* 4.1 (factor value) = $6,619,446.93

171.2 * 260 (weekdays) * 15 (dollar value) * 4.1 (factor value) * 8 (number of

cross streets) = $21,902,654.28

ASCT System Cost $1,236,111.59

Construction Cost $3,877,962.73

Operation Cost per Year

$358,279.6

Maintenance Cost per Year

$300,000

5-Year Cost $1,236,111.59 + $3,877,962.73 + 4.1*($358,279.6 + $300,000)

= $7,813,020.68

B-C Ratio ($6,619,447.93 + $21,902,654.28) / $7,813,020.68

= 3.7

* Negative value of travel time reduction means that the travel time was increased.

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Table E - 6: B-C Ratio Calculation based on HERE Data


EB WB NB SB

AM MD PM AM MD PM AM MD PM AM MD PM


1.9 2.1 2.8 2.3 3.1 5.9 1.2 0.4 0.4 0.4 0.4 1.7

Hourly Volume 1812 1632 1566 1109 1459 1988 918 932 879 514 879 1203

Hours 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5


114.8 371.3 255.8 85.0 490 684.2 36.7 40.4 20.5 6.9 38.1 119.3


2,001.0 261.9

5-Year Benefits 2,001.0 * 260 (weekdays) * 15 (dollar value)

* 4.1 (factor value) = $31,996,429.73

261.9 * 260 (weekdays) * 15 (dollar value) * 4.1 (factor value) * 8 (number of cross

streets) = $33,496,811.40

ASCT System Cost

$1,236,111.59

Construction Cost

$3,877,962.73


$358,279.6


$300,000

5-Year Cost $1,236,111.59 + $3,877,962.73 + 4.1*($358,279.6 + $300,000)

= $7,813,020.68

B-C Ratio ($31,996,429.73 + $33,496,811.40) / $7,813,020.68

= 8.4

Page 29

Table of Contents

1. INTRODUCTION ................................................................................................................ 36

2. DATA COLLECTION AND PROCESSING ...................................................................... 40

2.1 Wi-Fi Data ........................................................................................................................ 40

2.2 Third-Party Travel Time Data .......................................................................................... 43

2.3 Traffic Counts ................................................................................................................... 43

2.4 Crash Data ......................................................................................................................... 46

3. PERFORMANCE MEASUREMENT ESTIMATION RESULTS ...................................... 47

3.1 Roadway Segmentation .................................................................................................... 47

3.2 Mobility............................................................................................................................. 51

3.2.1 Travel Time and Speed ................................................................................................ 52

3.2.2 Delay ............................................................................................................................ 63

3.2.3 Percentage of Time in Congestion ............................................................................... 68

3.2.4 Level of Service ........................................................................................................... 75

3.2.5 Average Hourly Volume .............................................................................................. 78

3.3 Reliability .......................................................................................................................... 82

3.3.1 TTI ............................................................................................................................... 82

3.3.2 Travel Time Rate CDF................................................................................................. 87

3.4 Safety .............................................................................................................................. 109

4. HYPOTHESIS TESTING RESULTS ................................................................................ 120

4.1 Main Street Mobility Hypothesis Testing ....................................................................... 120

4.5 Cross streets Delay Hypothesis Testing .......................................................................... 126

5. BENIFIT-COST ANALYSIS ............................................................................................. 129

6. CONCLUSION ................................................................................................................... 132

7. REFERENCE ...................................................................................................................... 135

APPENDIX A. TRAVEL TIME ............................................................................................. 136

APPENDIX B. DETAILED SPEED RESULTS BASED ON Wi-Fi DATA ........................ 142

APPENDIX C. DELAY RESULTS ....................................................................................... 151

APPENDIX D. DETAILED CONGESTION TIME PERCENTAGE (Wi-Fi DATA) .......... 164

APPENDIX E. DETAILED LEVEL OF SERVICE RESULTS (Wi-Fi DATA) .................. 173

APPENDIX F. DETAILED AVERAGE HOURLY VOLUME RESULTS ......................... 189

APPENDIX G. DETAILED TRAVEL TIME INDEX RESULTS (Wi-Fi DATA) ............... 193

APPENDIX H. DETAILED CRASH RATE RESULTS BY CRASH TYPES ..................... 202

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List of Figures

Figure 1: Study Area and Signalized Intersections with ASCT ................................................... 36 Figure 2: Wi-Fi Reader Locations ................................................................................................ 41 Figure 3: Sample Snapshots of the Information Reported in the Acyclica Website .................... 42 Figure 4: Example of Tube Count Data ........................................................................................ 45 Figure 5: Example of Crash Data.................................................................................................. 46 Figure 6: Main Street Segment I between SW 67th Avenue and SR 826 NB On-ramp .............. 47 Figure 7: Main Street Segment II between SR 826 NB On-ramp and SR 826 SB On-ramp ....... 48 Figure 8: Main Street Segment III between SW 82nd Avenue and Turnpike NB On-ramp ........ 48 Figure 9: Main Street Segment IV between Turnpike NB On-ramp and SW 142th Avenue ....... 48 Figure 10: Main Street Segment ALL between SW 67th Avenue and SW 142th Avenue .......... 49 Figure 11: SW 87th Ave between W Flagler St. and SW 16th St. ................................................ 50 Figure 12: SW 97th Ave between W Flagler St. and SW 16th St. ................................................ 50 Figure 13: SW 107th Ave between W Flagler St. and SW 11th St. .............................................. 50 Figure 14: SW137th Ave between NW 6th St. and SW 26th St. ................................................... 50 Figure 15: Main Street Speed based on Wi-Fi Data (Phase I) ...................................................... 54 Figure 16: Main Street Speed based on Wi-Fi Data (Phase II)..................................................... 56 Figure 17: Main Street Speed based on HERE Data (Phase II) .................................................... 58 Figure 18: Main Street Speed based on NPMRDS Data (Phase II) .............................................. 59 Figure 19: Cross Streets Speed based on Wi-Fi Data (Phase I) .................................................... 61 Figure 20: Cross Streets Speed based on Wi-Fi Data (Phase II) .................................................. 61 Figure 21: Cross Streets Speed based on HERE Data (Phase II) ................................................. 62 Figure 22: Main Street Congestion Percentage based on Wi-Fi Data (Phase I) ........................... 70 Figure 23: Main Street Congestion Percentage based on Wi-Fi Data (Phase II).......................... 72 Figure 24: Main Street Congestion Percentage based on HERE Data (Phase II) ......................... 73 Figure 25: Main Street Congestion Percentage based on NPMRDS Data (Phase II) ................... 74 Figure 26: Main Street Average Hourly Volume (Phase I) .......................................................... 79 Figure 27: Main Street TTIs (Phase I) .......................................................................................... 84 Figure 28: Main Street TTIs (Phase II) ......................................................................................... 85 Figure 29: Cross Streets TTIs (Phase I) ........................................................................................ 86 Figure 30: Cross Streets TTIs (Phase II)....................................................................................... 86 Figure 31: Segment I EB Travel Time Rate CDF (Phase I) ......................................................... 88 Figure 32: Segment I EB Travel Time Rate CDF (Phase II) ........................................................ 89 Figure 33: Segment I WB Travel Time Rate CDF (Phase I) ........................................................ 90 Figure 34: Segment I WB Travel Time Rate CDF (Phase II) ...................................................... 91 Figure 35: Segment II EB Travel Time Rate CDF (Phase I) ........................................................ 92 Figure 36: Segment II EB Travel Time Rate CDF (Phase II) ...................................................... 93 Figure 37: Segment II WB Travel Time Rate CDF (Phase I)....................................................... 94 Figure 38: Segment II WB Travel Time Rate CDF (Phase II) ..................................................... 95 Figure 39 Segment III EB Travel Time Rate CDF (Phase I) ........................................................ 96 Figure 40: Segment III EB Travel Time Rate CDF (Phase II) ..................................................... 97 Figure 41: Segment III WB Travel Time Rate CDF (Phase I) ..................................................... 98 Figure 42: Segment III WB Travel Time Rate CDF (Phase II) .................................................... 99 Figure 43: Segment IV EB Travel Time Rate CDF (Phase I) .................................................... 100 Figure 44: Segment IV EB Travel Time Rate CDF (Phase II) ................................................... 101

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Figure 45: Segment IV WB Travel Time Rate CDF (Phase I) ................................................... 102 Figure 46: Segment IV WB Travel Time Rate CDF (Phase II) .................................................. 103 Figure 47: Segment ALL EB Travel Time Rate CDF (Phase I) ................................................. 105 Figure 48: Segment ALL EB Travel Time Rate CDF (Phase II) ............................................... 106 Figure 49: Segment ALL WB Travel Time Rate CDF (Phase I) ............................................... 107 Figure 50: Segment ALL WB Travel Time Rate CDF (Phase II) .............................................. 108 Figure 51: Main Street Total Crash Rate .................................................................................... 111 Figure 52: Main Street Hourly Crash Rate ................................................................................. 112 Figure 53: Cross Streets Total Crash Rate .................................................................................. 113 Figure 54: Cross Streets Hourly Crash Rate ............................................................................... 114 Figure 55: Main Street Travel Time Based on Wi-Fi Data (Phase I) ......................................... 136 Figure 56: Main Street Travel Time Based on Wi-Fi Data (Phase II) ........................................ 137 Figure 57: Main Street Travel Time Based on HERE Data (Phase II) ....................................... 138 Figure 58: Main Street Travel Time Based on NPMRDS Data (Phase II) ................................. 139 Figure 59: Cross Streets Travel Time based on Wi-Fi Data (Phase I)........................................ 140 Figure 60: Cross Streets Travel Time based on Wi-Fi Data (Phase II) ...................................... 140 Figure 61: Cross Streets Travel Time based on HERE Data (Phase II) ..................................... 141 Figure 62: Speed between SW 67 Ave and SW 72 Ave ............................................................. 142 Figure 63: Speed between SW 72 Ave and SR 826 NB Ramp .................................................. 142 Figure 64: Speed between SR 826 NB Ramp and SR 826 SB Ramp ......................................... 143 Figure 65: Speed between SR 826 SB Ramp and SW 82 Ave ................................................... 143 Figure 66: Speed between SW 82 Ave and SW 87 Ave ............................................................. 144 Figure 67: Speed between SW 87 Ave and SW 92 Ave ............................................................. 144 Figure 68: Speed between SW 92 Ave and SW 97 Ave ............................................................. 145 Figure 69: Speed between SW 97 Ave and SW 102 Ave ........................................................... 145 Figure 70: Speed between SW 102 Ave and SW 107 Ave ......................................................... 146 Figure 71: Speed between SW 107 Ave and SW 109 Ave ......................................................... 146 Figure 72: Speed between SW 109 Ave and SW Turnpike NB Ramp ....................................... 147 Figure 73: Speed between Turnpike NB Ramp and Turnpike SB Ramp ................................... 147 Figure 74: Speed between Turnpike SB Ramp and SW 122 Ave .............................................. 148 Figure 75: Speed between SW 122 Ave and SW 127 Ave ......................................................... 148 Figure 76: Speed between SW 127 Ave and SW 132 Ave ......................................................... 149 Figure 77: Speed between SW 132 Ave and SW 137 Ave ......................................................... 149 Figure 78: Speed between SW 137 Ave and SW 142 Ave ......................................................... 150 Figure 79: Main Street Delay per Vehicle based on Wi-Fi Data (Phase I) ................................. 151 Figure 80: Main Street Hourly Delay based on Wi-Fi Data (Phase I) ........................................ 152 Figure 81: Main Street Delay per Vehicle based on Wi-Fi Data (Phase II) ............................... 153 Figure 82: Main Street Delay per Vehicle based on HERE Data (Phase II) .............................. 154 Figure 83: Main Street Delay per Vehicle based on NPMRDS Data (Phase II) ........................ 155 Figure 84: Mainline Hourly Delay based on Wi-Fi Data (Phase II) ........................................... 156 Figure 85: Mainline Hourly Delay based on HERE Data (Phase II) .......................................... 157 Figure 86: Mainline Hourly Delay based on NPMRDS Data (Phase II) .................................... 158 Figure 87: Cross Streets Delay per Vehicle based on Wi-Fi Data (Phase I) .............................. 159 Figure 88: Cross Streets Hourly Delay based on Wi-Fi Data (Phase I)...................................... 159 Figure 89: Cross Streets Delay per Vehicle based on Wi-Fi Data (Phase II) ............................. 160 Figure 90: Cross Streets Delay per Vehicle based on HERE Data (Phase II) ............................ 161 Figure 91: Cross Streets Hourly Delay based on Wi-Fi Data (Phase II) .................................... 162

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Figure 92: Cross Streets Hourly Delay based on HERE Data (Phase II) ................................... 163 Figure 93: Congestion Percentage between SW 67 Ave and SW 72 Ave .................................. 164 Figure 94: Congestion Percentage between SW 72 Ave and SR 826 NB Ramp ....................... 164 Figure 95: Congestion Percentage between SR 826 NB Ramp and SR 826 SB Ramp .............. 165 Figure 96: Congestion Percentage between SR 826 SB Ramp and SW 82 Ave ........................ 165 Figure 97: Congestion Percentage between SW 82 Ave and SW 87 Ave .................................. 166 Figure 98: Congestion Percentage between SW 87 Ave and SW 92 Ave .................................. 166 Figure 99: Congestion Percentage between SW 92 Ave and SW 97 Ave .................................. 167 Figure 100: Congestion Percentage between SW 97 Ave and SW 102 Ave .............................. 167 Figure 101: Congestion Percentage between SW 102 Ave and SW 107 Ave ............................ 168 Figure 102: Congestion Percentage between SW 107 Ave and SW 109 Ave ............................ 168 Figure 103: Congestion Percentage between SW 109 Ave and SW Turnpike NB Ramp .......... 169 Figure 104: Congestion Percentage between Turnpike NB Ramp and Turnpike SB Ramp ...... 169 Figure 105: Congestion Percentage between Turnpike SB Ramp and SW 122 Ave ................. 170 Figure 106: Congestion Percentage between SW 122 Ave and SW 127 Ave ............................ 170 Figure 107: Congestion Percentage between SW 127 Ave and SW 132 Ave ............................ 171 Figure 108: Congestion Percentage between SW 132 Ave and SW 137 Ave ............................ 171 Figure 109: Congestion Percentage between SW 137 Ave and SW 142 Ave ............................ 172 Figure 110: Average Hourly Volume between SW 67 Ave and SW 72 Ave ............................. 189 Figure 111: Average Hourly Volume between SR 826 SB and SW 87 Ave .............................. 189 Figure 112: Average Hourly Volume between SW 87 Ave and SW 97 Ave ............................. 190 Figure 113: Average Hourly Volume between SW 97 Ave and SW 107 Ave ........................... 190 Figure 114: Average Hourly Volume between SW 107 Ave and SW 117 Ave ......................... 191 Figure 115: Average Hourly Volume between Turnpike SB and SW 127 Ave ......................... 191 Figure 116: Average Hourly Volume between SW 127 Ave and SW 137 Ave ......................... 192 Figure 117: TTIs between SW 67 Ave and SW 72 Ave ............................................................. 193 Figure 118: TTIs between SW 72 Ave and SR 826 NB Ramp .................................................. 193 Figure 119: TTIs between SR 826 NB Ramp and SR 826 SB Ramp ......................................... 194 Figure 120: TTIs between SR 826 SB Ramp and SW 82 Ave ................................................... 194 Figure 121: TTIs between SW 82 Ave and SW 87 Ave ............................................................. 195 Figure 122: TTIs between SW 87 Ave and SW 92 Ave ............................................................. 195 Figure 123: TTIs between SW 92 Ave and SW 97 Ave ............................................................. 196 Figure 124: TTIs between SW 97 Ave and SW 102 Ave ........................................................... 196 Figure 125: TTIs between SW 102 Ave and SW 107 Ave ......................................................... 197 Figure 126: TTIs between SW 107 Ave and SW 109 Ave ......................................................... 197 Figure 127: TTIs between SW 109 Ave and SW Turnpike NB Ramp ....................................... 198 Figure 128: TTIs between Turnpike NB Ramp and Turnpike SB Ramp ................................... 198 Figure 129: TTIs between Turnpike SB Ramp and SW 122 Ave .............................................. 199 Figure 130: TTIs between SW 122 Ave and SW 127 Ave ......................................................... 199 Figure 131: TTIs between SW 127 Ave and SW 132 Ave ......................................................... 200 Figure 132: TTIs between SW 132 Ave and SW 137 Ave ......................................................... 200 Figure 133: TTIs between SW 137 Ave and SW 142 Ave ......................................................... 201 Figure 134: Segment I Crash Rate by Types (After) .................................................................. 202 Figure 135: Segment I Crash Rate by Types (Before) ................................................................ 203 Figure 136: Segment II Crash Rate by Types (After) ................................................................. 204 Figure 137: Segment II Crash Rate by Types (Before) .............................................................. 205 Figure 138: Segment III Crash Rate by Types (After) ............................................................... 206

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Figure 139: Segment III Crash Rate by Types (Before) ............................................................. 207 Figure 140: Segment IV Crash Rate by Types (After) ............................................................... 208 Figure 141: Segment IV Crash Rate by Types (Before) ............................................................. 209 Figure 142: Segment ALL Crash Rate by Types (After) ............................................................ 210 Figure 143: Segment ALL Crash Rate by Types (Before) ......................................................... 211 Figure 144: SW 87th Ave Crash Rate by Types (After) ............................................................. 212 Figure 145: SW 87th Ave Crash Rate by Types (Before) .......................................................... 213 Figure 146: SW 97th Ave Crash Rate by Types (After) ............................................................. 214 Figure 147: SW 97th Ave Crash Rate by Types (Before) .......................................................... 215 Figure 148: SW 107th Ave Crash Rate by Types (After) ........................................................... 216 Figure 149: SW 107th Ave Crash Rate by Types (Before) ........................................................ 217 Figure 150: SW 137th Ave Crash Rate by Types (After) ........................................................... 218 Figure 151: SW 137th Ave Crash Rate by Types (Before) ........................................................ 219

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List of Tables Table 1: List of Signalized Intersections Installed with ASCT .................................................... 36 Table 2: List of Intersections Installed with Wi-Fi Data Collection Devices ............................... 41 Table 3: Portable Traffic Count Stations Details .......................................................................... 45 Table 4: Boundaries and Distances of Main Street Segments ...................................................... 49 Table 5: Boundaries and Distances of Cross Streets Segments .................................................... 51 Table 6: Date Ranges of Before and After Conditions ................................................................. 52 Table 7: Total Delay of Main Street Traffic based on Different Data Sources ............................ 64 Table 8: Total Delay of the Cross Street Traffic ........................................................................... 65 Table 9: Results of Main Street Worst LOS Time based on Wi-Fi Data (Phase I) ..................... 76 Table 10: Results of Main Street Worst LOS Time based on Wi-Fi Data (Phase II) .................. 76 Table 11: Results of Cross Streets Worst LOS Time based on Wi-Fi Data (Phase I) .................. 77 Table 12: Results of Cross Streets Worst LOS Time based on Wi-Fi Data (Phase II)................. 77 Table 12: Portable Stations Hourly Traffic Counts ...................................................................... 81 Table 13: Historical AADT in Vehicle per Day for the Study Corridor .................................... 115 Table 14: Historical AADT in Vehicle per Day for the Study Corridor Cross Street ................ 115 Table 15: Florida-specific SPFs Regression Coefficients .......................................................... 116 Table 16: Results of the EB Method on SW 8th Street .............................................................. 118 Table 17: Results of the EB Method on SW 87th AVE ............................................................. 118 Table 18: Results of the EB Method on SW 97th AVE ............................................................. 119 Table 19: Results of the EB Method on SW 137th AVE ........................................................... 119 Table 20: Main Street Segment ALL Hypothesis Testing Results (Wi-Fi Data) ....................... 123 Table 21: Main Street Segment ALL Hypothesis Testing Results (HERE Data, Phase II) ....... 124 Table 22: Main Street Segment ALL Hypothesis Testing Results (NPMRDS Data, Phase II) . 125 Table 23: Cross streets Delay Hypothesis Results based on Wi-Fi Data ................................... 127 Table 24: Cross Streets Delay Hypothesis Results based on HERE Data (Phase II) ................. 128 Table 25: B-C Ratio Calculation based on Wi-Fi data ............................................................... 130 Table 26: B-C Ratio Calculation based on HERE Data ............................................................. 131 Table 27: LOS by 15-Minute during Peak Periods for Main Street Movements (Phase I) ........ 173 Table 28: LOS by 15-Minute during Peak Periods for Main Street Movements (Phase II) ....... 178 Table 29: LOS by 15-Minute Interval during Peak Periods for Cross streets (Phase I) ............. 184 Table 30: LOS by 15-Minute Interval during Peak Periods for Cross streets (Phase II)............ 187

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List of Acronyms

ASCT Adaptive Signal Control Technology

B-C Ratio Benefit Cost Ratio

CATT Lab Center for Advanced Transportation Technology Laboratory

CDF Cumulative Distribution Function

EB Eastbound

FDOT Florida Department of Transportation

FHWA Federal Highway Administration

FIU Florida International University

HCM Highway Capacity Manual

ITSDCAP ITS Data Capture and Performance Management

LOS Level of Service

MDC DTPW Miami-Dade County Department of Transportation & Public Works

MPH Miles per Hour

NHS National Highway System

NPMRDS National Performance Management Research Data Set

NPV Net Present Value

RITIS Regional Integrated Transportation Information System

TTI Travel Time Index

TTR Travel Time Rate

USDOT United States Department of Transportation

VHT Vehicle Hours Travelled

WB Westbound

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1. INTRODUCTION

An adaptive signal control technology (ASCT) system has the potential to improve transportation

system mobility, reliability, safety, and environmental impacts by accommodating changing traffic

demands at signalized intersections. The Florida Department of Transportation (FDOT) District

6 has deployed the InSync adaptive traffic control system between SW 67th Avenue and SW 142nd

Avenue along SW 8th Street in Miami-Dade County, as shown in Figure 1 and Table 1.

Figure 1: Study Area and Signalized Intersections with ASCT

Table 1: List of Signalized Intersections Installed with ASCT

INTERSECTION NUMBER LOCATION MDC ASSET ID

1 SW 8th St & SW 142nd Ave 6863 2 SW 8th St & SW 137th Ave 4869 3 SW 8th St & SW 132nd Ave 4758 4 SW 6th St & SW 132nd Ave 5988 5 SW 8th St & SW 129th Pl 5984 6 SW 6th St & SW 127th Ave 5406 7 SW 8th St & SW 127th Ave 5130 8 SW 6th St & SW 122nd Ave 5145 9 SW 8th St & SW 122nd Ave 3730 10 SW 8th St & SB HEFT SB 4239 11 SW 8th St & HEFT NB 4238 12 SW 8th St & 117th Ave 4974 13 SW 8th St & SW 112th Ave (FIU) 3879 14 SW 8th St & SW 109th Ave 5430 15 SW 8th St & SW 107th Ave 3709

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INTERSECTION NUMBER LOCATION MDC ASSET ID

16 SW 8th St & SW 102nd Ave 4510 17 SW 8th St & SW 97th Ave 3743 18 SW 8th St & SW 94th Ave 4563 19 SW 8th St & SW 92nd Ave 5164 20 SW 8th ST & SW 89th Ave 6989 21 SW 8th St & SW 87th Ave 3362 22 SW 8th St & SW 82nd Ave 4565 23 SW 8th St & SB On Ramp Palmetto Expy SR826 5425 24 SW 8th St & NB Off Ramp Palmetto Expy SR826 5424 25 SW 8th St & 75th Ave_74th_Ct_Ped Xing 2135 26 SW 8th St & SW 74th Ave 2634 27 SW 8th St & SW 72nd Ave 5741 28 SW 8th St & SW 70th Ave 6944 29 SW 8th St & SW 67th Ave 2633 30 SW 10th St & SW 67th Ave 5999

The installed ASCT is the InSync Fusion adaptive traffic control system developed by Rhythm

Engineering. It is an intelligent transportation system that enables traffic signals to adapt to actual

traffic demand. The system is based on two main concepts,

Local Optimization: uses traffic sensors (video image detection (VID) or the existing loop

detection) to estimate the demands, the waiting times, and queues at an intersection.

InSync utilizes this data to assign dynamic phasing and dynamic green splits to approaches

to use green time efficiently at the local level.

Global Optimization: InSync can provide progression along an entire corridor using what

is referred to as the “green tunnels.” As such, platoons of vehicles gather and are then

released through the corridor. By communicating with each other, the signals anticipate

the green tunnel’s arrival to allow the vehicles to pass through without slowing down or

stopping. The green tunnels’ duration and frequency can vary to best support traffic

conditions. Between green tunnels, the local optimization works to best serve the side

streets and left turns. After a specified percentage of the tunnel duration has passed, In|Sync

can end the tunnel early if there is a large enough gap between cars. This truncation

percentage and size of the gap are specified by the engineer.

An important concept in InSync is the period length. This length is set to serve all coordinated

tunnels and serve all occupied phases at least once per period. At the site level, the InSync

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installation involved installing the InSync processor, an industrial-grade micro controller that

integrates with the traffic cabinets and controllers via industry standard connections. It also

involved installing VID cameras for traffic detection.

Please note that although the evaluation was done to determine the impact of the installed

technology, FDOT District 6 has been effectively involved in the day-to-day management and

operations of the facility. Thus, the benefits presented in this report reflected these activities also.

The evaluation of the system was done for the before and after period, and this report clearly shows

the changes of performances between the two periods. It is expected that the changes in

performance are due to the deployed system.

The goal of this project was agreed on in a stakeholder workshop conducted as part of an effort to

develop an evaluation plan for the project, and reads as follows: “The project goal is to improve

the efficiency of SW 8th Street, between SW 142nd Avenue and SW 67th Avenue, using

sustainable signal technology to minimize congestion and increase throughput where possible,

without compromising safety for all users.”

To test if the above goal and the associated objectives are met with the deployment of an adaptive

signal control system, an evaluation plan was developed by this research team based on significant

stakeholder involvement from project stakeholders and in accordance with the United States

Department of Transportation’s (USDOT’s) ITS Evaluation Guidelines, the Federal Highway

Administration’s (FHWA’s) “Measures of Effectiveness and Validation Guidance for Adaptive

Signal Control Technologies” document, and other related guidelines and reports on the subject.

Various performance measures were proposed in the evaluation plan to assess the effectiveness of

this adaptive signal control system, including measures related to mobility, reliability, safety,

environmental impacts, and agency operations. The evaluation plan is available in a different

document produced as part of this effort.

This document presents the evaluation of the system according to a previously developed

evaluation plan, developed by the evaluation team, as part of the project. The remainder of this

document is organized as follows. Section 2 details the data collection and processing procedures

utilized in the evaluation. Section 3 presents the results of the comparison of the performance

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measures between the after and the before conditions. Section 4 presents the results of the

statistical hypothesis testing to further compare the after to the before conditions. The benefit-cost

analysis results are presented in Section 5. The conclusion is provided in Section 6.

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2. DATA COLLECTION AND PROCESSING

To compare the performance measures between the after and before conditions, data from multiple

sources were collected and preprocessed in this project. This section presents a review of the

collected data that was used in the evaluation.

2.1 Wi-Fi Data

Compared to floating car studies, automatic vehicle identification technologies such as Bluetooth,

Wi-Fi and others can provide continuous measurements of travel time for a given roadway

segment. In this project, the Acyclica Wi-Fi readers were selected by FDOT District 6 and their

consultants to be deployed along SW 8th Street in Miami, Florida, between SW 67th Avenue and

SW 142nd Avenue (the study segment), and also at cross street approaches to the study segment.

Figure 2 and Table 2 show the locations of these Wi-Fi readers. The Wi-Fi readers can report the

travel times between any pair of predefined origins and destinations at the 5-minute aggregation

level. The raw-detection data of individual vehicles, as well as a number of matching vehicles, can

also be visualized and downloaded from the Acyclica website. Figure 3 shows sample snapshots

of the information reported in the Acyclica website. The green pins in the figure represent the Wi-

Fi readers.

In this project, the Wi-Fi data is used as a major data source for evaluating the performance of the

adaptive signal control system, including main street travel times, speed, delay, and percentage

time in congestion. The Wi-Fi data was uploaded to the ITS Data Capture and Performance

Management (ITSDCAP) tool, developed for the FDOT by the research team.

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Figure 2: Wi-Fi Reader Locations

Table 2: List of Intersections Installed with Wi-Fi Data Collection Devices No. Intersection Asset ID 1 SW 142nd Ave. 6863 2 SW 137th Ave. 4869 3 SW 137th Ave. at SW 18th St. 5168 4 SW 137th Ave. at NW 6th St. 6940 5 SW 132nd Ave. 4758 6 SW 127th Ave. 5130 7 SW 127 Ave. at SW 18th St. 5042 8 SW 127th Avenue at SW 6th St. 5406 9 SW 122nd Ave. 3730 10 SB On-Ramp to TPK 4239 11 NB On-Ramp to TPK 4238 12 SW 109th Ave. 5430 13 SW 107th Ave. 3709 14 SW 107th Ave. at SW 1100 Block 4757 15 SW 107th Ave. at SW 4th Street 4560 16 SW 107th Ave. at Flagler Street 3894 17 SW 102nd Ave. 4510 18 SW 97th Ave. 3743 19 SW 97th Ave. at SW 16th St. 6865 20 SW 97th Ave. at Flagler St. 4520 21 SW 92nd Ave. 5164 22 SW 87th Ave. 3362 23 SW 87th Ave. at SW 16th St. 3231 24 SW 87th Ave. at Flagler St. 3747 25 SW 82nd Ave. 4565 26 SR- 826 SB 5425 27 SR- 826 NB 5424 28 SW 72nd Ave. 5741 29 SW 67th Ave. 2633

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(a) Travel Time

(b) Raw Detection Data of Individual Vehicles

Figure 3: Sample Snapshots of the Information Reported in the Acyclica Website

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2.2 Third-Party Travel Time Data

In addition to Acyclica Wi-Fi data, third-party travel time data including HERE data and the

National Performance Management Research Data Set (NPMRDS) data were also used to evaluate

the mobility of SW 8th Street main street segments. The NPMRDS is procured and sponsored

by the Federal Highway Administration (FHWA), and it includes archived speed and travel time

data set (including associated location referencing data) that covers the National Highway System

(NHS) and additional roadways near 26 key border crossings with Canada (20 crossings) and

Mexico (6 crossings). It includes speeds and travel times at 5-minute intervals on over 400,000

road segments for passenger vehicles, trucks, and combination of both. The University of

Maryland Center for Advanced Transportation Technology Laboratory (CATT Lab) hosts the

NPMRDS on the website of the Regional Integrated Transportation Information System (RITIS),

making the data easy to download at user-defined geographies and temporal resolutions. RITIS

website also provides the travel time data collected by HERE and provided as part of a contract

with the FDOT. HERE is a company that provides mapping and location data and related services.

The travel times obtained from both NPMRDS and HERE are estimated based on vehicle tracking

using automatic vehicle location (AVL technology like GPS).

The HERE and NPMRDS data sets were downloaded from RITIS website and uploaded to the

ITSDCAP tool. The results based on the three data sources were used in the evaluation of this

study.

2.3 Traffic Counts

A number of measures require traffic volume counts as inputs. Therefore, 7-day tube counts were

collected by a data collection team that has a contract with FDOT District 6 at the following main

street segments and cross streets.

Main Street segments:

Segment 1: SW 137th Avenue to SW 127th Avenue

Segment 2: SW 127th Avenue to SB TPK On/Off-Ramp

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Segment 6: SW 87th Avenue to SR 826 SB On/Off-Ramp

Segment 7: SW 72nd Avenue to SW 67th Avenue

Minor street segments:

SW 137th Avenue: Coral Way to SW 8th Street

SW 127th Avenue: SW 16 Street to SW 8th Street


SW 107th Avenue: Flagler Street to SW 8th Street



SW 87th Avenue: Flagler Street to SW 8th Street

Florida Turnpike on- and off-ramps

SR 826 on- and off-ramps

The data collection was performed in four periods in 2015 (May, August, October, and December,

respectively), one period in 2017 (August), and one period in 2018 (February) as well. The

collected volume data were used to perform the benefit-cost analysis. In addition, the volume data

was compared between August of 2015 and August of 2017, representing the before and after

conditions, respectively. The collected tube counts were processed and aggregated to 15-minute

time intervals. Figure 4 shows an example of the tube count data after processing and aggregation.

The traffic count data were uploaded to ITSDCAP tool.

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Figure 4: Example of Tube Count Data

Table 3: Portable Traffic Count Stations Details

# Station Month

Description Direction Before After

1 0005 May June SR 90/US-41/SW 8 ST 200' E SW 74 AV

2 0088 February August SR 90/US-41/SW 8 ST 200' E SW 137 AV

3 0089 February June SR-90/ SW 8 ST/TAMIAMI TRAIL

1000' W SW 137

4 0090 May June SR 90/US-41/SW 8 ST 500' E SW 109 AV

5 0092 May June SR 90/US-41/SW 8 ST E Galloway RD/SW 87 AV

6 0589 June August SR 90/US-41/SW 8 ST 200' W SW 87 AV

7 2561 September August SR90/SW8ST/Tamiami Trail 0.25M West of SW122AVE

The permanent count station data from the Florida Traffic Online database and temporary

(portable) count station data obtained from FDOT district 6 were also used for the analysis. The

permanent and portable counts are collected and maintained by the FDOT Forecasting and Trends

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Offices. The permanent station is located at the west end of the facility at SW 8th Street between

SW 142nd Avenue and SW 139th Avenue. The details of the data collection from the portable

stations are shown in Error! Reference source not found..

2.4 Crash Data

To assess the safety improvement, crash data were extracted and downloaded from the Signal Four

Analytics, an interactive web-based geospatial crash analytical system developed by and hosted at

the University of Florida Geoplan Center. Typically a minimum of three years of crash data is

used for safety analysis. In this study, the after period is less than three years. To get the longest

period of time for the after condition, the before and after periods of Phase I were combined with

those of Phase II, respectively, so there was a total of 16 months (from May 2017 to August 2017

and from June 2018 to May 2019) of crash data for the after conditions with adaptive control. A

total of 3 years and 10 months (from January 2014 to March 2017 and from October 2017 to April

2018) of crash data was used for the before conditions (time-of-day control). Figure 5 shows an

example of crash data obtained from this study. The crash data was uploaded to the ITSDCAP

tool.

Figure 5: Example of Crash Data

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3. PERFORMANCE MEASUREMENT ESTIMATION RESULTS

This chapter presents the comparison results of the assessment performance measures identified

in the evaluation plan for the conditions after and before the implementation of the adaptive signal

control system. The spatial segmentation of both the main street and cross streets is first

introduced. The results of the estimation of the performance measures are then presented in terms

of mobility, reliability, and safety.

3.1 Roadway Segmentation

To facilitate the estimation of the system performance of the corridor, the main street was divided

into four segments, which are referred to as Segments I, II, III, and IV. These segments correspond

to the coordinated sub-systems as previously set by Miami-Dade County Department of

Transportation & Public Works (MDC DTPW) in the time of day operations of the corridor. In

addition to these four segments, the entire study corridor along SW 8th Street is assessed and

referred to as Segment ALL in this document. Figure 6 to Figure 10 show the locations of the

segments as red lines and the Wi-Fi readers as green pins, and Table 4 lists the boundaries of the

segments and the average distances between the signalized intersections for each segment.

Figure 6: Main Street Segment I between SW 67th Avenue and SR 826 NB On-ramp

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Figure 7: Main Street Segment II between SR 826 NB On-ramp and SR 826 SB On-ramp

Figure 8: Main Street Segment III between SW 82nd Avenue and Turnpike NB On-ramp

Figure 9: Main Street Segment IV between Turnpike NB On-ramp and SW 142th Avenue

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Figure 10: Main Street Segment ALL between SW 67th Avenue and SW 142th Avenue

Table 4: Boundaries and Distances of Main Street Segments

Segment Boundary

Average Distance

between Signalized

Intersections (miles)

Length (miles)

I between SW 67th Avenue and SR

826 NB On-Ramp 0.17 1.0

II between SR 826 NB On-Ramp

and SR 826 SB On-Ramp 0.2 0.2

III between SW 82nd Avenue and

Turnpike NB On-Ramp 0.49 3.4

IV between Turnpike NB On-Ramp

and SW 142th Avenue 0.45 2.7

ALL between SW 67th Avenue and SW

142th Avenue 0.36 7.5

To estimate how the adaptive signal control system affects the cross streets, the performance

measures were also assessed for four cross street segments in the before-conditions report. Their

locations are shown in Figure 11 to Figure 14, and Table 5 lists their boundaries and distances.

However, due to the construction activities for SW 107th Ave in the after conditions, the

comparison results for mobility and reliability are only presented for the three cross streets (SW

87th Ave, SW 97th Ave, and SW 137th Ave). The safety analysis includes all four cross streets

including the SW 107th Ave.

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Figure 11: SW 87th Ave between W Flagler St. and SW 16th St.



Figure 14: SW137th Ave between NW 6th St. and SW 26th St.

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Table 5: Boundaries and Distances of Cross Streets Segments

Segment Boundary

Average Distance between

Signalized Intersections

(miles)

Distance

(miles)

SW 87th Ave between W Flagler St. and

SW 16th St. 0.5 1.0


SW 16th St. 0.5 1.0


SW 11th St. 0.19 0.57

SW 137th Ave between NW 6th St. and

SW 26th St. 0.67 2.0

3.2 Mobility

Mobility measures, including travel times, speeds, delays, and percentage of time in congestion,

were estimated based on data collected utilizing the Wi-Fi readers and the third-party-based travel

time data from two sources, which are HERE and the National Performance Management Research

Data Set (NPMRDS) data. The Highway Capacity Manual (HCM) procedures were used to

determine the urban street level of service (LOS) based on travel speeds. The collected tube count

data were used to calculate the hourly average volumes, which in turn was used to calculate the

total delay in veh-hr/hr.

The adaptive signal control technology (ASCT) system along SW 8th Street was first activated on

April 22, 2017, but it was suspended on Sep 7, 2017 due to Hurricane Irma and then reactivated

on May 16, 2018, so there are two before-after phases to compare in this study. The phase before

Hurricane Irma is named “Phase I”, and the before and after periods in this phase are named

“Before-I” and “After-I”, respectively. The phase after the hurricane is named “Phase II”, and the

before and after periods in this phase are named “Before-II” and “After-II”, respectively. The date

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ranges of the before and after conditions selected for this study are listed in Table 6. For Phase-I,

August 2015 and August 2017 were selected so that the volume related measures can be estimated,

since 7-day tube counts were performed in these months. In this study, three time periods were

selected for the analysis: the AM peak (7:00 AM to 9:00 AM), the midday (9:00 AM to 3:30 PM),

and the PM peak (3:30 PM to 7:00 PM).

Table 6: Date Ranges of Before and After Conditions

Phase Before/After Date Range

Phase I Before-I August 2015

After-I August 2017

Phase II Before-II from October 2017 to April 2018

After-II from October 2018 to April 2019

3.2.1 Travel Time and Speed

The segment travel time is defined as the average travel time of all vehicles, while traveling on the

segment between an origin and a destination along the corridor during the study period. Since the

speed and travel time data provide the same trends, the charts showing travel time results are

presented in APPENDIX A, and only the charts of the speed results are presented in this section.

Main Street Analysis Based on Wi-Fi Data

APPENDIX A includes the average travel times of the main street segments based on Wi-Fi data

during Phase I. Figure 15 shows a comparison between the speeds in the before and after

conditions for Phase I based on travel time data collected using Wi-Fi data. The speed results

presented in these figures are consistent with the travel time results presented in APPENDIX A.

The results show that, for all four study segments, there was a reduction in the average travel times

for the eastbound (EB) direction during the PM peak period and westbound (WB) direction during

the AM peak period. These are considered as the off-peak travel directions in the two peaks,

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although they still carry relatively high volumes. Specifically, the average travel time of the WB

during the AM peak for Segment ALL decreased by about 5.8 minutes (about 30%). The

corresponding reduction in travel time in the EB direction in the PM peak was 2.5 Minutes

(10.4%).

For the EB direction during the AM peak (the peak direction during the AM peak), the average

travel times of Segment I and III decreased, but the travel times of Segment II (a short segment

that is very congested in this peak) and Segment IV increased. The overall change for Segment

ALL (the whole corridor) in the EB direction in the AM peak was an increase of 1.5 minutes (about

5% increase). For the WB direction during the PM peak, the average travel times of Segments I

and III increased slightly, but the travel times for Segments II and IV decreased. The travel time

for the ALL Segment in the WB direction increased 0.43 minutes (1.3%) in the PM peak.

As for the mid-day period, the results show that the travel time for the EB traffic on Segment I,

WB traffic on Segment II, and WB traffic on Segment IV WB decreased. However, the overall

change in travel time (for the ALL segment) appears to be small.

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Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 15: Main Street Speed based on Wi-Fi Data (Phase I)

05

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APPENDIX A also includes the average travel times of the main street segments based on Wi-Fi

data during Phase II, and the corresponding speed results are shown in Figure 16. The comparison

between the before and after conditions shows that, there was a reduction in the average travel

time for all four segments and all time periods and directions, except the EB (peak direction) during

the AM peak. The travel time for the EB during the AM peak increased by an average of 0.8

minutes (3.4%), while the travel times of the EB during the PM peak and the WB during the AM

peak decreased by 1.2 minutes (5.5%) and 1 minute (5.2%), respectively. The peak direction

during the PM, which is the WB, had a slight reduction in travel time of 0.1 minute (0.5%). As

for the mid-day period, the travel time for the EB and WB periods decreased by 0.4 and 1.5 minutes

(2.1% and 8.3%), respectively.

The results also show that the travel times of different segments changed differently. Segment I

had a reduced travel time for the EB direction in all time periods and the WB direction in mid-day.

Segment II, which is a very short segment, had increased travel time for all directions and time

periods. Segment III had reduced travel time for the WB during the AM and mid-day periods.

Segment IV had reduced travel times for almost all directions and time periods except the WB

during the PM peak. The EB during the PM and WB during AM and mid-day periods; contributing

2.4, 1.2, and 1.0 minutes of travel time reduction, respectively.

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Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 16: Main Street Speed based on Wi-Fi Data (Phase II)

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5

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20

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30

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Main Street Analysis Based on Third-Party Data

For phase II, the results of the mobility measures were also verified with third-party travel time

data. The travel time results are included in APPENDIX A. Figure 17 and Figure 18 show the

corresponding speed results based on HERE data and NPMRDS data, respectively. The results

show reductions in the travel times of all main street directions and time periods. In particular, the

analysis based on HERE data shows the highest improvements among the three utilized data

sources. The reductions in travel times for the EB direction according to HERE data for the AM,

midday, and PM time periods were 1.9 minutes (7.3%), 2.1 minutes (10.2%), and 2.8 minutes

(13.1%), respectively. For the WB direction, the corresponding values were 2.3 minutes (11.9%),

3.1 minutes (15.8%), and 5.9 minutes (20.2%), respectively. The analysis based on NPMRDS

data shows less improvements compared to the HERE data, with the degree of improvements being

closer to the results from the analysis based on Wi-Fi data, except that the analysis based on

NPMRDS data also indicated reduced travel times for the EB direction during the AM peak,

meaning that both directions of travel had reductions in travel times for all time periods according

to this analysis. The travel time reduction based on NPMRDS data are 2.1 minutes (7.0%), 0.9

minutes (4.1%), and 1.3 minutes (5.1%) for the AM, midday, and PM periods in EB direction and

1.7 minutes (7.9%), 1.4 minutes (6.6%), and 0.5 minutes (1.7%) for the three time periods in the

WB direction, respectively.

Similar to the results based on Wi-Fi data, the changes to the different segments are different. The

analysis based on HERE data show improvements for almost all four segments in both directions

during all time periods, except that Segment II, which is a very short segment, had slightly

increased travel time in the EB during the AM and midday periods. In general, the analysis based

on NPMRDS data shows slightly increased travel time for Segment I and II but obvious reduction

in travel times for Segment III and IV, except for the WB direction during the PM peak that has

the opposite results.

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Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 17: Main Street Speed based on HERE Data (Phase II)

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Segment III between SW 82nd Avenue and the Florida Turnpike northbound ramp and Segment

IV between the Florida Turnpike northbound and SW 142nd Avenue are relatively long segments.

It is useful to examine the performance of these at a finer spatial resolution for operational

Segment I

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Segment ALL

Figure 18: Main Street Speed based on NPMRDS Data (Phase II)

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assessment purposes. Although such resolution of the analysis is not used in the performance

assessment in this document, it can be used by the system engineers and operators to support their

decisions with regard to setting system parameters. APPENDIX B includes detailed speed analyses

for sub-segments of Segment III and IV based on Wi-Fi data (both phase I and II). Each of these

sub-segments connect neighboring intersections that are equipped with Wi-Fi readers. In addition

to higher spatial resolution, the comparison in APPENDIX B is based on each hour of the peak

period, rather than for the whole period. Thus, the results in APPENDIX B provide a more detailed

examination of the speed changes in the system that are useful to the operators of the system.

The above shows that all three data sources show a decrease in travel time on the main street. For

the main street on the whole segment, the evaluation based on HERE data showed the highest

improvement ranging from 7.3% to 20.2%, depending on the direction of travel and the peak period

with a median improvement of 12.5%. The corresponding values based on the NPMRDS was a

range of improvement between 1.7% and 7.9%, with a median of 5.85%. The evaluation based on

the Wi-Fi data showed a range of improvement between -3.4% and 8.3%, with a median value of

3.65%. The only negative impact value obtained in the evaluation was the -3.4% for the eastbound

direction in the AM peak. This small change is acceptable considering the improvements in other

movement performance and the increase in throughput, as described in the rest of the document.

Cross Street Analysis

Figure 19 to Figure 21 shows the speeds for the cross streets. It should be mentioned that the

results for SW 107th Avenue are not given here, due to the lane addition construction along the

street. There are also no results presented based on NPMRDS data for the cross streets due to the

lack of data. The results for SW 137th Avenue are provided based on only HERE data, because

it’s found that the quality of the Wi-Fi data for SW 137th Avenue was too poor to provide

reasonable results.

Figure 19 shows the results during Phase I based on Wi-Fi data. It can be seen that there were no

significant speed changes for SW 97th Ave. However, the speed of SW 87th Ave improved in

both directions and all three time periods. The improvement was particularly high for the SW 87th

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Ave SB. Figure 20 shows the speeds for the cross streets during for Phase II analysis based on

Wi-Fi data. For most directions and time periods, SW 87th Avenue and SW 97th Avenue had

improved speed. The results for phase II based on HERE data as provided in Figure 21 show that

all the cross streets including SW 137th Avenue had improved speeds for all directions and time

periods. This is further reflected in the delay analysis results reported next.

SW 87th Ave

SW 97th Ave

Figure 19: Cross Streets Speed based on Wi-Fi Data (Phase I)

SW 87th Ave

SW 97th Ave

Figure 20: Cross Streets Speed based on Wi-Fi Data (Phase II)

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SW 87th Ave

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3.2.2 Delay

Segment delay is defined as the difference between the time it takes to travel along a given segment

and the time it would have taken to travel along that segment at the free-flow speed without stops.

In this study, the speed limits of the corridor were used as the free-flow speeds. Two types of

delays were calculated and compared. The first is the average delay in minutes per vehicle and

the second is the hourly total delay in veh-hr/hr, which is the delay in minute per vehicle multiplied

by the average hourly volume. The average delay is the measure considered from a traveler point

of view, while the total delay is the measure considered from the system point of view and is used

in benefit-cost analysis, as discussed later in this document.

The average and total delays are included in figures in APPENDIX C. These results indicate the

average delay results, as expected, are consistent with the travel time and speed results presented

in Section 3.2.1. It is more interesting to compare the total delay, since it better illustrates the

impact of the change percentages in travel time of different movements and directions on the

overall delays, considering that different movements have different volumes. Table 7 and Table

8 show the change in delay in veh-hr/hr for the main street and cross streets, respectively. Since

the total delay is a multiplication of the volume and average delay, the results may be biased

because an increase in total delay may be due to an increase in volume, not necessarily in average

delay. For this reason, Figure 80 in Appendix C, shows that the EB of Segment ALL during the

AM peak in Phase I increased when using different volumes for the before and after conditions,

but it decreased when both the before volume is used for both before and after conditions.

However, it’s worth noting that the results presented in Table 7 and Table 8 were calculated using

the same volumes for both the before and after conditions.

Page 64

Table 7: Total Delay of Main Street Traffic based on Different Data Sources

Phase Data Peak Direction Main Street Delay veh-hr/hr

Before After Difference % Difference

I Wi-Fi

AM

EB 488 464 25 5.1

WB 233 157 76 32.4

Both 721 621 100 13.9

MD

EB 336 316 20 6.1

WB 245 235 10 4.1

Both 582 551 31 5.2

PM

EB 385 301 84 21.8

WB 645 682 -36 -5.6

Both 1030 983 48 4.6

All 8831 8265 566 6.4

II

Wi-Fi

AM

EB 463 496 -32 -7.0

WB 206 187 19 9.1

Both 669 683 -14 -2.1

MD

EB 277 270 7 2.4

WB 221 188 33 14.9

Both 498 458 40 8.0

PM

EB 328 303 25 7.7

WB 602 597 5 0.8

Both 930 900 30 3.2

All 7832 7495 336 4.3

HERE

AM

EB 548 480 69 12.5

WB 231 180 51 22.2

Both 779 659 120 15.4

MD

EB 317 255 62 19.6

WB 265 185 80 30.3

Both 582 440 143 24.5

PM

EB 305 234 71 23.2

WB 682 477 205 30.0

Both 987 712 275 27.9

All 8798 6667 2131 24.2

NPMRDS

AM

EB 705 663 42 6.0

WB 269 246 23 8.7

Both 974 909 66 6.8

MD

EB 377 351 26 6.9

WB 282 246 35 12.5

Both 659 597 61 9.3

PM

EB 395 362 33 8.3

WB 674 663 11 1.7

Both 1069 1025 44 4.1 All 9972 9287 685 6.9

Page 65

Table 8: Total Delay of the Cross Street Traffic

Street Data Peak Direction Delay veh-hr/hr - Phase I Delay veh-hr/hr - Phae II


87th Av.

Wi-Fi

AM

SB 20 14 6 31.3 29 32 -3 -9.9

NB 70 29 41 59.0 60 50 10 17.0

Both 90 43 48 52.7 89 81 7 8.3

MD

SB 33 22 11 33.3 39 35 4 10.9

NB 39 30 9 23.8 45 35 10 22.6

Both 72 52 20 28.2 84 70 14 17.2

PM

SB 86 30 56 65.0 82 56 26 31.6

NB 48 30 18 37.0 50 37 13 25.9

Both 133 60 73 55.0 131 93 39 29.4

All 1115.8 632.1 484 43.4 1183.8 939.7 244 20.6

HERE

AM

SB 62 53 8.2 13.3

NB 80 65 14.8 18.6

Both 141 118 23.0 16.3

MD

SB 68 58 10.1 14.9

NB 63 52 10.7 17.2

Both 130 109 20.9 16.0

PM

SB 133 73 60.3 45.2

NB 61 52 9.2 15.1

Both 195 125 69.5 35.7

All 1810.3 1385.6 424.8 23.5

97th Av.

Wi-Fi

AM

SB 9 9 0 0.0 14 15 -1 -9.9

NB 20 18 2 9.5 42 40 2 4.6

Both 29 27 2 6.5 56 55 1 1.0

MD

SB 15 16 -1 -5.9 14 15 -1 -9.4

NB 17 16 2 9.5 20 18 2 10.0

Both 32 31 1 2.5 34 33 1 1.9

PM

SB 32 32 0 0.0 41 33 8 19.2

NB 19 17 3 13.0 27 21 7 24.1

Both 51 48 3 5.0 68 54 14 21.1

All 444.8 427.1 18 4.0 568.1 512.4 56 9.8

Page 66

Street Data Peak Direction Delay veh-hr/hr - Phase I Delay veh-hr/hr - Phae II


97th Av. HERE

AM

SB 32 26 5.6 17.6

NB 62 53 9.6 15.3

Both 94 79 15.2 16.1

MD

SB 27 25 2.6 9.6

NB 28 27 1.4 5.0

Both 55 51 4.0 7.3

PM

SB 65 46 18.9 29.1

NB 33 28 4.1 12.6

Both 98 75 23.0 23.6

All 889.8 752.7 137.2 15.4

137th Av. HERE

AM

SB 11 10 1.2 11.0

NB 146 100 45.7 31.3

Both 157 110 46.9 29.8

MD

SB 17 14 3.1 17.9

NB 22 17 5.5 24.4

Both 39 31 8.5 21.6

PM

SB 127 119 8.1 6.4

NB 17 15 2.0 11.8

Both 144 133 10.1 7.0

All 1073.8 889.3 184.6 17.2

Page 67

The results of Phase 1 assessment of the main street total delay based on Wi-Fi data indicate

significant improvements in the non-peak directions during the AM and PM peak periods. As

shown inTable 7, the total delay of the WB direction in the AM peak and the EB direction in the

PM peak decreased by 32.4% and 21.8% respectively. The delay of the WB direction in the PM

peak (the peak direction in the PM) increased by 5.6%, and the EB direction in the AM peak (the

peak direction in the AM) had a 5.1% decrease in total delay. The total delay of the main street

in the midday improved by 5.2%. Overall, there was a 6.4% reduction in total delay of the main

street for the analyzed 12 hours of the day. Phase II analysis based on Wi-Fi data showed less

total delay benefits to the main street compared to Phase I analysis. Again the non-peak direction

in the peak hour had more positive results than the peak direction but the reductions in travel time

for the non-peak directions (the EB in the PM and WB in the AM) were lower than those based on

Phase I analysis, ranging from 7.7% to 9.1%. While the total delay of the EB in the AM period

increased by 7.0%, the total delay of the WB in the PM period decreased by 0.8%, and the total

delay of the main street decreased by 8.0% in the midday period. Phase II analysis based on Wi-

Fi data indicates there was a 4.3% reduction in total delay of the main street total delay for the

analyzed 12 hours of the analysis. A sensitivity analysis was conducted by excluding the months

of March and April, to isolate the effect of the introduction of the intersection of SW 89th Street.

The results showed a similar trend to the evaluation with these two months but the improvement

in the main street performance increased from 4.3% to 6.3%. Interesting enough, this percentage

is the same as what is calculated in Phase I evaluation,

The Phase II analysis based on HERE data in Table 7 shows significant improvements in total

delay for all main street movements. The improvements in delay are significantly higher than what

was estimated based on Wi-Fi data. The assessed improvement percentage in veh-hr for the two

directions of main street travel for the three analysis periods based on HERE data ranges from

15.4% to 27.9%, with an overall improvement for the 12-hour analysis period of 24.2%. This

improvement percentage seems to be high, particularly when compared with those presented based

on Wi-Fi data earlier. The analysis based on NPMRDS data also shows that both directions had

lower travel time in the after period but to a much less extent than what the analysis based on

HERE data indicates and more in line with the results of the analysis based on Wi-Fi data. The

improvement in travel times of the EB direction in the AM, midday, and PM peak periods

Page 68

according to NPMRDS analysis are 6.0%, 6.9%, and 8.3%; respectively. The improvement for

the westbound direction in the AM, midday, and PM peak periods according to NPMRDS analysis

are 8.7%, 12.5%, and 1.7%; respectively. The reduction in delay of the main street when

considering the 12 hour of the analysis is 6.9%. It can be concluded based on the above results

that the main street have an overall reduction in travel time for the whole day of 6% to 7% based

on NPMRDS and Wi-Fi data analysis with the analysis based on HERE data shows much higher

improvement (24.2%).

The cross street analysis based on Wi-Fi data and HERE data (see Table 8) shows the change in

total delay in veh-hr/hr of the cross street movements. The cross street analysis based on Wi-Fi

data and HERE data conducted in Phase II, showed improvements in cross street total delays. No

analysis based on NPMRDS data was conducted for the cross streets since this data is not available

for the cross streets. Phase I analysis based on Wi-Fi data showed that the total delay benefits for

the 12 hour period for NW 87th Avenue and NW 97th Avenue were 43.4% and 4.0%, respectively.

Phase II analysis based on Wi-Fi data showed that the benefits for the 12-hour period for NW 87th

Avenue and NW 97th Avenue were 20.6% and 9.8%, respectively. Phase II analysis based on

HERE data showed that the benefits for the 12-hour period for NW 87th Avenue, NW 97th

Avenue, and NW 137th Avenue were 23.5%, 15.4%, and 17.2%; respectively.

3.2.3 Percentage of Time in Congestion

The percentage of time in congestion was used to determine the proportion of time that the main

street traffic operates under unacceptable levels of service based on travel time/speed

measurements. In this study, a speed of 20 mph was used as the speed threshold to calculate this

measure, since the speed range for Level of Service (LOS) D is between 17 mph and 22 mph for

the study segments based on the Highway Capacity Manual (HCM) procedure (Section 3.2.4

provides more details on how the LOS is determined). The 20 mph rather than the 17 mph (the

threshold between LOS D and E) was used to indicate congestion. This is because the LOS

analysis is normally performed in 15-minute increments, while the percentage of time in

congestion in this study is calculated for the whole peak period and also for each hour within the

peak. Figure 22to Figure 25 show the percentage of time in congestion for the main street segments

Page 69

during the before and after study periods. More detailed results that involve the assessment of the

percentage of time in congestion for each segment between neighboring intersections using Wi-Fi

reader data and for each hour of the analysis are included in APPENDIX D and can be useful for

the operation analysis.

Figure 22 presents the main street percentage of time in congestion based on Wi-Fi Data during

Phase I. The results show that the percentage of time in congestion decreased for almost all

segments in both directions during all time periods, except for the EB during the AM peak at

Segment II (a very short segment), the WB during the midday period at Segment III, and the EB

during the midday at Segment IV.

Page 70

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 22: Main Street Congestion Percentage based on Wi-Fi Data (Phase I)

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Figure 23 to Figure 25 present the main street percentage of time in congestion during Phase II

based on Wi-Fi Data, HERE data, and NPMRDS data, respectively. Phase II analysis results based

on Wi-Fi data show that the percentage of time in congestion for the EB during the PM peak

decreased significantly. There was also a slight reduction in the percentage of time in congestion

for the WB during the AM peak and mid-day. The EB during the AM peak and WB during PM

peak had slightly increased percentage of time in congestion. However, the results based on HERE

data and NPMRDS data show that all directions and time periods had a reduction in the percentage

of time in congestion. In particular, the analysis based on HERE data show a very high reduction

in the congestion percentage. As examples, the HERE data shows that the percentage of time in

congestion for the WB during the PM peak decreased from 78.6% to 50.1% and the percentage for

the EB during the PM peak decreased from 24.4% to 4.73%.

Page 72

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 23: Main Street Congestion Percentage based on Wi-Fi Data (Phase II)

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Segment I

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Figure 24: Main Street Congestion Percentage based on HERE Data (Phase II)

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Segment I

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Segment ALL

Figure 25: Main Street Congestion Percentage based on NPMRDS Data (Phase II)

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3.2.4 Level of Service

The Highway Capacity Manual (HCM) procedure was used to determine the arterial LOS based

on travel speeds. In this procedure, the first step is to identify the functional category and design

category of the study streets. According to the criteria given in the HCM, the functional category

of both the main street and cross streets in this study is “Principal Arterial,” and their design

category is “Suburban,” except that Segment I of the main street is categorized as “Intermediate”

based on the criteria listed in the HCM, which includes the speed limit and signals per mile. Based

on the functional category and design category, all study segments were determined to be urban

street class II. The LOS of each segment was determined based on its urban street class and

average travel speed by looking up an exhibit table in the HCM.

Table 9 to Table 12 present the LOSs of the time period with the worst LOS in the AM (07:00 AM

to 09:00 AM) and PM (3:30 PM to 7:00 PM) peak periods for the main street and cross streets

based on Wi-Fi data according to Phase I and Phase II analysis, respectively. To clarify, the time

period with the worst LOS can be different for different segment and different direction depending

on the congestion variation along the facility. The LOS for each 15-minute intervals is presented

in APPENDIX E. Please note that although the LOS on most segments appear to be acceptable,

this is likely due to the long segments used in the analysis, which does not consider localized

congestion on individual sub-segments within each segment. The WB of Segment I (a one-mile

segment between SW 67th Avenue and SR 826 NB on-ramp) has the worst LOS (LOS F) for both

the before and after conditions in the PM peak according to Phase I analysis. However, Phase II

analysis shows that in the after conditions, the LOS improved from F to E.

Page 76

Table 9: Results of Main Street Worst LOS Time based on Wi-Fi Data (Phase I)

Segment Direction Time Period

After - I Before - I

Time Speed (MPH)

LOS Time Speed (MPH)

LOS

I EB AM 08:15 - 09:00 21 D 08:00 - 09:00 20 D I EB PM 15:30 - 17:30 21 D 15:30 - 16:30 16 E I WB AM 08:15 - 09:00 21 D 08:30 - 09:00 21 D I WB PM 17:00 - 19:00 11 F 16:45 - 18:45 11 F II EB AM 08:45 - 09:00 17 E 07:15 - 09:00 25 C II EB PM 15:30 - 19:00 31 B 15:30 - 17:00 26 C II WB AM 07:00 - 09:00 33 B 07:00 - 09:00 27 C II WB PM 15:45 - 19:00 27 C 15:30 - 19:00 26 C III EB AM 07:00 - 09:00 30 B 07:15 - 09:00 32 B III EB PM 15:30 - 19:00 24 C 15:30 - 18:30 27 C III WB AM 08:45 - 09:00 28 C 08:30 - 08:45 28 C III WB PM 15:30 - 19:00 27 C 16:45 - 18:00 28 C IV EB AM 07:30 - 09:00 26 C 07:15 - 09:00 26 C IV EB PM 15:30 - 19:00 26 C 16:45 - 18:15 22 D IV WB AM 07:00 - 09:00 32 B 07:15 - 09:00 26 C IV WB PM 16:45 - 19:00 25 C 15:30 - 19:00 24 C

ALL EB AM 07:45 - 09:00 26 C 07:45 - 09:00 27 C ALL EB PM 15:30 - 19:00 25 C 15:30 - 19:00 24 C ALL WB AM 07:00 - 09:00 30 B 07:45 - 09:00 27 C ALL WB PM 15:30 - 19:00 25 C 15:30 - 19:00 25 C

Table 10: Results of Main Street Worst LOS Time based on Wi-Fi Data (Phase II)

Segment Direction Time Period

After - II Before - II

Time Speed (MPH)

LOS Time Speed (MPH)

LOS

I EB AM 08:00 - 09:00 21 D 08:00 - 09:00 21 D I EB PM 15:30 - 17:45 21 D 15:30 - 17:15 20 D I WB AM 07:30 - 09:00 25 C 07:30 - 09:00 25 C I WB PM 16:30 - 18:30 15 E 17:45 - 18:00 13 F II EB AM 07:45 - 09:00 20 D 08:00 - 09:00 20 D II EB PM 15:30 - 19:00 30 B 15:30 - 19:00 32 B II WB AM 07:00 - 09:00 31 B 07:00 - 09:00 32 B II WB PM 15:45 - 19:00 27 C 16:15 - 18:45 27 C III EB AM 07:00 - 09:00 30 B 07:30 - 09:00 33 B III EB PM 15:30 - 19:00 24 C 16:15 - 16:45 28 C III WB AM 07:15 - 09:00 32 B 07:30 - 09:00 33 B III WB PM 16:15 - 19:00 27 C 15:30 - 19:00 29 B IV EB AM 07:15 - 09:00 25 C 07:30 - 09:00 26 C IV EB PM 15:30 - 19:00 26 C 15:30 - 19:00 24 C IV WB AM 08:30 - 09:00 28 C 08:30 - 09:00 28 C IV WB PM 16:00 - 19:00 24 C 16:30 - 19:00 26 C

ALL EB AM 07:30 - 09:00 26 C 08:00 - 09:00 28 C ALL EB PM 15:30 - 19:00 25 C 15:30 - 19:00 26 C ALL WB AM 07:00 - 09:00 31 B 07:00 - 09:00 32 B ALL WB PM 15:30 - 19:00 25 C 16:15 - 19:00 26 C

Page 77

Table 11: Results of Cross Streets Worst LOS Time based on Wi-Fi Data (Phase I)

Segment Direction Time

Period

After-I Before-I

Time Speed

(MPH) LOS Time

Speed

(MPH) LOS

SW 87 Ave NB AM 07:00 - 09:00 21 D 07:45 - 09:00 15 E SW 87 Ave NB PM 15:30 - 19:00 20 D 15:30 - 18:30 16 E SW 87 Ave SB AM 07:00 - 09:00 26 C 08:15 - 09:00 22 D SW 87 Ave SB PM 15:30 - 19:00 23 C 15:30 - 19:00 14 E SW 97 Ave NB AM 07:00 - 09:00 19 D 08:30 - 09:00 17 E SW 97 Ave NB PM 15:30 - 19:00 18 D 15:30 - 19:00 16 E SW 97 Ave SB AM 07:00 - 09:00 20 D 07:00 - 09:00 20 D SW 97 Ave SB PM 16:45 - 19:00 16 E 16:00 - 19:00 16 E

Table 12: Results of Cross Streets Worst LOS Time based on Wi-Fi Data (Phase II)

Segment Direction

Time

Perio

d

After-II Before-II

Time Speed

(MPH) LOS Time

Speed

(MPH) LOS

SW 87 Ave NB AM 07:45 - 09:00 21 D 08:30 - 09:00 22 D SW 87 Ave NB PM 15:30 - 19:00 16 E 17:45 - 19:00 13 F SW 87 Ave SB AM 07:15 - 09:00 19 D 08:45 - 09:00 17 E SW 87 Ave SB PM 15:30 - 19:00 18 D 15:30 - 19:00 16 E SW 97 Ave NB AM 07:45 - 09:00 19 D 07:30 - 09:00 20 D SW 97 Ave NB PM 15:30 - 19:00 16 E 16:30 - 19:00 15 E SW 97 Ave SB AM 08:00 - 09:00 16 E 07:45 - 09:00 15 E SW 97 Ave SB PM 15:30 - 18:30 17 E 15:30 - 19:00 15 E

Page 78

3.2.5 Average Hourly Volume

The average hourly volume, as estimated in this study, is the average number of vehicles per hour

on all through-lanes of a roadway segment in one direction of travel for each time period. Seven-

day volume tube counts were collected in the Phase I evaluation (August of 2015 and 2017).

Figure 26 shows the volumes for the main street segments. It can be seen from these figures that

the obvious changes include the volume increase for Segment II and IV WB during the PM peak

and Segment III EB during AM peak. The detailed average hourly volume results for each tube

count location in the main street are included in APPENDIX F. Comparing the volumes reported

in Figure 26 based on Phase I analysis of the whole facility (Segment ALL), it can be seen that the

main differences between the before and after conditions are the increase in volumes in the

eastbound in the AM peak (by 5.8%) and the westbound in the PM peak (by 5.2%). As discussed

in the previous sections, according to Phase I analysis, there were a small decrease in speed as

shown in Figure 15, in the EB during the AM peak and WB during the PM peak. This slight

decrease in speed is justifiable given the increase in throughputs of these movements and the

improvements in travel times/delays of other intersection movements.

Page 79

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 26: Main Street Average Hourly Volume (Phase I)

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Page 80

The research team did not collect data from the field for Phase II analysis. However, the analysis

of the change in throughput was still possible by using permanent count station data from the

Florida Traffic Online database and temporary (portable) count station data obtained from FDOT

district 6.

The hourly counts were collected from each station for the before and after conditions. For each

station, the hourly daily traffic counts were averaged for each analysis period (the AM peak, PM

peak, and Mid-Day) as shown in Table 13. Table 13 shows that all hourly traffic counts for the

after period were higher than the before period except for few location/time period combinations.

When averaged over the seven station locations, the increase in volume in the eastbound direction

was estimated at 5.78%, 6.66%, and 3.76% for the AM peak, PM peak, and Mid-Day periods;

respectively. For the westbound directions, the corresponding estimates were 5.95%, 2.63%, and

7.47%. The median of the six values listed above is 5.86%. Combining this result with the result

from Phase I analysis, it can be concluded that the system throughput increased by an average of

about 5% to 6% in the after period.

Page 81

Table 13: Portable Stations Hourly Traffic Counts

Sta. no.

Sta. ID Analysis Period

Eastbound Westbound Month

Volume (Veh/hr)

% Difference Results

Volume (Veh/hr)

% Difference Results Before After Before After Before After

1 0005

AM 1700 1725 1.45% Increase 1072 1149 6.7% Increase

May June Mid-Day 1408 1504 6.38% Increase 1428 1497 4.61% Increase

PM 1088 1171 7.09% Increase 1338 1480 9.59% Increase

2 0088


Feb Aug Mid-Day 1302 1434 9.21% Increase 1242 1138 -9.14% Decrease


3 0089


Feb Jun Mid-Day 1573 1698 7.36% Increase 1520 1569 3.12% Increase


4 0090


May Jun Mid-Day 1645 1785 7.84% Increase 1457 1513 3.70% Increase


5 0092


May Jun Mid-Day 1778 1814 1.98% Increase 1798 2006 10.37% Increase

PM 1768 1719 -2.85% Decrease 2521 2409 -4.65% Decrease

6 0589


Jun Aug Mid-Day 1459 1585 7.95% Increase 1268 1286 1.40% Increase


7 2561

AM 1764 2064 14.53% Increase 1045 1022 -2.25% Decrease

Sep Aug Mid-Day 1408 1508 6.63% Increase 1503 1483 -1.35% Decrease


Station Average


Mid-Day 1510 1618 6.66% Increase 1459 1499 2.63% Increase


Page 82

3.3 Reliability

The reliability measures utilized in this study are the Travel Time Index (TTI) and Travel Time

Rate (TTR) Cumulative Distribution Function (CDF). TTI is the ratio of travel time in the peak

period to the travel time at free-flow conditions. TTR is the reciprocal of speed, and it means the

time taken to travel one mile. CDF is the probability that a variable takes a value less than or equal

to certain value. It is a method to describe the distribution of random variables. The reliability

analyses for both phases are based on the Wi-Fi data. The travel time rate, a measure of reliability,

was also estimated based on HERE and NRPDMS data for Phase II.

3.3.1 TTI

Different percentile TTI, including the 95th percentile, 80th percentile, and 50th percentile

(Median) TTIs were calculated for both the before and after study periods. When comparing travel

time measurements, it is important to compare not only the changes in the average values, as is

done in the mobility analysis section of this document but also different percentile travel times.

These percentiles reflect how reliable is the travel along the corridor, which is an important

consideration to travelers trying to arrive on time to their destinations.

Reliability analysis should include the same months in the before and after periods and should be

compared for at least a six month period, and preferably for a whole year. While seven months

data was used for Phase II analysis, only one month data was used for Phase I analysis. Thus, the

results from the reliability analysis based on Phase I should be viewed with caution.

Figure 27 and Figure 28 present the main street TTIs according to Phase I and Phase II analysis,

respectively. The results show that Segment I’s reliability improved for all scenarios. The TTI

results for Segment II (a very short segment) show that, the EB during AM had improved reliability

according to Phase II. For all other segments, the TTI values decreased and their reliability

improved according to both Phase I and Phase II. The TTI results for Segment III show small

changes in reliability. The results for Segment IV show that that the TTI values for the WB during

the AM peak and mid-day and the EB during the PM peak decreased and that there was no

significant change for the EB reliability during the AM peak and midday. When considering the

Page 83

whole segment, the TTI results show improvements in the TTI based on Phase II analysis for all

scenarios except in the EB direction in the midday and PM. Phase II is more dependable than

Phase I analysis for reliability assessment, as described earlier. Overall, it can be stated that there

was no significant improvement in the reliability of the facility based on Wi-Fi data analysis. More

detailed analyses of these changes are presented in the next section when discussing the CDF plots.

APPENDIX G lists the detailed TTI results for each segment between neighboring intersections

with Wi-Fi readers for the main street for each hour of the analysis. This appendix can provide a

more detailed assessment of the reliability of the corridor.

Page 84

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 27: Main Street TTIs (Phase I)

0

5

10

15

AM MD PM AM MD PM

EB WB

TTI

After-I 95th Before-I 95th



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AM MD PM AM MD PM

EB WB

TTI




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TTI




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TTI




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TTI




Page 85

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 28: Main Street TTIs (Phase II)

02468

10

AM MD PM AM MD PM

EB WB

TTI

After-II 95th Before-II 95th



02468

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EB WB

TTI




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EB WB

TTI




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EB WBTT

I




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TTI




Page 86

Figure 29 and Figure 30 show the TTI results for the cross streets according to Phase I and Phase

II analyses, respectively. The TTI values improved for both directions of SW 87th Ave, in all

three time periods. In particular, the TTIs for the SB direction during the PM peak and the NB

direction during the AM peak decreased significantly. Along SW 97th Ave, most of the scenarios

have a slight change in the TTI values except that the TTI for the NB during the AM peak decreased

significantly. The results of Phase II analyses show that in the PM peak, both directions of SW

97th Avenue had significant improvement in reliability.

SW 87th Ave

SW 97th Ave

Figure 29: Cross Streets TTIs (Phase I)

SW 87th Ave

SW 97th Ave

Figure 30: Cross Streets TTIs (Phase II)

02468

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AM MD PM AM MD PM

SB NB

TTI




02468

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AM MD PM AM MD PM

SB NB

TTI




02468

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AM MD PM AM MD PM

SB NB

TTI




02468

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TTI




Page 87

3.3.2 Travel Time Rate CDF

In addition to the travel time index analysis presented in the previous section, the cumulative

distribution functions (CDF) of the travel time rate were also plotted and assessed to allow further

evaluation of the reliability. The travel time rate is defined as the average number of seconds a

vehicle spends to travel one mile along the corridor. Thus, it is the inverse of speed. The CDF

chart displays the cumulative probability of a certain travel time rate value (the probability of

having travel time rate less than or equal to certain value). Figure 31 to Figure 50 show the travel

time rate CDF results for each direction of the main street segments. As a general note, the CDF

indicates a worse reliability if it is to the right and to the bottom of a plot compared to another

CDF.

Figure 31 and Figure 32 present Segment I EB travel time rate CDF based on Phase I and Phase II

analysis, respectively. It can be observed that the reliability improved for all three time periods.

The difference between Phase I and Phase II is that the improvements according to Phase II

analysis were not as significant as according to Phase I analysis. Phase II is more dependable for

reliability analysis.

Page 88

Figure 31: Segment I EB Travel Time Rate CDF (Phase I)

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Page 89

Figure 32: Segment I EB Travel Time Rate CDF (Phase II)

The travel time rate CDF of Segment I WB according to Phase I and Phase II analysis are shown

in Figure 33 and Figure 34, respectively. The results according to Phase I indicate improvements

in the midday and AM peak and deterioration in the PM peak. However, the results of Phase II

during PM peak show improvements in the PM peak and slight deterioration in the AM peak.

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Page 90

Figure 33: Segment I WB Travel Time Rate CDF (Phase I)

0102030405060708090

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Page 91

Figure 34: Segment I WB Travel Time Rate CDF (Phase II)

Figure 35 presents the Segment II EB travel time rate CDF according to Phase I analysis. It can

be observed that during the PM peak and middle day, the reliability improved. The AM peak on

this short link in the EB direction is oversaturated and there is no improvement in travel time.

However, the results during Phase II as shown in Figure 36 indicate slight deterioration for the

three time periods.

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Page 92

Figure 35: Segment II EB Travel Time Rate CDF (Phase I)

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Page 93

Figure 36: Segment II EB Travel Time Rate CDF (Phase II)

Segment II WB travel time rate CDF according to Phase I and Phase II analysis are shown in

Figure 37 and Figure 38, respectively. The results for Phase I show that the reliability improved

significantly for all three time periods. However, the results for Phase II indicate slight

deterioration.

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Page 94

Figure 37: Segment II WB Travel Time Rate CDF (Phase I)

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Travel Time Rate (Second/Mile)


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Page 95

Figure 38: Segment II WB Travel Time Rate CDF (Phase II)

Figure 39 shows the Segment III EB travel time rate CDF according to Phase I analysis. It can be

observed from this figure that the reliability generally improved on this segment according to

Phase I analysis. However, the results for Phase II as shown in Figure 40 indicate slight

deterioration for the AM and PM peak and negligible change for the mid-day.

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Page 96

Figure 39 Segment III EB Travel Time Rate CDF (Phase I)

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Page 97

Figure 40: Segment III EB Travel Time Rate CDF (Phase II)

Segment III WB results according to Phase I and Phase II analyses are shown in Figure 41 and

Figure 42, respectively. The results indicate that the reliability as measured by the 80th or more

percentile generally improved in the AM and midday but got worse in the PM peak (a congested

period).

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Page 98

Figure 41: Segment III WB Travel Time Rate CDF (Phase I)

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Page 99

Figure 42: Segment III WB Travel Time Rate CDF (Phase II)

Segment IV EB results according to Phase I analysis are shown in Figure 43. It can be observed

that the AM and midday peak reliability decreased but the reliability of the PM peak improved.

However, the results according to Phase II analysis, as shown in Figure 44, indicate that there were

no significant change in the reliability in the AM peak and that the reliability for mid-day improved

slightly.

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Page 100

Figure 43: Segment IV EB Travel Time Rate CDF (Phase I)

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Page 101

Figure 44: Segment IV EB Travel Time Rate CDF (Phase II)

Figure 45 and Figure 46 shows the Segment IV WB travel time rate CDF according to Phase I

and Phase II analyses, respectively. The AM peak and midday day became much more reliable.

However, the change in the reliability of the PM peak (congested period) is small.

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Page 102

Figure 45: Segment IV WB Travel Time Rate CDF (Phase I)

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Page 103

Figure 46: Segment IV WB Travel Time Rate CDF (Phase II)

The travel time rate CDF results for EB of the whole study corridor according to Phase I and Phase

II analysis are shown in Figure 47 and Figure 48, respectively. If the 80th percentile TTR is taken

as a reference, then the Phase I results shown in Figure 47 indicate deterioration of 5.1% (297 vs.

312 seconds per mile) in the AM peak, 1.9% deterioration in the midday (207 vs. 211 seconds per

mile), but 5.3% improvement in the PM (226 vs. 214 seconds per mile). According to Phase II

results, shown in Figure 48, these values are 4.5% deterioration (231 vs. 241 seconds per mile),

0% (164 seconds per mile in both cases), and 4.5% improvement (190 vs. 181 seconds per mile),

respectively. Figure 49 and Figure 50 shows the CDF results for the WB for Phase I and Phase II,

respectively. The changes in the 80th percentile TTR according to Phase I for the AM, midday,

and PM peak are 22.1% improvement (234 vs. 182 seconds per mile), 1.9% improvement (182 vs.

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Page 104

179 seconds per mile), and 6.8% deterioration (297 vs. 317 seconds per mile). The Phase II

analysis results, shown in Figure 50 indicate the corresponding changes for AM, MD, and PM are

4.2% improvement (159 vs. 153 seconds per mile), 7.7% improvement (156 vs. 144 seconds per

mile), and 2.8% deterioration (243 vs. 250 seconds per mile). Considering that Phase II reliability

analysis is more dependable. It can be concluded that the improvement in reliability ranges

between -2.8% and 7.7%, depending on the peak period and the direction of travel.

The 80th percentile TTRs were also calculated based on HERE data and NPMRDS data for Phase

II. The results of HERE data indicate that all the directions and time periods had improved

reliability. The improvements for EB are 4.8% (250 vs. 238 seconds per mile), 9.9% (175 vs. 158

seconds per mile), and 12.2% (183 vs. 161 seconds per mile) during AM peak, mid-day, and PM

peak, respectively. The corresponding improvements for WB are 11.8% (167 vs. 147 seconds per

mile), 18.0% (172 vs. 141 seconds per mile), and 20.1% (279 vs. 223 seconds per mile),

respectively. The results based on NPMRDS data also indicate that all the directions and time

periods had improved reliability except that there was almost no change for WB during PM peak

(270 vs. 271 seconds per mile). The improvements for EB are 3.9% (284 vs. 273 seconds per

mile), 3.7% (205 vs. 198 seconds per mile), and 5.6% (224 vs. 211 seconds per mile) during AM

peak, mid-day, and PM peak, respectively. The improvements for WB are 9.4% (191 vs. 173

seconds per mile) and 8.1% (186 vs. 171 seconds per mile) during AM peak and mid-day,

respectively.

Page 105

Figure 47: Segment ALL EB Travel Time Rate CDF (Phase I)

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Page 106

Figure 48: Segment ALL EB Travel Time Rate CDF (Phase II)

.

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Page 107

Figure 49: Segment ALL WB Travel Time Rate CDF (Phase I)

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Page 108

Figure 50: Segment ALL WB Travel Time Rate CDF (Phase II)

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Page 109

3.4 Safety

The safety of SW 8th Street was assessed by calculating the crash rate (number of crashes per year

per mile) based on the Signal Four Analytics crash data. To get longer period of time for the after

condition, the before and after conditions of Phase I were combined with those of Phase II for

safety analysis, respectively. So there was a total of 16 months (from May 2017 to August 2017

and from June 2018 to May 2019) of crash data for the after conditions with adaptive control. The

study used a total of 3 years and 10 months (from January 2014 to March 2017 and from October

2017 to April 2018) of crash data for the before conditions (time-of-day control). It should be

pointed out that a before-after safety analysis should be based on a minimum of three years for the

before period and a minimum of three years for the after period. Thus, the presented results should

be viewed with caution and the analysis should be repeated when three year of crash data becomes

available for the after conditions. The evaluation was done first using the Naïve method. To

address biases in the evaluation, an additional method recommended in the Highway Safety

Manual (HSM) (AASHTO, 2010) was used in the evaluation. This method is referred to as the

Observational Before/After Evaluation Using Safety Performance Functions (the Empirical Bayes

Method) and is explained later in this section.

The crash rate is the frequency of crashes typically measured by the number of crashes per year

per mile. In this study, the crash rates were calculated for four time periods: the AM peak, midday

day, PM peak, and the whole day, which is indicated as “DAY” in the charts. There are two types

of results. One is the crash rate based on the total hours in each period (indicated as “Total” in the

charts), and the other is the crash rate further normalized by the hours in each peak (indicated as

“Hourly” in the charts). The hourly crash rate is the total crash rate divided by the number of

hours in that time period. Figure 51 to Figure 54 show the results. The crash rate results were

further categorized by crash types. The results are included in APPENDIX H. The discussion of

the results in Figures 51 to 54 can be considered as a Naïve evaluation since the stochastic nature

of crashes is not considered. Figure 51 and Figure 52 show the total crash rate and hourly crash

rate for the main street segments. It can be observed that both the total and hourly crash rates were

reduced for Segment ALL. While Segment I had slightly increased crash rates, Segment II, III,

and IV had reduced crash rates. In particular, Segment II had reduced crash rates for the three

Page 110

time periods by 47.7%, 59.6%, and 47.7%, respectively, and the crash rate for the whole day were

42.1% lower in the after conditions compared to the before conditions. However, it should be

mentioned that Segment II is a very short segment. Other significant improvements include

Segment III during mid-day (24.1%), and Segment IV during the PM peak (22.1%). Overall, the

whole day crash rate for Segment ALL decreased by 12.5% (from 176 to 154 crashes per mile per

year). Figure 53 and Figure 54 show the total crash rate and hourly crash rate for the cross streets.

The analysis of cross street crashes indicates that both the total and hourly crash rates increased

based on the Naive evaluation for NW 87th Avenue, NW 97th Avenue, NW 107th Avenue, and

NW 137th Avenue, respectively. However, the EB method results, discussed next, show

improvements in cross street safety.

Page 111

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 51: Main Street Total Crash Rate

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Page 112

.

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 52: Main Street Hourly Crash Rate

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Page 113

SW 87th Ave

SW 97th Ave

SW 107th Ave

SW 137th Ave

Figure 53: Cross Streets Total Crash Rate

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Page 114

The above discussion is based on the Naïve evaluation, as stated earlier. The Observational

Before/After evaluation using Safety Performance Functions SPFs (Empirical Bayes Method) was

also used. This method considers the stochastic nature of crashes by compensating for the

potential bias resulting from regression-to-the mean. In addition, the method accounts for the

change in traffic volumes since a change in volume is expected to result in a natural change in

crash frequency independent of any treatment. Table 14 shows the historical traffic volumes along

the study corridor collected from Florida Traffic Online Database. Table 15 shows the historical

traffic volumes along the cross streets (SW 87th Ave, SW 97th Ave, and SW 137th Ave) that were

used in the analysis.

SW 87th Ave

SW 97th Ave

SW 107th Ave

SW 137th Ave

Figure 54: Cross Streets Hourly Crash Rate

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Page 115

Table 14: Historical AADT in Vehicle per Day for the Study Corridor

Year

SW 142nd AVE

to: SAN MIGUEL

SAN MIGUEL

to: SW 137 AVE

SW 137 AVE

to: SW 127 AVE

SW 127 AVE to: SR 821 SB

SR 821 SB

to: SR 985/SW

107

SR 985/SW 107

to: SR 973/SW 87

SR 973/SW

87 to: SR 826 SB

SR 826 SB To: SW 67

AVE

2014 17000 55000 49500 56000 66500 70500 69000 44000 2015 17826 53000 43500 55000 55000 57000 69000 46000 2016 17969 59000 44500 49000 66500 50000 66000 47500 2017 18056 55000 43500 53500 60000 55000 69500 52500 2018 18806 61500 42500 54000 57000 56500 65000 55000

Table 15: Historical AADT in Vehicle per Day for the Study Corridor Cross Street

SW 87th AVE

Year SR 968/W FLAGLER ST

To: NW 12 ST SW 24 ST

To: US 41/SR 90/SW 8 ST SR 976/SW 40 ST

To: SW 24 ST

2014 59000 34000 34000

2015 57000 38500 32000

2016 61500 40000 32500

2017 52000 35000 32500

2018 54500 35500 30500

SW 97th AVE

Year SW 24 ST/CORAL WAY To: SW 8 ST/TAMIAMI

SR 976/SW 40 ST To: SW 2 4ST

SW 56 ST/MILLER DR To: SR976/SW40ST

2014 18300 14800 16000

2015 18100 14600 16000

2016 17800 16400 13300

2017 17600 16200 13100

2018 19900 14500 11700

SW 137th AVE

Year SW 8th ST To: SW 26th ST SW 42 ST

To: SW 42nd ST SW 56 ST

TO: SW 42nd ST

2014 46500 36500 33500

2015 47000 37000 40500

2016 48500 36000 39500

2017 46500 35000 38500

2018 49000 37500 40000

Page 116

A safety performance function (SPF) is a regression equation that is used to predict the average

crash frequency of roadway segments. Normally, SPFs are function of both geometric

characteristics and traffic volumes. An SPF can be expressed in a general model as shown in

equation (1):

SPF = �(��×��(��)��⋯��) (1)

Where,

SPF = Predicted average crash frequency

AADT = Average Annual Daily Traffic

α, β, ��, ��,…�� = regression coefficients

The HSM recommended calibrating and applying calibration factors for the SPFs to better

represent the local conditions in each state. Vargas et al., (2019) calibrated the SPFs models to

Florida-specific SPFs using the mean squared predicted error, Freeman-Tukey R square goodness-

of-fit, and mean absolute deviation as shown in Table 16. The Florida-specific SPFs were used in

the EB method in this study to estimate the predicted average crash frequency on SW 8th street

corridor.

Table 16: Florida-specific SPFs Regression Coefficients

Category Inj. Sev.

α β Overdispersion C

2-lane T -5.877 0.833 0.748 1.527

FI -6.264 0.805 0.678 1.192

Multi-lane Undivided

T -5.44 0.853 0.694 1.723

FI -4.261 0.655 0.571 1.23

Multi-lane Divided

T -7.545 0.988 0.652 3.064

FI -8.134 0.976 0.545 2.263 * Table adapted from Vargas, Raihan, Alluri, and Gan (2019)

The EB method uses a weight factor, which is a function of the SPF overdispersion parameter to

combine the two estimates into a weighted average. For SW 8th street study corridor, the EB

method was used to combine the observed crash frequency with the statistical model estimate in

Equation 1 using Equation (2) based on the HSM;

Page 117

�� = � ×�� + (1 − �) × �� (2)

Where:

��= expected average crashes frequency for the study period,

� = weighted adjustment to be placed on the SPF prediction,

�� = predicted average crash frequency predicted using SPF

�� = observed crash frequency at the site over the study period.

The weighted adjustment factor, � is a function of the SPF’s overdispersion parameter, k, and

calculated using equation (3)

� =�

��×(��

��) (3)

Where:

� = overdispersion parameter from SPF

Table 18 shows the results from applying the Empirical Bayes method for the main street. This

method shows a reduction in crash frequency of 9.73%. The EB method was also applied to

evaluate the overall impact of ASCT on the safety evaluation on cross streets during the same

before and after periods. Table 18 to Table 20 show the estimated safety effectiveness for the

crash frequency on SW 87th Avenue (11.6% improvement), SW 97th Avenue (4.05%

improvement), and SW 137th Avenue (9.74% improvement); respectively. It is shown that

particularly for the cross street analysis, there is a significant difference between the EB method

and the Naïve method in the safety estimation. The Naïve method is the simplest form of the

Before/After study as it just considered the change in crash rates in both periods and neglect all

other conditions including the stochastic nature in crashes. The results from the Naïve method

showed a slight safety deterioration on the cross streets. The EB method accounts for the increase

in volumes that result in nature change in crash frequency.

Page 118

Table 17: Results of the EB Method on SW 8th Street Before After

Total N observed 2934 2594

Analysis Duration (year) 16 months 16 months

Average AADT 51012 51157

Average N predicted crashes/mile-year

60.55 60.73

Average Total N predicted (crashes)

200.48 202.51

Sum 601.44 607.52

Average weight (w) 0.055 0.055

Adjustment factor ® 1.010107401

N expected 948.24 957.82

Total N expected 2844.73 2873.48

Odds ratio 0.9027

Safety effectiveness 9.73%

Var 2763.174

Var (OR) 0.0005

SE(OR) 0.024

SE(Safety effectiveness) 2.422

Table 18: Results of the EB Method on SW 87th AVE

87th Street - All day Before After

Total N observed 409 323 Analysis Duration (year) 16 months 16 months

Average AADT 33766 31622 Average N predicted SPF

crashes/mile-year 39.95 37.40


17.79 16.60

Sum 53.37 49.82 Average weight (w) 0.055 0.060 Adjustment factor ri 0.933

N expected 130.5 121.83 Total N expected 391.482 365.484

Odds ratio 0.883


Var 300.224

Var (OR) 0.0041

SE(OR) 0.0645


Page 119


97th Street - All day

Before After

Total N observed 180 224 Analysis Duration (year) 16 months 16 months

Average AADT 16144 15666 Average N predicted SPF

crashes/mile-year 18.99 18.42


8.83 8.17

Sum 21.04 33.72 Average weight (w) 0.055 0.060 Adjustment factor ri 1.603


Odds ratio 0.822 Safety effectiveness 4.05%

Var 411.79 Var (OR) 0.011 SE(OR) 0.105



137th Street - All day Before After

Total N observed 329 413

Analysis Duration (year) 16 months 16 months

Average AADT 40555 41388

Average N predicted SPF crashes/mile-year

48.05 49.05


42.94 43.78

Sum 82.80 107.29 Average weight (w) 0.055 0.060

Adjustment factor ri 1.2957


Odds ratio 0.995


Var 845.25 Var (OR) 0.00524 SE(OR) 0.0723


Page 120

4. HYPOTHESIS TESTING RESULTS

Various hypotheses testing was conducted to test the effectiveness of the ASCT. These types of

testing are normally used to compare parameters between two conditions such as the means,

standard deviations, proportions, and data distributions. This is necessary to prove that the

difference in the parameters between the two conditions is statistically significant. For example,

the difference in the means of the data in the before and after conditions could be due to statistical

random variation in the data. Thus, hypotheses testing is conducted to ensure that this is not the

case. The results from statistical hypothesis testing were found to be consistent with the results

of the percentage change in the mean of the mobility measures reported earlier in the mobility

analysis section. This chapter presents the hypotheses and the corresponding testing results.

4.1 Main Street Mobility Hypothesis Testing

The main street mobility hypothesis testing includes the testing of travel time, time in congestion,

and delay. The null hypothesis and alternative hypothesis for each mobility measure are listed as

follows:

Travel Time:

o Null hypothesis: The mean main street route travel time without ASCT is equal to the

mean main street route travel time with ASCT.

o Alternative hypothesis: The mean main street route travel time without ASCT is greater

than the mean main street route travel time with ASCT.

Time in Congestion:

o Null hypothesis: The mean percentage of time in congestion without ASCT is equal to

the mean percentage of time in congestion with ASCT.

o Alternate hypothesis: The mean percentage of time in congestion without ASCT is less

than the percentage of time in congestion with ASCT.

Delay:

o Null hypothesis: The mean delay for the main street without ASCT is equal to the mean

delay for the major street with ASCT.

Page 121

o Alternate hypothesis: The mean delay for major movements without ASCT is greater

than the mean delay for the major street with ASCT.

For each direction and time period of Segment ALL, the means of travel time, time in congestion,

and delay were calculated by 5-minute intervals for both the before and after periods based on

three types of data (Phase I and Phase II for Wi-Fi data; Phase II only for HERE and NPMRDS

data). The paired t-test was performed for the AM peak, since the number of the 5-mintue interval

during the AM peak is less than 30, and the Z-test was performed for the midday and PM peak

periods.

Table 21 to Table 23 present the hypothesis testing results based on Wi-Fi data, HERE data, and

NPMRDS data, respectively. The column “Statistic Value” is the calculated t-test or Z-test statistic

values. The column “P Value 2-tail” is the 2-tailed test p-value, which is compared to the threshold

chosen for statistical significance (0.05) to decide if the null hypothesis is accepted or not. The

column “Equal” indicates if the null hypothesis testing is accepted (“Yes”) or rejected (“No”). The

column “Change” indicates how the measure value changes (increased or decreased) if there were

statistically significant changes between the before and after travel times. “Decreased” means the

measure value decreased and the performance was improved after applying ASCT, and on the

contrary, “Increased” means the measure value was increased.

When the travel time hypothesis testing results are compared with the travel time results shown in

APPENDIX A, it can be observed that the results based on HERE data and NPMRDS data have

statistically significant reduced travel time for all directions and time periods, and the results based

on Wi-Fi data have statistically significant changes of travel time for the following directions and

time periods:

EB during AM peak (increased, both phases)

EB during PM peak (decreased, both phases)

WB during AM peak (decreased, both phases)

WB during mid-day (decreased, Phase II only)

Page 122

It’s worth mentioning that the travel time based on Wi-Fi data show that there was increased travel

time for the EB during mid-day and WB during PM peak in Phase I, but the hypothesis testing

results show that these changes were not statistically significant.

For the time in congestion, the results based on HERE data and NPMRDS data have statistically

significant decreased time in congestion for all directions and time periods except that the decrease

of the time in congestion for the WB during AM and PM peak based on NPMRDS data was not

statistically significant. When the hypothesis testing results are compared with the results in

Section 3.2.3 based on Wi-Fi data, it can be observed that both directions and all time periods have

statistically significant reduced time in congestion in Phase I and that the increased time in

congestion for EB during mid-day and WB during PM peak in Phase II were not statistically

significant.

When the delay hypothesis testing results are compared with the delay per vehicle results provided

in APPENDIX C, it can be observed that the results based on HERE data and NPMRDS data have

statistically significant reduced delay for all directions and time periods except that reduction of

the delay for the WB during PM peak based on NPMRDS data was not statistically significant.

The comparison of the results based on Wi-Fi data shows that the delay increase is not statistically

significant for the EB during mid-day and WB during PM peak in both Phase I and Phase II.

Page 123

Table 21: Main Street Segment ALL Hypothesis Testing Results (Wi-Fi Data)

Hypothesis Testing

Phase Direction Time Period

Statistic Value

P Value 2-tail

Equal Change Mean Difference (Minutes)

Percentage of Change (%)

Travel Time

I

EB AM -3.06 0.00543 No Increased 1.54 5.2

EB MD -1.79 0.073438 Yes 1.14 4.7

EB PM 6.59 4.47E-11 No Decreased -2.5 -9.4

WB AM 21.96 0 No Decreased -5.78 -22.6

WB MD 0.7 0.480843 Yes -0.25 -1.2

WB PM -0.36 0.719064 Yes 0.44 1.4

II

EB AM -10.76 1.16E-10 No Increased 0.81 3.4

EB MD 1.77 0.0760861 Yes -0.39 -2


WB AM 23.56 0 No Decreased -0.92 -5

WB MD 7.83 4.88E-15 No Decreased -1.5 -8.2

WB PM 0.12 0.9052759 Yes -0.1 -0.4

Time in Congestion

I

EB AM 3.09 0.005041 No Decreased -12.34 -26.1 EB MD 2.37 0.017598 No Decreased -7.21 -109.6 EB PM 5.39 7.16E-08 No Decreased -36.28 -75 WB AM 6.38 1.34E-06 No Decreased -45.32 -100 WB MD 2.59 0.009598 No Decreased -4.22 -100 WB PM 1.55 0.12132 Yes -10.69 -20.9

II

EB AM -3.38 0.00244673 No Increased 4.98 8.9 EB MD -0.96 0.33650043 Yes 2.2 30.2 EB PM 7.13 1.03E-12 No Decreased -27.32 -55.3 WB AM 5.03 3.90E-05 No Decreased -2.38 -76.4 WB MD 12 0 No Decreased -4.23 -94.2 WB PM -0.52 0.60494739 Yes 3.39 5.4

Delay I

EB AM -3.03 0.005744 No Increased 1.53 7.6 EB MD -1.78 0.074484 Yes 1.14 7.8 EB PM 6.59 4.41E-11 No Decreased -2.5 -14.5 WB AM 22.03 0 No Decreased -5.77 -35.8 WB MD 0.71 0.480787 Yes -0.25 -2.3 WB PM -0.35 0.723414 Yes 0.43 1.9

Page 124

Hypothesis Testing


Statistic Value

P Value 2-tail



II

EB AM 10.76 1.16E-10 No Decreased -0.81 -5.7 EB MD 1.77 0.076104 Yes -0.39 -3.9 EB PM 6.57 5.17E-11 No Decreased -1.23 -9.4 WB AM 23.58 0 No Decreased -0.92 -10.2 WB MD 7.83 4.88E-15 No Decreased -1.5 -17.1 WB PM 0.12 0.905183 Yes -0.1 -0.6

Table 22: Main Street Segment ALL Hypothesis Testing Results (HERE Data, Phase II)

Hypothesis Testing


Statistic Value

P Value 2-tail



Travel Time II

EB AM 15.65 4.27E-14 No Decreased -1.85 -7.3

EB MD 6.25 4.00E-10 No Decreased -2.12 -10.2

EB PM 14.9 0 No Decreased -2.8 -13.1

WB AM 35.67 0 No Decreased -2.3 -11.8

WB MD 17.29 0 No Decreased -3.12 -15.9

WB PM 6.6 4.12E-11 No Decreased -5.86 -20.1

Time in Congestion

II


EB MD 2.52 0.011855 No Decreased -6.67 -43.2


WB AM 6.85 4.42E-07 No Decreased -5.12 -79.5


WB PM 5 5.64E-07 No Decreased -28.4 -36.2

Delay II


EB MD 6.25 4.00E-10 No Decreased -2.12 -18.6


WB AM 35.66 0 No Decreased -2.3 -23


WB PM 6.6 4.12E-11 No Decreased -5.86 -29.8

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Table 23: Main Street Segment ALL Hypothesis Testing Results (NPMRDS Data, Phase II)

Hypothesis Testing


Statistic Value

P Value 2-tail



Travel Time II




EB PM 3.8 0.000145 No Decreased -1.68 -6.8

WB AM 2.35 0.027965 No Decreased -1.16 -5.5


Time in Congestion

II

EB AM 4.32 0.000235 No Decreased -5.41 -6.4



WB AM 1.81 0.083268 Yes -10.77 -40.7


WB PM 1.28 0.201527 Yes -5.22 -6.1

Delay II



EB PM 3.8 0.000145 No Decreased -1.68 -10.9

WB AM 2.35 0.027963 No Decreased -1.16 -10.1


WB PM 0.16 0.874084 Yes -0.13 -0.7

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4.5 Cross streets Delay Hypothesis Testing

To estimate the performance on cross streets (SW 87th Ave, SW 97th Ave, and SW 137th Ave), the

delay hypothesis testing was conducted for cross streets. The hypotheses are listed as follows:

Null hypothesis: The mean delay for cross street without ASCT is equal to the mean

delay for cross street with ASCT.

Alternative hypothesis: The mean delay for cross street without ASCT is greater than

the mean delay for cross street with ASCT.

For each cross street in each direction and time period, the average delay was calculated by 5-

minute intervals for both the before and after study periods. The paired t-test was performed for

the AM peak, and the Z-test was performed for middle day and PM peak. Table 24 and Table 25

present the hypothesis testing results based on Wi-Fi data and HERE data, respectively.

By comparing the testing results with the delay per vehicle results shown in APPENDIX C, it can

be observed that most of the scenarios are consistent. The testing results based on HERE data

show that all three cross streets in both directions and all three time periods have statistically

significant reduced delay except that the delay reduction for SW 137th Ave in the SB in the PM

peak was not statistically significant. The testing results based on Wi-Fi data show that the delay

per vehicle changes for SW 87th Ave and SW 97th Ave based on Wi-Fi data shown in APPENDIX

C are statistically significant except that the delay reduction of SW 87th Ave SB during mid-day

and SW 97th Ave SB AM during AM peak in Phase II was not statistically significant.

Page 127

Table 24: Cross streets Delay Hypothesis Results based on Wi-Fi Data

Segment Phase Direction Time

Period Statistic

Value P Value 2-

tail Equal Change

Mean Difference (Minutes)


SW 87 Ave

I

SB AM 77.41 0 No Decreased -0.5 -31.6 MD 11.11 0 No Decreased -0.64 -35 PM 46.32 0 No Decreased -2.57 -64.1

NB AM 10.22 3.20E-10 No Decreased -2.26 -58.7 MD 6.68 2.39E-11 No Decreased -0.5 -23.4 PM 24.79 0 No Decreased -0.97 -36.4

II

SB AM -7.9 3.94E-08 No Increased 0.12 8.2 MD 1.79 0.072667 Yes -0.11 -5.5 PM 10.52 0 No Decreased -1.16 -30.2

NB AM 9.5 1.32E-09 No Decreased -0.39 -15.3 MD 11.22 0 No Decreased -0.43 -18.1 PM 25.99 0 No Decreased -0.63 -22.9

SW 97 Ave

I

SB AM 2.31 0.029711 No Decreased -0.03 -1.8 MD -5.56 2.75E-08 No Increased 0.13 7.4 PM 1.3 0.192981 Yes -0.05 -2.1

NB AM 3.84 0.000789 No Decreased -0.27 -12.8 MD 5.83 5.57E-09 No Decreased -0.19 -8.9 PM 50.09 0 No Decreased -0.29 -12.7

II

SB AM -1.68 0.106617 Yes 0.08 5.2 MD -4.44 8.83E-06 No Increased 0.17 10.3 PM 4.52 6.32E-06 No Decreased -0.44 -15.2

NB AM 8.43 1.24E-08 No Decreased -0.16 -6 MD 3.47 0.000517 No Decreased -0.2 -9.1 PM 19.41 0 No Decreased -0.55 -20.6

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Table 25: Cross Streets Delay Hypothesis Results based on HERE Data (Phase II)

Segment Phase Direction Time

Period Statistic Value

P Value 2-tail

Equal Change Mean Difference

(Minutes) Percentage of Change

(%)

SW 87 Ave

II

SB

AM 21.6 0 No Decreased -0.37 -11.2

MD 8.49 0 No Decreased -0.57 -14.6

PM 25.94 0 No Decreased -2.49 -40.3

NB

AM 24.13 0 No Decreased -0.58 -15.7

MD 17.27 0 No Decreased -0.54 -15.5

PM 12.52 0 No Decreased -0.5 -14.1

SW 97 Ave

II

SB

AM 10.24 3.07E-10 No Decreased -0.49 -14.4

MD 5.97 2.37E-09 No Decreased -0.3 -9.1

PM 13.32 0 No Decreased -1.19 -25.7

NB

AM 17.45 3.89E-15 No Decreased -0.58 -14.1

MD 4.62 3.83E-06 No Decreased -0.2 -6.1

PM 12.45 0 No Decreased -0.37 -11

SW 137 Ave

II

SB

AM 8.77 5.93E-09 No Decreased -0.14 -13

MD 12.26 0 No Decreased -0.23 -20.7

PM 1.26 0.20674 Yes -0.24 -7.1

NB

AM 13.15 1.85E-12 No Decreased -1.53 -30

MD 6.88 5.85E-12 No Decreased -0.31 -26.1

PM 12.91 0 No Decreased -0.18 -16.8

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5. BENIFIT-COST ANALYSIS

An important criterion in the selection and adoption of a technology is the return on investment of

the technology. The return on investment analysis is conducted by calculating estimates of the

net present value (NPV) or benefit-cost ratio (B-C Ratio) of the analyzed solution. This involves

estimating of the present values of the current and future benefits and costs over the project’s

economic life. A discount rate is used to calculate the present values of the cash flows.

The travel time reduction for each direction and time periods according to Phase II analysis and

the corresponding hourly volume estimated based on all the volume counts were used to calculate

the saving of vehicle hours travelled (VHT) per day along both main street and cross streets. It’s

worth mentioning that the volume counts used are from the before condition of Phase II. The

saved VHT were multiplied by the number of weekdays per year (260 days) and the dollar value

($15 per vehicle-hour) to get the annual benefit. To get the total 5-year benefits, the annual benefit

was multiplied by 4.1 which is the factor value for an interest rate of 7% and project life of 5 years

to convert benefits to present worth. For the cross streets benefit, it’s further multiplied by the

number of cross streets (8 major cross streets) to get the total benefits for all cross streets. The

sum of the 5-year benefits of both main street and cross streets were divided by the total cost of

the system including one time investment in the ASCT system including hardware and software

and construction costs and recurrent operation and maintenance costs to get the B-C Ratio. Table

26 and Table 27 show the procedure of calculating the B-C Ratio based on Wi-Fi data and HERE

data, respectively. There were no NPMRDS data for cross streets, so they were not used for the

analysis. The results show that the B-C Ratios calculated based on Wi-Fi data and HERE data are

3.7 and 8.4, respectively. It can be concluded that the ASCT system is cost-effective. Please note

that although the evaluation was done to determine the impact of the installed technology, FDOT

District 6 has been effectively involved in the day-to-day management and operations of the

facility. Thus, the benefits presented in this report reflected these activities also.

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Table 26: B-C Ratio Calculation based on Wi-Fi data


EB WB NB SB

AM MD PM AM MD PM AM MD PM AM

MD PM


-0.8* 0.4 1.2 0.9 1.5 0.1 0.4 0.4 0.8 -0.2 0.04 1.1

Hourly Volume 1812 1632 1566 1109 1459 1988 918 932 879 514 879 1203

Hours 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5


-48.3 70.7 109.6 33.3 237.1 11.6 12.2 40.4 41 -3.4 3.8 77.2


414.0 171.2

5-Year Benefits 414.0 * 260 (weekdays) * 15 (dollar value)

* 4.1 (factor value) = $6,619,446.93

171.2 * 260 (weekdays) * 15 (dollar value) * 4.1 (factor value) * 8 (number of

cross streets) = $21,902,654.28

ASCT System Cost $1,236,111.59

Construction Cost $3,877,962.73


$358,279.6


$300,000

5-Year Cost $1,236,111.59 + $3,877,962.73 + 4.1*($358,279.6 + $300,000)

= $7,813,020.68

B-C Ratio ($6,619,447.93 + $21,902,654.28) / $7,813,020.68

= 3.7

* Negative value of travel time reduction means that the travel time was increased.

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Table 27: B-C Ratio Calculation based on HERE Data


EB WB NB SB

AM MD PM AM MD PM AM MD PM AM MD PM


1.9 2.1 2.8 2.3 3.1 5.9 1.2 0.4 0.4 0.4 0.4 1.7

Hourly Volume 1812 1632 1566 1109 1459 1988 918 932 879 514 879 1203

Hours 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5 2 6.5 3.5


114.8 371.3 255.8 85.0 490 684.2 36.7 40.4 20.5 6.9 38.1 119.3


2,001.0 261.9

5-Year Benefits 2,001.0 * 260 (weekdays) * 15 (dollar value)

* 4.1 (factor value) = $31,996,429.73

261.9 * 260 (weekdays) * 15 (dollar value) * 4.1 (factor value) * 8 (number of cross

streets) = $33,496,811.40

ASCT System Cost

$1,236,111.59

Construction Cost

$3,877,962.73


$358,279.6


$300,000

5-Year Cost $1,236,111.59 + $3,877,962.73 + 4.1*($358,279.6 + $300,000)

= $7,813,020.68

B-C Ratio ($31,996,429.73 + $33,496,811.40) / $7,813,020.68

= 8.4

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6. CONCLUSION

The Florida Department of Transportation (FDOT) District 6 deployed an adaptive signal control

system between SW 67th Avenue and SW 142th Avenue along SW 8th Street in Miami-Dade

County. The goal of this project was agreed on in a stakeholder workshop, as follows: “The project

goal is to improve the efficiency of SW 8th Street, between SW 142nd Avenue and SW 67th

Avenue, using sustainable signal technology to minimize congestion and increase throughput

where possible, without compromising safety for all users.”

To evaluate if the project goal has been achieved, data from multiple sources were collected and

processed to calculate the various performance measures for both after and before conditions.

These performance measures can prove the effectiveness of the adaptive signal control system in

terms of mobility, reliability, and safety.

This report presents the results of the evaluation of an adaptive signal control system deployed by

the Florida Department of Transportation (FDOT) District 6 between SW 67th Avenue and SW

142nd Avenue along SW 8th Street in Miami-Dade County. Data from multiple sources were

collected and processed to determine the impacts of deployments on various performance

measures. Three sources of travel time estimates were utilized to assess the mobility performance:

Wi-Fi reader data and two third-party-based travel time data, which are HERE and the National

Performance Management Research Data Set (NPMRDS) data. All three data sources show a

decrease in travel time on the main street and cross street. For the main street on the whole

segment, the evaluation based on HERE data showed the highest improvement ranging from 7.3%

to 20.2%, depending on the direction of travel and the peak period with a median improvement of

12.5%. The corresponding values based on the NPMRDS was a range of improvement between

1.9% and 7.9%, with a median of 5.85%. The evaluation based on the Wi-Fi data showed a range

of improvement between -3.4% and 8.3%, with a median value of 3.65%. The only negative

impact value obtained in the evaluation was the -3.4% for the eastbound direction in the AM peak.

This small change is acceptable considering the improvements in other movement performance

and the increase in throughput, as described below. In terms of total delay in veh-hr aggregated

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over the whole day, the improvements were 4.3%, 24.2%, and 6.9% based on the Wi-Fi data,

HERE, and NPMRDS, with an average of 11.8% among the three data sources.

The analysis showed significant improvements in cross street delays in terms of veh-hr. When

considering both directions of the cross streets and aggregating overall periods of the day, the

analysis based on Wi-Fi data showed improvements of 20.6% and 9.8% for SW 87th Avenue and

SW 97th Avenue, respectively. The analysis based on HERE data showed improvements of 23.5%,

15.4%, and 17.2% for SW 87th Avenue, SW 97th Avenue, and SW 137th Avenue, respectively.

The Wi-Fi data for SW 137th Avenue had quality issues that prevented it from being used in the

analysis. Data from the NPMRDS are not available for the evaluated cross streets since they are

not on the National Highway System.

In general, the evaluation showed a reduction in the percentage of time in congestion on the

corridor. In addition, the throughput analysis indicates that there is an increase in the throughput

of the system by an average of about 5% to 6%.

For safety analysis, this study utilized the Observational Before/After Evaluation using Safety

Performance Functions (the Empirical Bayes Method) recommended in the Highway Safety

Manual (HSM). This method showed a reduction in crash frequency on the main street by 9.73%.

For the cross streets, the estimated reductions in the crash frequency were 11.6%, 4.05%, and

9.74% for SW 87th Avenue, SW 97th Avenue, and SW 137th Avenue; respectively.

While reliability analysis results based on Wi-Fi data showed that there was no significant change

in the reliability of the main street, the 80th percentile TTR results based on HERE data and

NPMRDS data showed that the reliability improvement were between 4.8% and 20.1% (HERE

data) and between 3.7% and 9.4% (NPMRDS data). In addition, there were significant reliability

improvements for both directions of the evaluated cross streets based on Wi-Fi data.

Various hypothesis testing was also conducted to verify the effectiveness of the ASCT, considering

the stochasticity of the measured variables. The hypothesis testing results were found to be

consistent with the findings mentioned above. A return on investment analysis was also conducted

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by calculating the benefit-cost ratio (B-C Ratio) for 5-year project life. The results show that the

B-C Ratios calculated based on Wi-Fi data and HERE data are 3.7 and 8.4, respectively. It’s worth

noting that the benefits include the operational benefits only. If the safety benefits were included,

the B-C Ratio would be even higher. The safety benefits will be added when three year of crash

data becomes available for the after conditions.

Based on the evaluation results presented in this report, it can be concluded that the goal of the

project was fulfilled successfully.

Page 135

7. REFERENCE

AASHTO, 2010. The Highway Safety Manual, American Association of State Highway Transportation Professionals, Washington, D.C., http://www.highwaysafetymanual.org. Hadi, M., Y. Xiao, T. Wang, P. Hu, J. Jia, R. Edelstein, and A. Lopez. Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Florida. The 94th Annual Meeting of the Transportation Research Board, Washington, D.C, 2015. Herbel, S., Laing, L., & McGovern, C. (2010). Highway safety improvement program (HSIP) manual. US Department of Transportation, Federal Highway Administration, Office of Safety, Washington, DC. Vargas, H., Raihan, A., Alluri, P., & Gan, A. (2019). Jurisdiction-Specific versus SafetyAnalyst-Default Safety Performance Functions: Case Study on Two-Lane and Multi-Lane Arterials. Transportation Research Record, 0361198119848710.

http://www.highwaysafetymanual.org/

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APPENDIX A. TRAVEL TIME

Segment I

Segment II

Segment III

Segment IV

Segment ALL

Figure 55: Main Street Travel Time Based on Wi-Fi Data (Phase I)

0

2

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6

8

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EB WB

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Segment I

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Figure 57: Main Street Travel Time Based on HERE Data (Phase II)

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Figure 58: Main Street Travel Time Based on NPMRDS Data (Phase II)

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Figure 59: Cross Streets Travel Time based on Wi-Fi Data (Phase I)

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Figure 60: Cross Streets Travel Time based on Wi-Fi Data (Phase II)

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Figure 61: Cross Streets Travel Time based on HERE Data (Phase II)

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APPENDIX B. DETAILED SPEED RESULTS BASED ON Wi-Fi DATA

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Figure 62: Speed between SW 67 Ave and SW 72 Ave

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Figure 63: Speed between SW 72 Ave and SR 826 NB Ramp

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Figure 64: Speed between SR 826 NB Ramp and SR 826 SB Ramp

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Figure 65: Speed between SR 826 SB Ramp and SW 82 Ave

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Figure 72: Speed between SW 109 Ave and SW Turnpike NB Ramp

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Figure 73: Speed between Turnpike NB Ramp and Turnpike SB Ramp

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Figure 74: Speed between Turnpike SB Ramp and SW 122 Ave

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APPENDIX C. DELAY RESULTS

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Figure 79: Main Street Delay per Vehicle based on Wi-Fi Data (Phase I)

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Segment I

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Figure 80: Main Street Hourly Delay based on Wi-Fi Data (Phase I)

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Figure 81: Main Street Delay per Vehicle based on Wi-Fi Data (Phase II)

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Figure 82: Main Street Delay per Vehicle based on HERE Data (Phase II)

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Figure 83: Main Street Delay per Vehicle based on NPMRDS Data (Phase II)

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Figure 84: Mainline Hourly Delay based on Wi-Fi Data (Phase II)

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Figure 85: Mainline Hourly Delay based on HERE Data (Phase II)

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Figure 86: Mainline Hourly Delay based on NPMRDS Data (Phase II)

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SW 87th Ave

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Figure 87: Cross Streets Delay per Vehicle based on Wi-Fi Data (Phase I)

SW 87th Ave (Used After Volume for Before)

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Figure 88: Cross Streets Hourly Delay based on Wi-Fi Data (Phase I)

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Figure 89: Cross Streets Delay per Vehicle based on Wi-Fi Data (Phase II)

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Figure 90: Cross Streets Delay per Vehicle based on HERE Data (Phase II)

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Figure 91: Cross Streets Hourly Delay based on Wi-Fi Data (Phase II)

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SW 87th Ave

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Figure 92: Cross Streets Hourly Delay based on HERE Data (Phase II)

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APPENDIX D. DETAILED CONGESTION TIME PERCENTAGE (Wi-Fi DATA)

Phase I

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Figure 93: Congestion Percentage between SW 67 Ave and SW 72 Ave

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Figure 94: Congestion Percentage between SW 72 Ave and SR 826 NB Ramp

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Phase I

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Figure 95: Congestion Percentage between SR 826 NB Ramp and SR 826 SB Ramp

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Figure 96: Congestion Percentage between SR 826 SB Ramp and SW 82 Ave

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Phase I

Phase II

Figure 103: Congestion Percentage between SW 109 Ave and SW Turnpike NB Ramp

Phase I

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Figure 104: Congestion Percentage between Turnpike NB Ramp and Turnpike SB Ramp

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Figure 105: Congestion Percentage between Turnpike SB Ramp and SW 122 Ave

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Page 171

Phase I

Phase II


Phase I

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Page 172

Phase I

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Page 173

APPENDIX E. DETAILED LEVEL OF SERVICE RESULTS (Wi-Fi DATA)

Table 28: LOS by 15-Minute during Peak Periods for Main Street Movements (Phase I)

Segment, Direction and

Period Time Interval

After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

I

E

AM

07:00 - 07:15 30 B 25 C 07:15 - 07:30 28 C 24 C 07:30 - 07:45 25 C 23 C 07:45 - 08:00 23 C 22 C 08:00 - 08:15 23 C 22 D 08:15 - 08:30 22 D 21 D 08:30 - 08:45 21 D 20 D 08:45 - 09:00 20 D 20 D

PM

15:30 - 15:45 21 D 16 E 15:45 - 16:00 21 D 16 E 16:00 - 16:15 21 D 16 E 16:15 - 16:30 21 D 17 E 16:30 - 16:45 21 D 17 E 16:45 - 17:00 22 D 17 D 17:00 - 17:15 22 D 18 D 17:15 - 17:30 22 D 18 D 17:30 - 17:45 22 C 18 D 17:45 - 18:00 22 C 18 D 18:00 - 18:15 22 C 19 D 18:15 - 18:30 23 C 20 D 18:30 - 18:45 23 C 20 D 18:45 - 19:00 24 C 20 D

W

AM

07:00 - 07:15 32 B 31 B 07:15 - 07:30 30 B 29 B 07:30 - 07:45 27 C 27 C 07:45 - 08:00 25 C 25 C 08:00 - 08:15 23 C 24 C 08:15 - 08:30 22 D 23 C 08:30 - 08:45 21 D 22 D 08:45 - 09:00 20 D 21 D

PM

15:30 - 15:45 14 E 15 E 15:45 - 16:00 15 E 16 E 16:00 - 16:15 16 E 15 E 16:15 - 16:30 15 E 14 E 16:30 - 16:45 14 E 13 E 16:45 - 17:00 13 E 13 F 17:00 - 17:15 12 F 12 F 17:15 - 17:30 11 F 11 F

Page 174



After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

17:30 - 17:45 10 F 10 F 17:45 - 18:00 10 F 10 F 18:00 - 18:15 10 F 11 F 18:15 - 18:30 10 F 11 F 18:30 - 18:45 11 F 12 F 18:45 - 19:00 12 F 13 E

II

E

AM

07:00 - 07:15 32 B 31 B 07:15 - 07:30 28 C 29 B 07:30 - 07:45 25 C 27 C 07:45 - 08:00 24 C 26 C 08:00 - 08:15 22 D 26 C 08:15 - 08:30 19 D 24 C 08:30 - 08:45 18 D 24 C 08:45 - 09:00 17 E 25 C

PM

15:30 - 15:45 33 B 27 C 15:45 - 16:00 31 B 26 C 16:00 - 16:15 30 B 26 C 16:15 - 16:30 30 B 26 C 16:30 - 16:45 30 B 27 C 16:45 - 17:00 29 B 28 C 17:00 - 17:15 30 B 29 B 17:15 - 17:30 30 B 29 B 17:30 - 17:45 30 B 29 B 17:45 - 18:00 31 B 29 B 18:00 - 18:15 31 B 29 B 18:15 - 18:30 31 B 29 B 18:30 - 18:45 31 B 30 B 18:45 - 19:00 32 B 30 B

W

AM

07:00 - 07:15 34 B 28 B 07:15 - 07:30 34 B 27 C 07:30 - 07:45 33 B 26 C 07:45 - 08:00 32 B 28 C 08:00 - 08:15 33 B 28 C 08:15 - 08:30 32 B 27 C 08:30 - 08:45 31 B 27 C 08:45 - 09:00 31 B 27 C

PM

15:30 - 15:45 28 B 25 C 15:45 - 16:00 28 C 26 C 16:00 - 16:15 29 B 25 C 16:15 - 16:30 28 B 25 C 16:30 - 16:45 28 C 26 C 16:45 - 17:00 28 B 26 C

Page 175



After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

17:00 - 17:15 28 C 26 C 17:15 - 17:30 27 C 26 C 17:30 - 17:45 27 C 26 C 17:45 - 18:00 27 C 26 C 18:00 - 18:15 27 C 27 C 18:15 - 18:30 27 C 27 C 18:30 - 18:45 27 C 27 C 18:45 - 19:00 28 C 27 C

III

E

AM

07:00 - 07:15 32 B 37 A 07:15 - 07:30 32 B 35 A 07:30 - 07:45 31 B 34 B 07:45 - 08:00 31 B 34 B 08:00 - 08:15 30 B 33 B 08:15 - 08:30 29 B 32 B 08:30 - 08:45 29 B 32 B 08:45 - 09:00 28 B 32 B

PM

15:30 - 15:45 25 C 28 C 15:45 - 16:00 24 C 28 C 16:00 - 16:15 23 C 27 C 16:15 - 16:30 23 C 27 C 16:30 - 16:45 23 C 27 C 16:45 - 17:00 23 C 27 C 17:00 - 17:15 24 C 27 C 17:15 - 17:30 23 C 27 C 17:30 - 17:45 23 C 28 C 17:45 - 18:00 24 C 28 B 18:00 - 18:15 24 C 28 B 18:15 - 18:30 24 C 28 B 18:30 - 18:45 25 C 29 B 18:45 - 19:00 25 C 30 B

W

AM

07:00 - 07:15 32 B 35 B 07:15 - 07:30 30 B 33 B 07:30 - 07:45 30 B 32 B 07:45 - 08:00 29 B 31 B 08:00 - 08:15 29 B 30 B 08:15 - 08:30 29 B 30 B 08:30 - 08:45 28 B 29 B 08:45 - 09:00 28 C 29 B

PM

15:30 - 15:45 28 C 30 B 15:45 - 16:00 28 C 29 B 16:00 - 16:15 28 C 29 B 16:15 - 16:30 27 C 29 B

Page 176



After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

16:30 - 16:45 27 C 29 B 16:45 - 17:00 27 C 29 B 17:00 - 17:15 27 C 28 B 17:15 - 17:30 27 C 28 C 17:30 - 17:45 26 C 28 B 17:45 - 18:00 26 C 28 B 18:00 - 18:15 27 C 29 B 18:15 - 18:30 27 C 29 B 18:30 - 18:45 27 C 30 B 18:45 - 19:00 27 C 30 B

IV

E

AM

07:00 - 07:15 32 B 30 B 07:15 - 07:30 29 B 29 B 07:30 - 07:45 28 C 27 C 07:45 - 08:00 26 C 26 C 08:00 - 08:15 25 C 26 C 08:15 - 08:30 25 C 25 C 08:30 - 08:45 25 C 25 C 08:45 - 09:00 25 C 26 C

PM

15:30 - 15:45 25 C 25 C 15:45 - 16:00 25 C 24 C 16:00 - 16:15 26 C 24 C 16:15 - 16:30 26 C 24 C 16:30 - 16:45 25 C 23 C 16:45 - 17:00 25 C 22 D 17:00 - 17:15 26 C 22 D 17:15 - 17:30 26 C 22 D 17:30 - 17:45 27 C 22 D 17:45 - 18:00 27 C 22 D 18:00 - 18:15 27 C 22 C 18:15 - 18:30 27 C 23 C 18:30 - 18:45 27 C 23 C 18:45 - 19:00 28 C 23 C

W

AM

07:00 - 07:15 34 B 29 B 07:15 - 07:30 33 B 28 C 07:30 - 07:45 33 B 27 C 07:45 - 08:00 32 B 26 C 08:00 - 08:15 32 B 25 C 08:15 - 08:30 32 B 25 C 08:30 - 08:45 31 B 25 C 08:45 - 09:00 30 B 24 C

PM 15:30 - 15:45 30 B 26 C 15:45 - 16:00 30 B 26 C

Page 177



After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

16:00 - 16:15 30 B 25 C 16:15 - 16:30 29 B 25 C 16:30 - 16:45 28 B 25 C 16:45 - 17:00 28 C 25 C 17:00 - 17:15 27 C 24 C 17:15 - 17:30 26 C 24 C 17:30 - 17:45 25 C 23 C 17:45 - 18:00 24 C 24 C 18:00 - 18:15 24 C 24 C 18:15 - 18:30 24 C 24 C 18:30 - 18:45 24 C 25 C 18:45 - 19:00 24 C 25 C

ALL

E

AM

07:00 - 07:15 32 B 32 B 07:15 - 07:30 30 B 30 B 07:30 - 07:45 28 B 29 B 07:45 - 08:00 27 C 28 B 08:00 - 08:15 26 C 28 C 08:15 - 08:30 25 C 27 C 08:30 - 08:45 25 C 27 C 08:45 - 09:00 25 C 27 C

PM

15:30 - 15:45 25 C 25 C 15:45 - 16:00 25 C 25 C 16:00 - 16:15 24 C 25 C 16:15 - 16:30 24 C 24 C 16:30 - 16:45 24 C 24 C 16:45 - 17:00 24 C 24 C 17:00 - 17:15 25 C 24 C 17:15 - 17:30 25 C 24 C 17:30 - 17:45 25 C 24 C 17:45 - 18:00 25 C 25 C 18:00 - 18:15 26 C 25 C 18:15 - 18:30 26 C 25 C 18:30 - 18:45 26 C 25 C 18:45 - 19:00 26 C 26 C

W AM

07:00 - 07:15 33 B 32 B 07:15 - 07:30 32 B 30 B 07:30 - 07:45 31 B 29 B 07:45 - 08:00 30 B 28 B 08:00 - 08:15 30 B 28 C 08:15 - 08:30 29 B 27 C 08:30 - 08:45 29 B 27 C 08:45 - 09:00 28 B 26 C

Page 178



After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

PM

15:30 - 15:45 27 C 26 C 15:45 - 16:00 27 C 26 C 16:00 - 16:15 27 C 26 C 16:15 - 16:30 26 C 25 C 16:30 - 16:45 26 C 25 C 16:45 - 17:00 26 C 25 C 17:00 - 17:15 25 C 25 C 17:15 - 17:30 24 C 24 C 17:30 - 17:45 24 C 24 C 17:45 - 18:00 23 C 24 C 18:00 - 18:15 23 C 25 C 18:15 - 18:30 23 C 25 C 18:30 - 18:45 24 C 25 C 18:45 - 19:00 24 C 26 C

Table 29: LOS by 15-Minute during Peak Periods for Main Street Movements (Phase II)



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

I E

AM

07:00 - 07:15 27 C 28 C 07:15 - 07:30 25 C 26 C 07:30 - 07:45 23 C 24 C 07:45 - 08:00 23 C 22 C 08:00 - 08:15 22 D 22 D 08:15 - 08:30 21 D 21 D 08:30 - 08:45 21 D 20 D 08:45 - 09:00 20 D 20 D

PM

15:30 - 15:45 19 D 19 D 15:45 - 16:00 20 D 20 D 16:00 - 16:15 21 D 20 D 16:15 - 16:30 21 D 20 D 16:30 - 16:45 21 D 21 D 16:45 - 17:00 21 D 21 D 17:00 - 17:15 22 D 22 D 17:15 - 17:30 22 D 22 C 17:30 - 17:45 22 D 22 C 17:45 - 18:00 22 C 22 C 18:00 - 18:15 23 C 22 C 18:15 - 18:30 23 C 23 C

Page 179



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

18:30 - 18:45 24 C 24 C 18:45 - 19:00 24 C 24 C

W

AM

07:00 - 07:15 31 B 32 B 07:15 - 07:30 29 B 30 B 07:30 - 07:45 27 C 28 C 07:45 - 08:00 25 C 26 C 08:00 - 08:15 24 C 25 C 08:15 - 08:30 24 C 24 C 08:30 - 08:45 24 C 24 C 08:45 - 09:00 24 C 24 C

PM

15:30 - 15:45 17 D 18 D 15:45 - 16:00 18 D 18 D 16:00 - 16:15 18 D 18 D 16:15 - 16:30 17 D 17 E 16:30 - 16:45 16 E 16 E 16:45 - 17:00 16 E 15 E 17:00 - 17:15 15 E 15 E 17:15 - 17:30 14 E 14 E 17:30 - 17:45 14 E 13 E 17:45 - 18:00 13 E 13 F 18:00 - 18:15 14 E 13 E 18:15 - 18:30 15 E 14 E 18:30 - 18:45 17 D 15 E 18:45 - 19:00 20 D 17 D

II E

AM

07:00 - 07:15 30 B 31 B 07:15 - 07:30 27 C 28 B 07:30 - 07:45 23 C 25 C 07:45 - 08:00 22 D 23 C 08:00 - 08:15 20 D 21 D 08:15 - 08:30 19 D 19 D 08:30 - 08:45 19 D 19 D 08:45 - 09:00 19 D 19 D

PM

15:30 - 15:45 30 B 32 B 15:45 - 16:00 29 B 31 B 16:00 - 16:15 30 B 32 B 16:15 - 16:30 30 B 32 B 16:30 - 16:45 30 B 32 B 16:45 - 17:00 30 B 32 B 17:00 - 17:15 30 B 32 B 17:15 - 17:30 30 B 32 B 17:30 - 17:45 30 B 32 B 17:45 - 18:00 30 B 32 B

Page 180



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

18:00 - 18:15 31 B 32 B 18:15 - 18:30 31 B 33 B 18:30 - 18:45 31 B 33 B 18:45 - 19:00 32 B 33 B

W

AM

07:00 - 07:15 32 B 33 B 07:15 - 07:30 32 B 33 B 07:30 - 07:45 31 B 32 B 07:45 - 08:00 31 B 32 B 08:00 - 08:15 31 B 31 B 08:15 - 08:30 31 B 31 B 08:30 - 08:45 31 B 31 B 08:45 - 09:00 31 B 31 B

PM

15:30 - 15:45 29 B 29 B 15:45 - 16:00 28 C 29 B 16:00 - 16:15 28 C 28 B 16:15 - 16:30 27 C 28 C 16:30 - 16:45 26 C 27 C 16:45 - 17:00 27 C 27 C 17:00 - 17:15 27 C 27 C 17:15 - 17:30 27 C 28 C 17:30 - 17:45 27 C 27 C 17:45 - 18:00 26 C 27 C 18:00 - 18:15 26 C 27 C 18:15 - 18:30 26 C 27 C 18:30 - 18:45 27 C 27 C 18:45 - 19:00 27 C 28 B

III E

AM

07:00 - 07:15 34 B 37 A 07:15 - 07:30 32 B 35 A 07:30 - 07:45 31 B 34 B 07:45 - 08:00 30 B 33 B 08:00 - 08:15 30 B 33 B 08:15 - 08:30 29 B 33 B 08:30 - 08:45 29 B 32 B 08:45 - 09:00 30 B 32 B

PM

15:30 - 15:45 25 C 29 B 15:45 - 16:00 24 C 28 B 16:00 - 16:15 24 C 28 B 16:15 - 16:30 24 C 28 C 16:30 - 16:45 24 C 28 C 16:45 - 17:00 24 C 28 B 17:00 - 17:15 24 C 29 B 17:15 - 17:30 24 C 28 B

Page 181



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

17:30 - 17:45 24 C 29 B 17:45 - 18:00 25 C 29 B 18:00 - 18:15 25 C 29 B 18:15 - 18:30 25 C 29 B 18:30 - 18:45 26 C 29 B 18:45 - 19:00 26 C 30 B

W

AM

07:00 - 07:15 35 A 36 A 07:15 - 07:30 34 B 36 A 07:30 - 07:45 33 B 35 B 07:45 - 08:00 32 B 34 B 08:00 - 08:15 32 B 33 B 08:15 - 08:30 32 B 33 B 08:30 - 08:45 31 B 32 B 08:45 - 09:00 31 B 32 B

PM

15:30 - 15:45 28 B 29 B 15:45 - 16:00 28 B 29 B 16:00 - 16:15 28 B 29 B 16:15 - 16:30 28 C 29 B 16:30 - 16:45 27 C 29 B 16:45 - 17:00 27 C 29 B 17:00 - 17:15 27 C 29 B 17:15 - 17:30 26 C 29 B 17:30 - 17:45 26 C 29 B 17:45 - 18:00 26 C 29 B 18:00 - 18:15 26 C 29 B 18:15 - 18:30 26 C 29 B 18:30 - 18:45 26 C 29 B 18:45 - 19:00 27 C 30 B

IV E

AM

07:00 - 07:15 29 B 30 B 07:15 - 07:30 27 C 28 B 07:30 - 07:45 25 C 27 C 07:45 - 08:00 24 C 26 C 08:00 - 08:15 24 C 26 C 08:15 - 08:30 24 C 26 C 08:30 - 08:45 24 C 26 C 08:45 - 09:00 25 C 27 C

PM

15:30 - 15:45 23 C 24 C 15:45 - 16:00 24 C 24 C 16:00 - 16:15 25 C 24 C 16:15 - 16:30 25 C 24 C 16:30 - 16:45 25 C 24 C 16:45 - 17:00 25 C 24 C

Page 182



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

17:00 - 17:15 26 C 24 C 17:15 - 17:30 26 C 24 C 17:30 - 17:45 26 C 24 C 17:45 - 18:00 26 C 25 C 18:00 - 18:15 27 C 25 C 18:15 - 18:30 27 C 25 C 18:30 - 18:45 27 C 25 C 18:45 - 19:00 27 C 25 C

W

AM

07:00 - 07:15 32 B 32 B 07:15 - 07:30 31 B 31 B 07:30 - 07:45 31 B 30 B 07:45 - 08:00 30 B 30 B 08:00 - 08:15 29 B 29 B 08:15 - 08:30 28 B 28 B 08:30 - 08:45 28 C 28 C 08:45 - 09:00 27 C 28 C

PM

15:30 - 15:45 30 B 30 B 15:45 - 16:00 29 B 30 B 16:00 - 16:15 28 C 29 B 16:15 - 16:30 27 C 28 B 16:30 - 16:45 26 C 27 C 16:45 - 17:00 25 C 26 C 17:00 - 17:15 24 C 26 C 17:15 - 17:30 24 C 25 C 17:30 - 17:45 23 C 25 C 17:45 - 18:00 23 C 25 C 18:00 - 18:15 23 C 25 C 18:15 - 18:30 23 C 25 C 18:30 - 18:45 24 C 25 C 18:45 - 19:00 24 C 25 C

ALL E

AM

07:00 - 07:15 31 B 33 B 07:15 - 07:30 29 B 31 B 07:30 - 07:45 27 C 29 B 07:45 - 08:00 26 C 28 B 08:00 - 08:15 26 C 28 C 08:15 - 08:30 25 C 27 C 08:30 - 08:45 25 C 27 C 08:45 - 09:00 26 C 28 C

PM

15:30 - 15:45 24 C 26 C 15:45 - 16:00 24 C 26 C 16:00 - 16:15 24 C 26 C 16:15 - 16:30 24 C 26 C

Page 183



After-II Speed

(MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

16:30 - 16:45 24 C 26 C 16:45 - 17:00 25 C 26 C 17:00 - 17:15 25 C 26 C 17:15 - 17:30 25 C 26 C 17:30 - 17:45 25 C 26 C 17:45 - 18:00 25 C 27 C 18:00 - 18:15 26 C 27 C 18:15 - 18:30 26 C 27 C 18:30 - 18:45 26 C 27 C 18:45 - 19:00 27 C 28 C

W

AM

07:00 - 07:15 33 B 34 B 07:15 - 07:30 32 B 33 B 07:30 - 07:45 31 B 32 B 07:45 - 08:00 31 B 32 B 08:00 - 08:15 30 B 31 B 08:15 - 08:30 30 B 30 B 08:30 - 08:45 29 B 30 B 08:45 - 09:00 29 B 30 B

PM

15:30 - 15:45 28 C 29 B 15:45 - 16:00 27 C 28 B 16:00 - 16:15 27 C 28 B 16:15 - 16:30 26 C 28 C 16:30 - 16:45 25 C 27 C 16:45 - 17:00 25 C 27 C 17:00 - 17:15 24 C 27 C 17:15 - 17:30 24 C 26 C 17:30 - 17:45 23 C 26 C 17:45 - 18:00 23 C 26 C 18:00 - 18:15 23 C 26 C 18:15 - 18:30 24 C 26 C 18:30 - 18:45 24 C 26 C 18:45 - 19:00 25 C 27 C

Page 184

Table 30: LOS by 15-Minute Interval during Peak Periods for Cross streets (Phase I)

Street, Direction and Period Time Interval After-I Speed

(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

SW 87th Ave

S

AM

07:00 - 07:15 26 C 21 D

07:15 - 07:30 25 C 21 D

07:30 - 07:45 26 C 21 D

07:45 - 08:00 26 C 20 D

08:00 - 08:15 26 C 20 D

08:15 - 08:30 26 C 20 D

08:30 - 08:45 26 C 21 D

08:45 - 09:00 26 C 21 D

PM

15:30 - 15:45 23 C 14 E

15:45 - 16:00 23 C 14 E

16:00 - 16:15 23 C 13 E

16:15 - 16:30 23 C 13 F

16:30 - 16:45 23 C 13 F

16:45 - 17:00 23 C 13 F

17:00 - 17:15 23 C 12 F

17:15 - 17:30 23 C 12 F

17:30 - 17:45 22 C 12 F

17:45 - 18:00 22 C 13 F

18:00 - 18:15 22 C 13 F

18:15 - 18:30 22 C 13 F

18:30 - 18:45 22 C 13 E

18:45 - 19:00 22 C 14 E

N

AM

07:00 - 07:15 21 D 18 D

07:15 - 07:30 21 D 17 E

07:30 - 07:45 21 D 15 E

07:45 - 08:00 21 D 14 E

08:00 - 08:15 21 D 14 E

08:15 - 08:30 21 D 14 E

08:30 - 08:45 21 D 14 E

08:45 - 09:00 21 D 14 E

PM

15:30 - 15:45 21 D 15 E

15:45 - 16:00 21 D 15 E

16:00 - 16:15 21 D 15 E

16:15 - 16:30 21 D 15 E

16:30 - 16:45 21 D 15 E

16:45 - 17:00 21 D 15 E

17:00 - 17:15 20 D 15 E

17:15 - 17:30 20 D 15 E

17:30 - 17:45 20 D 16 E

17:45 - 18:00 20 D 16 E

Page 185


(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

18:00 - 18:15 20 D 16 E

18:15 - 18:30 20 D 16 E

18:30 - 18:45 20 D 17 E

18:45 - 19:00 20 D 17 E

SW 97th Ave

S

AM

07:00 - 07:15 20 D 20 D

07:15 - 07:30 20 D 20 D

07:30 - 07:45 20 D 20 D

07:45 - 08:00 20 D 19 D

08:00 - 08:15 20 D 19 D

08:15 - 08:30 20 D 19 D

08:30 - 08:45 20 D 18 D

08:45 - 09:00 20 D 18 D

PM

15:30 - 15:45 18 D 16 E

15:45 - 16:00 17 D 16 E

16:00 - 16:15 17 D 16 E

16:15 - 16:30 17 D 16 E

16:30 - 16:45 17 D 16 E

16:45 - 17:00 17 E 16 E

17:00 - 17:15 17 E 16 E

17:15 - 17:30 16 E 15 E

17:30 - 17:45 16 E 15 E

17:45 - 18:00 16 E 15 E

18:00 - 18:15 16 E 15 E

18:15 - 18:30 16 E 15 E

18:30 - 18:45 15 E 15 E

18:45 - 19:00 15 E 16 E

N

AM

07:00 - 07:15 19 D 18 D

07:15 - 07:30 19 D 18 D

07:30 - 07:45 19 D 17 D

07:45 - 08:00 19 D 17 E

08:00 - 08:15 18 D 16 E

08:15 - 08:30 18 D 15 E

08:30 - 08:45 18 D 15 E

08:45 - 09:00 19 D 15 E

PM

15:30 - 15:45 18 D 16 E

15:45 - 16:00 18 D 16 E

16:00 - 16:15 18 D 16 E

16:15 - 16:30 18 D 16 E

16:30 - 16:45 18 D 16 E

16:45 - 17:00 18 D 16 E

17:00 - 17:15 18 D 16 E

17:15 - 17:30 18 D 16 E

Page 186


(MPH)

After-I

LOS

Before-I

Speed (MPH)

Before-I

LOS

17:30 - 17:45 18 D 16 E

17:45 - 18:00 18 D 16 E

18:00 - 18:15 18 D 16 E

18:15 - 18:30 18 D 16 E

18:30 - 18:45 18 D 16 E

18:45 - 19:00 18 D 16 E

Page 187

Table 31: LOS by 15-Minute Interval during Peak Periods for Cross streets (Phase II)

Street, Direction and Period Time Interval After-II

Speed (MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

SW 87th Ave

S

AM

07:00 - 07:15 25 C 24 C 07:15 - 07:30 23 C 23 C 07:30 - 07:45 22 C 23 C 07:45 - 08:00 22 D 23 C 08:00 - 08:15 22 D 23 C 08:15 - 08:30 21 D 22 C 08:30 - 08:45 21 D 22 D 08:45 - 09:00 20 D 21 D

PM

15:30 - 15:45 17 E 16 E 15:45 - 16:00 17 E 16 E 16:00 - 16:15 17 D 16 E 16:15 - 16:30 17 D 16 E 16:30 - 16:45 17 D 15 E 16:45 - 17:00 17 E 14 E 17:00 - 17:15 17 E 14 E 17:15 - 17:30 16 E 14 E 17:30 - 17:45 16 E 13 E 17:45 - 18:00 16 E 13 F 18:00 - 18:15 16 E 13 F 18:15 - 18:30 16 E 13 F 18:30 - 18:45 16 E 12 F 18:45 - 19:00 17 E 13 F

N

AM

07:00 - 07:15 22 C 22 D 07:15 - 07:30 21 D 20 D 07:30 - 07:45 20 D 19 D 07:45 - 08:00 19 D 19 D 08:00 - 08:15 19 D 18 D 08:15 - 08:30 19 D 18 D 08:30 - 08:45 18 D 17 D 08:45 - 09:00 18 D 17 E

PM

15:30 - 15:45 19 D 16 E 15:45 - 16:00 19 D 15 E 16:00 - 16:15 18 D 15 E 16:15 - 16:30 18 D 16 E 16:30 - 16:45 18 D 16 E 16:45 - 17:00 18 D 17 E 17:00 - 17:15 18 D 17 E 17:15 - 17:30 18 D 16 E 17:30 - 17:45 18 D 16 E 17:45 - 18:00 18 D 16 E 18:00 - 18:15 18 D 16 E 18:15 - 18:30 18 D 16 E 18:30 - 18:45 18 D 16 E 18:45 - 19:00 19 D 17 E

SW 97th Ave S AM 07:00 - 07:15 26 C 23 C 07:15 - 07:30 24 C 22 C 07:30 - 07:45 22 C 21 D

Page 188

Street, Direction and Period Time Interval After-II

Speed (MPH)

After-II

LOS

Before-II

Speed (MPH)

Before-II

LOS

07:45 - 08:00 21 D 21 D 08:00 - 08:15 20 D 20 D 08:15 - 08:30 19 D 20 D 08:30 - 08:45 19 D 20 D 08:45 - 09:00 18 D 20 D

PM

15:30 - 15:45 16 E 17 D 15:45 - 16:00 16 E 17 D 16:00 - 16:15 16 E 17 D 16:15 - 16:30 16 E 17 D 16:30 - 16:45 16 E 17 E 16:45 - 17:00 16 E 16 E 17:00 - 17:15 16 E 15 E 17:15 - 17:30 16 E 15 E 17:30 - 17:45 16 E 14 E 17:45 - 18:00 16 E 14 E 18:00 - 18:15 16 E 14 E 18:15 - 18:30 16 E 14 E 18:30 - 18:45 16 E 14 E 18:45 - 19:00 16 E 14 E

N

AM

07:00 - 07:15 23 C 21 D 07:15 - 07:30 21 D 20 D 07:30 - 07:45 20 D 18 D 07:45 - 08:00 18 D 17 E 08:00 - 08:15 17 E 16 E 08:15 - 08:30 16 E 15 E 08:30 - 08:45 15 E 15 E 08:45 - 09:00 15 E 14 E

PM

15:30 - 15:45 16 E 15 E 15:45 - 16:00 16 E 14 E 16:00 - 16:15 17 E 15 E 16:15 - 16:30 17 E 15 E 16:30 - 16:45 17 E 16 E 16:45 - 17:00 17 D 16 E 17:00 - 17:15 17 D 16 E 17:15 - 17:30 17 D 16 E 17:30 - 17:45 17 D 15 E 17:45 - 18:00 17 E 15 E 18:00 - 18:15 17 E 15 E 18:15 - 18:30 17 E 16 E 18:30 - 18:45 17 D 16 E 18:45 - 19:00 17 D 16 E

Page 189

APPENDIX F. DETAILED AVERAGE HOURLY VOLUME RESULTS

Figure 110: Average Hourly Volume between SW 67 Ave and SW 72 Ave

Figure 111: Average Hourly Volume between SR 826 SB and SW 87 Ave

0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

0

500

1000

1500

2000

2500

3000

3500

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

Page 190



0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

Page 191


Figure 115: Average Hourly Volume between Turnpike SB and SW 127 Ave

0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

Page 192


0

500

1000

1500

2000

2500

3000

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

07

:00

-08

:00

08

:00

-09

:00

16

:00

-17

:00

17

:00

-18

:00

EB WB

Ave

rage

Ho

url

y V

olu

me

(V

eh

pe

r H

ou

r)

After-I Before-I

Page 193

APPENDIX G. DETAILED TRAVEL TIME INDEX RESULTS (Wi-Fi DATA)

Phase I

Phase II

Figure 117: TTIs between SW 67 Ave and SW 72 Ave

Phase I

Phase II

Figure 118: TTIs between SW 72 Ave and SR 826 NB Ramp

0

2

4

6

8

10

12

14TT

I




0123456789

10

TTI




0

2

4

6

8

10

12

14

TTI




0

2

4

6

8

10

12

14

TTI




Page 194

Phase I

Phase II

Figure 119: TTIs between SR 826 NB Ramp and SR 826 SB Ramp

Phase I

Phase II

Figure 120: TTIs between SR 826 SB Ramp and SW 82 Ave

0123456789

TTI




0

1

2

3

4

5

6

TTI




0

2

4

6

8

10

12

14

16

TTI




0

2

4

6

8

10

12

14

TTI




Page 195

Phase I

Phase II


Phase I

Phase II


0

5

10

15

20

TTI




0

2

4

6

8

10

12

TTI




0

1

2

3

4

5

6

7

8

TTI




0

1

2

3

4

5

TTI




Page 196

Phase I

Phase II


Phase I

Phase II


0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




0

0.5

1

1.5

2

2.5

3

TTI




Page 197

Phase I

Phase II


Phase I

Phase II


0

2

4

6

8

10

12

14

TTI




0

1

2

3

4

5

6

TTI




0

2

4

6

8

10

TTI




0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




Page 198

Phase I

Phase II

Figure 127: TTIs between SW 109 Ave and SW Turnpike NB Ramp

Phase I

Phase II

Figure 128: TTIs between Turnpike NB Ramp and Turnpike SB Ramp

0

1

2

3

4

5

TTI




0

0.5

1

1.5

2

2.5

3

3.5

TTI




0

0.5

1

1.5

2

2.5

3

3.5

TTI




0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




Page 199

Phase I

Phase II

Figure 129: TTIs between Turnpike SB Ramp and SW 122 Ave

Phase I

Phase II


0

1

2

3

4

5

6

7

8

TTI




0

0.5

1

1.5

2

2.5

3

3.5

4

TTI




0

1

2

3

4

5

6

7

8

TTI




0

1

2

3

4

5

6

TTI




Page 200

Phase I

Phase II


Phase I

Phase II


00.5

11.5

22.5

33.5

44.5

TTI




0

0.5

1

1.5

2

2.5

3

3.5

TTI




0

2

4

6

8

10

TTI




0123456789

TTI




Page 201

Phase I

Phase II


0

2

4

6

8

10

TTI




0

1

2

3

4

5

6

TTI




Page 202

APPENDIX H. DETAILED CRASH RATE RESULTS BY CRASH TYPES

Figure 134: Segment I Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 203

Figure 135: Segment I Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 204

Figure 136: Segment II Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Sideswipe Rear End Sideswipe Rear End Left Turn Rear End Left Turn Sideswipe Other Rear End

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 205

Figure 137: Segment II Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

0

20

40

60

80

100

120

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

Un

kno

wn

Rea

r En

d

Off

Ro

ad

Left

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Sid

esw

ipe

Rea

r En

d

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Off

Ro

ad

Un

kno

wn

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 206

Figure 138: Segment III Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

An

imal

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

An

imal

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 207

Figure 139: Segment III Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Un

kno

wn

Rea

r En

d

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(T

ota

l)

Total Hourly

Page 208

Figure 140: Segment IV Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Off

Ro

ad

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 209

Figure 141: Segment IV Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

An

imal

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 210

Figure 142: Segment ALL Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

An

imal

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

An

imal

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 211

Figure 143: Segment ALL Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Rig

ht

Tu

rnSi

des

wip

e

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rnP

edes

tria

n

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Un

kno

wn

Rea

r En

dO

ff R

oad

Hea

d O

n

Left

Tu

rnP

edes

tria

n

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

erR

ollo

ver

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rnP

edes

tria

n

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

An

imal

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 212

Figure 144: SW 87th Ave Crash Rate by Types (After)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 213

Figure 145: SW 87th Ave Crash Rate by Types (Before)

0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Ro

llove

r

Rea

r En

d

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 214


0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Sid

esw

ipe

Rea

r En

d

Un

kno

wn

Left

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Sid

esw

ipe

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 215


0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 216


0

1

2

3

4

5

6

7

8

0

20

40

60

80

100

120Si

des

wip

e

An

gle

Un

kno

wn

Rea

r En

d

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Ped

estr

ian

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 217


0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Left

Tu

rn

Sid

esw

ipe

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Ped

estr

ian

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Bic

ycle

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 218


0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

An

gle

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Ro

llove

r

Oth

er

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Rea

r En

d

Un

kno

wn

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Page 219


0

1

2

3

4

5

6

7

8

9

10

0

20

40

60

80

100

120Le

ft T

urn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Left

Tu

rn

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Un

kno

wn

Off

Ro

ad

Hea

d O

n

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

Oth

er

Ro

llove

r

An

gle

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Left

Tu

rn

Ped

estr

ian

Rig

ht

Tu

rn

Sid

esw

ipe

An

gle

Oth

er

Ro

llove

r

Rea

r En

d

Off

Ro

ad

Un

kno

wn

Hea

d O

n

AM MD PM Daily

Cra

she

s p

er

Ye

ar p

er

Mile

(H

ou

rly)

Cra

she

s p

er

Ye

ar p

er

Mile

(To

tal)

Total Hourly

Date post:	28-Mar-2021
Category:	Documents
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