Spatial Modeling for Highway Performance Monitoring System Data: Part 2

Tuesday, March 6, 20181:00-3:00 PM ET

TRANSPORTATION RESEARCH BOARD

The Transportation Research Board has met the standards and

requirements of the Registered Continuing Education Providers Program.

Credit earned on completion of this program will be reported to RCEP. A

certificate of completion will be issued to participants that have registered

and attended the entire session. As such, it does not include content that

may be deemed or construed to be an approval or endorsement by RCEP.

Purpose Discuss how to use spatial modeling and statistical tools to enhance the quality and productivity of travel monitoring data.

Learning ObjectivesAt the end of this webinar, you will be able to:

• Describe the HPMS travel data validation rules and create pseudo-code queries that incorporate HPMS based validation rules

• Understand how to apply standard visualizing tools for the purpose of highlighting and identification of potential problems

• Understand the need to balance multimodal solutions within the system

• Understand how to create HPMS data items and summary data using a relational database

TRB WEBINAR:SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA PART 2 - MARCH 6, 2018

Maaza Christos Mekuria, PhD, PE, PTOEDan P. Seedah, PhDStephen Cropley 1

WEBINAR OUTLINE

Spatio-temporal Relationships

Spatial Clustering

Applications to HPMS Data Items

Generation of Summary Tables

Ramp Balancing

Opportunities and challenges for Probe data

Q & A session

2

RECAP FROM PART 1 WEBINAR

Overview of HPMS

Description of HPMS Travel Data Items

Relationship between HPMS Data items

Linear Referencing System

Sample Panels and HPMS items

Leveraging Spatial Analysis

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SPATIAL RELATIONSHIPS

Dimensionality

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Connectivity

B

A

Adjacency Containment

TEMPORAL RELATIONSHIPS

Time of Day

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Day of Week Month Year

HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS)

[Ref. FHWA]

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Sample Panels (Sections/Links/Segments)

Assign Counts to Monitored Sections

Expand to Unmonitored Sections

Generate Summaries

HPMS SAMPLE PANEL GENERATION

[Adapted from FHWA - CPI Manual 2001]

Sample Panels

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FHWA HPMS Tool

ArcGIS Spatial

Oracle PL/SQL

QGIS/Python

Spatialite/Python/C++

etc.

CASE WHEN "aadt"/"thrulanes" <= 2500 THEN 'Very Low'WHEN "aadt"/"thrulanes" <= 5000 THEN'Low' WHEN "aadt"/"thrulanes" <= 10000 THEN'Moderate'WHEN "aadt"/"thrulanes" <= 20000 THEN'High'WHEN "aadt"/"thrulanes" <= 50000 THEN 'Very High'WHEN "aadt"/"thrulanes" > 50000 THEN 'Super High'

END

TOPS GROUP SECTIONS SAMPLE CODES8

TOPS GROUP SECTIONS9

QUERY Group all roadways by functional class and order by number of records in each county

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Very Low Low Moderate High Very High Super High

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Volume Group Per Lane

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TOPS GROUP SECTIONS

TOPS GROUP SECTIONS11

FC_FacilityType_UrbanCode_Low_SampleCode

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CASE WHEN "aadt" <= 500 THEN 1WHEN "aadt" <= 1000 THEN 2WHEN "aadt" <= 2500 THEN 3WHEN "aadt" <= 5000 THEN 4WHEN "aadt" <= 10000 THEN 5WHEN "aadt" <= 17500 THEN 6WHEN "aadt" <= 27500 THEN 7WHEN "aadt" <= 42500 THEN 8WHEN "aadt" <= 62500 THEN 9WHEN "aadt" <= 87500 THEN 10WHEN "aadt" <= 125000 THEN 11WHEN "aadt" > 125000 THEN 12

END

HPMS VOLUME GROUPS

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Number of HPMS Volume Group Segments

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HPMS VOLUME GROUPS

HPMS VOLUME GROUPS ESTIMATION FORMUAR

[Ref. FHWA Field manual 2016]

Confidence Level Z Z Squared

90 Percent 1.645 2.706

80 Percent 1.282 1.644

70 Percent 1.04 1.082

Table 6.2

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HPMS TOPS CLUSTERS

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Number of TOPS Segment Groups

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HPMS LINK TRAVEL DATA ASSIGNMENT

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COUNTED HPMS SECTIONS FOR A GIVEN YEAR24/7 continuous counters Short Term Counters

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?

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? ?

? No Counters

Section Boundaries

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2016 HPMS FIELD MANUAL GUIDANCE - VOLUME DATA

For AADT (Pages 4-51 and 4-52)

For two-way facilities, provide the bidirectional AADT; for one-way roadways, and ramps, provide the directional AADT.

All AADTs shall reflect application of day of week, seasonal, and axle correction factors, as necessary; no other adjustment factors shall be used.

Growth factors shall be applied if the AADT is not derived from current year counts.

AADTs for the NHS, Interstate, Principal Arterial (OFE, OPA) roadway sections shall be based on traffic counts taken on a minimum three-year cycle.

AADTs for the non-Principal Arterial System (i.e., Minor Arterials, Major Collectors, and Urban Minor Collectors) can be based on a minimum six-year counting cycle.

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TRAVEL MONITORING : AADT LINK ASSIGNMENT19

Data Attr./Spatial Merge

Sample Panels

VolumeStations

-- Year

NoAre all Sections

Assigned?

Earlier year

meets FC criterion?

Yes

Yes

END

No

Section to be revised for HPMS Submission

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HPMS TRAFFIC STATIONS ASSIGNMENT RESULTS

AADT COUNT ASSIGNMENT - MAUI21

AADT COUNT ASSIGNMENT - KAUAI22

AADT COUNT ASSIGNMENT - KAUAI23

AADT COUNT ASSIGNMENT - KAUAI24

2016 HPMS FIELD MANUAL GUIDANCE – CLASS DATA

For Single Unit and Combination Unit AADT (Pages 4-52 to 4-57)

AADT values shall be updated annually to represent current year data.

Section specific measured values are requested based on traffic counts taken on a minimum three-year cycle.

If these data are not available, values derived from classification station data on the same route, or on a similar route with similar traffic characteristics in the same area can be used

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TRAVEL MONITORING : CLASS LINK ASSIGNMENT26

Find the adjacent section on same route with similar FC, thru

lanes, and urban code, and assign class data*

* Preference is given to permanent stations over short-

term stations

AADT CLASS COUNT ASSIGNMENT27

CONSIDERATIONS FOR APPLYING GROWTH RATES

Growth rates are applied on a statewide level

Further investigation required on applying growth rates based on:

Urban area codes

Rate computed from just the permanent stations

Considerations for:

Low volume rural roadways

Large area vs. small area

Sections that traverse multiple boundaries e.g. interstates

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CONSIDERATIONS FOR USING JOINS Attribute Joins

Attribute joins can use MILEPOINT and ROUTE information with respect to Section start and end mile points.

More accurate, if and only if, data attributes are correctly set in both the Station and Link records.

Spatial Joins

Point is typically matched to a target line which is closest/intersects/contains the point

Uses a search radius or tolerance

Issues include wrongly assigned joins e.g. ramp counts with coordinates placed close to the interstate

Best compromise is a spatial join with attribute join

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The result of the processing is HPMS Network with AADT, Class, Peak Hour Count Data assigned to links

AADT CLASS COUNT ASSIGNMENT

AADT ASSIGNMENT COMPARISON31

AADT ASSIGNMENT COMPARISON32

TRUCK AADT COMPUTATION33

TRUCK AADT for each section

Using the Permanent Stations Adjust Short Term Class Stations

Truck Single Unit = (Daily Single Unit Trucks (Class 5-7) + Bus )

Percent Peak Combination = (Daily Combination Unit Trucks , FHWA class 8-13 )

AADT K, D FACTOR ASSIGNMENT

Both K-factor ( the design hour volume - 30 th largest hourly volume for a given calendar year) as a percentage of AADT) and the Directional Factor (D) ( the percent of design hour volume - 30th largest hourly volume for a given calendar year flowing in the higher volume direction) are assigned to AADT links via permanent station factor groups.

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VALIDATION FOR TRAFFIC DATA ITEMS

FHWA Validation Queries are heavily dependent on user table structure, yet they are as simple as a single query once the table data are generated.

Samples will be provided when requested.

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PEAK HOUR ASSIGNMENT36

Peak Hour AADT for each section

Using the Permanent Stations Adjust Short Term Class Stations

Percent Peak Single = (Peak Hour Daily Single Unit Trucks + Bus ) / (AADT )

Percent Peak Combination = (Peak Hour Daily Combination Unit Trucks ) / (AADT )

VEHICLE SUMMARY37

create view vwHPMS2016AvgVehicleClass asSelect Year ,t2.State_Code, t2.FS_GROUP , Round(avg(t1.cycles),2) Pct_MC, round(avg(t1.PC),2) Pct_Cars, round(avg(t1.VCLS3),2) Pct_Lgt_Trucks, round(avg(t1.Bus),2) Pct_Buses,round(avg(t1.SU),2) Pct_SU_Trucks, round(avg(t1.CU),2) Pct_CU_Trucks

from HPMS2016VEHICLECLASS t1 , FHWA_HIGHWAY_Class_Group t2

where t1.Func_Class = t2.Func_Class group by t1.Year ,t2.State_Code, t2.FS_GROUP Order by t2.Year , t2.State_Code, t2. FS_GROUP ;

Step 1 - Create Average View of the AADT Link Class data table by FHWA Highway System Group

VEHICLE SUMMARY38

create view vwHPMS2016SumVehicleClass ast2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC+ t2.Pct_Cars + t2.Pct_Lgt_Trucks+ t2.Pct_Buses + t2.Pct_SU_Trucks + t2.Pct_CU_Trucks),2) TotPct

from vwHPMS2016AvgVehicleClass t2Order by t2.year_Record ,t2.State_Code,t2.Fs_Group;

Step 2 - Create Summary View of the AADT Link Class data by FHWA Highway System Group

VEHICLE SUMMARY39

select t2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars,

Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses,

Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks , Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks

from vwHPMS2016AvgVehicleClass t2 inner join vwHPMS2016SumVehicleClass t1 on t1.FS_GROUP = t2.FS_GROUP

Order by t2.year_record , t2.State_Code, t2.FS_Group;

Step 3 - Create Export for FHWA by FHWA Highway System Group

VEHICLE SUMMARY TO BE EXPORTED TO FHWA40

VEHICLE SUMMARY – VMT BASED41

create view vwHPMS2016VehicleClassVMTSUM asselect t2.Year_Record, '15' State_Code, t2.FS_GROUP , Round(sum(t1.cycles*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_MC,

round(sum(t1.PC*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_Cars, Round(sum(t1.VCLS3*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_Lgt_Trucks,

round(sum(t1.Bus*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_Buses, round(sum((t1.SU)*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_SU_Trucks ,

round(sum(t1.CU*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_CU_Trucks

from HPMS2016VEHICLECLASS t1 , HPMS2016AADT t2, FHWAClass t3where t2.Route = t1.Route_id and t2.BMP = t1.BMP and

t2.EMP = t1.EMP and t2.FS_GROUP = t3. FS_GROUPgroup by t2.year_record , t3. FS_GROUP Order by t2.year_record , t3. FS_GROUP ;

Step 1 - Create Summary View of the Link VMT Class data by FHWA Highway System

VEHICLE SUMMARY – VMT BASED42

create view vwHPMS2016VMTSum asselect t2.Year_Record, '15' State_Code, t3. FS_GROUP , Round(sum((t2.EMP-t2.BMP)*t2.AADT),2) VMT

from HPMS2016AADT t2, FHWAClass t3

where t2.FS_GROUP = t3. FS_GROUPgroup by t2.year_record , t3. FS_GROUP Order by t2.year_record , t3. FS_GROUP;

Step 2 - Create Summary View of the Total VMT Class data

VEHICLE SUMMARY – VMT BASED43

create view vwHPMS2016VMTVehicleClassSum asselect t2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC/t1.VMT),2) Pct_MC, Round((t2.Pct_Cars/t1.VMT),2) Pct_Cars,

Round((t2.Pct_Lgt_Trucks/t1.VMT),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.VMT),2) Pct_Buses,Round((t2.Pct_SU_Trucks/t1.VMT),2) Pct_SU_Trucks , Round((t2.Pct_CU_Trucks/t1.VMT),2)

Pct_CU_Trucksfrom vwHPMS2016VehicleClassVMTSUM t2 inner join vwHPMS2016VMTSum t1 on

t1.FS_GROUP = t2.FS_GROUP Order by t2.year_record , t2.State_Code, t2.FS_Group ;

Step 3 – Divide Link VMT (Step 1) by VMT Totals (Step 2)

VEHICLE SUMMARY – VMT BASED44

create view vwHPMS2016SumVehicleClass ast2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC+ t2.Pct_Cars + t2.Pct_Lgt_Trucks+ t2.Pct_Buses + t2.Pct_SU_Trucks + t2.Pct_CU_Trucks),2) TotPct

from vwHPMS2016VMTVehicleClassSum t2Order by t2.year_Record ,t2.State_Code,t2.Fs_Group;

Step 4 - Create Total view of the AADT Link Class data by FHWA Highway System Group from Step 3

VEHICLE SUMMARY – VMT BASED45

select t2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars,

Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses,

Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks , Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks

from vwHPMS2016AvgVehicleClass t2 inner join vwHPMS2016SumVehicleClass t1 on t1.FS_GROUP = t2.FS_GROUP

Order by t2.year_record , t2.State_Code, t2.FS_Group ;

Step 5 - Create Export for FHWA – Step 3 / Step 4

VEHICLE SUMMARY – VMT BASED46

select t2.Year_Record, t2.State_Code, t2.FS_GROUP , Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars,

Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses,

Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks , Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks

from vwHPMS2016AvgVehicleClass t2 inner join vwHPMS2016SumVehicleClass t1 on t1.FS_GROUP = t2.FS_GROUP

Order by t2.year_record , t2.State_Code, t2.FS_Group ;

Step 5 - Create Export for FHWA – Step 3 / Step 4

VEHICLE SUMMARY TO BE EXPORTED TO FHWA47

Vehicle Summaries Simple AverageYear_Record State_Code FS_GROUP Pct_MC Pct_Cars Pct_Lgt_Trucks Pct_Buses Pct_SU_Trucks Pct_CU_Trucks TotPct

2016 15 110 1.89 70.16 22.47 0.99 3.62 0.87 1002016 15 200 0.82 69.22 25.22 1.07 2.86 0.81 1002016 15 210 2.68 72.41 19.46 1.39 2.78 1.28 1002016 15 300 1.23 67.03 28.16 0.52 2.70 0.36 1002016 15 310 2.17 71.65 23.15 0.69 1.82 0.52 100

VMT BasedYear_Record State_Code FS_GROUP Pct_MC Pct_Cars Pct_Lgt_Trucks Pct_Buses Pct_SU_Trucks Pct_CU_Trucks TotPct

2016 15 110 1.36 66.83 24.80 1.37 4.82 0.82 1002016 15 200 0.97 68.49 25.74 1.09 2.81 0.90 1002016 15 210 3.05 71.23 20.14 1.66 2.68 1.24 1002016 15 300 0.87 69.97 27.11 0.34 1.46 0.25 1002016 15 310 2.27 71.66 23.41 0.65 1.50 0.51 100

CLUSTER PROCESSING

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HPMS SAMPLE PANEL CLUSTER SIZE = 5049

HPMS SAMPLE PANEL CLUSTER SIZE = 2550

HPMS COUNT STATION SELECTION 51

RAMP BALANCING PROCEDURES

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PURPOSE OF RAMP BALANCING Need to report traffic (AADT) on all interstate mainline sections.

Hard to count some sections, so “Ramp balancing can be used to estimate traffic volumes on roads where portable counts cannot be performed safely” (TMG 2017)

Its called “ramp balancing” because AADT values are adjusted so they “balance” along the computed sections.

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DIFFERENCE BETWEEN INTERCHANGE AND MAINLINE “RAMP BALANCING”

This needs to be spelled out!

WE ARE DISCUSSING THIS !!

Mainline Ramp BalancingInterchange Ramp Balancing (Computes ramp volumes at

interchanges)

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A TYPICAL SETUP

Anchor Points (Continuous, Known)Ramp On (Known)Ramp Off (Known)Computed Mainline

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SETTING UP FROM SPATIAL ANALYSIS

Continuous counters are called “Anchor points”. They are not changed by the computation.

Determine which ramps are part of a given computation.

Some ramps are actually connectors. They areOn for computing one highway segment butOff for another.

The order in which On and Off ramps appear in the computation is essential. Use mile points and look closely at the map.

The compass directions (N, S, E, W) that ramps flow towards are essential in computation

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EXAMPLE: COMPUTING VOLUMES

Point Counted COMPUTED CHANGE

CCS 1 START

11,995

Ramp 1 ON

923 12,918 -134

MID POINT

Ramp2OFF

(-)1,053 11,865 -153

Ramp3 ON

786 12,651 -115

CCS2 END

13,053

DIFF -402

57

* The Ramp Off below ramp1 is ignored in this example for simplicity. It would normally be included.

EXAMPLE: COMPUTING VOLUMES

Point Counted Adjusted GEHAccuracy

CCS 1 START

11,995

Ramp 1 ON

923 1,057 1.35

MID POINT 13,052Ramp2

OFF(-)1,053 900 1.55

Ramp3 ON

786 901 1.25

CCS2 END

13,053GEH Statistic

1.0 Excellent

2.0 Good

5.0 Acceptable

10.0 Rubbish

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CONSIDERATIONS FROM SPATIAL ANALYSIS

- Direction of traffic

- Order of ramps

- Ramp counts may have happened on different days

- “Missing” ramp values must be imputed: They are not calculated here

2017 Highway Computation PointsGeorgia DOT

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RESOURCES USED IN EXAMPLES

Source: http://geocounts.com/visual/rampbalancing(Example 4 is from the 2001 TMG)

TMG 6.2.2 AADT Reporting on Mainlines and Ramps

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OPPORTUNITIES AND CHALLENGES FOR PROBE DATA

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TRAVEL MONITORING: THE IDEAL ENVIRONMENT62

TRAVEL MONITORING: THE IDEAL ENVIRONMENT24/7 continuous counters

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TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT

Autonomous Self-Reporting Vehicles

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TRAVEL MONITORING: PROBE DATA65

PROBE DATA OPPORTUNITIES

Cost

Coverage

Frequency

Origin/Destination

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PROBE DATA CHALLENGES

Cell tower vs. GPS vs. Location-Based Data

Sample sizes for different functional classes

Urban vs. rural settings

“Black box” vs. open estimation algorithms

HPMS six vehicle class data requirement

Validation

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REFERENCES

HPMS 2016 Field Manual

Turner, Shawn, and Pete Koeneman. Using Mobile Device Samples to Estimate Traffic Volumes. No. MN/RC 2017-49. Minnesota. Dept. of Transportation. Research Services & Library, 2017.

I-95 Corridor Coalition’s Vehicle Probe Project (VPP)

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CONTACT INFORMATION

Maaza Christos Mekuria, PhD, PE, PTOEHawaii Department of Transportationmaaza.c.mekuria@hawaii.gov

Dan P. Seedah, PhDAsst. Research Scientist, Texas A&M Transportation Instituted-seedah@tti.tamu.edu

Stephen CropleyDirector, TechnologyTransmetric America Inc.stephen.cropley@transmetric.com

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APPENDIX A : HPMS NUMBER OF SAMPLES SELECTION

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HPMS NUMBER OF SAMPLES SELECTION

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HPMS NUMBER OF SAMPLES SELECTIONSELECT t1.yr, t1.HPMSVGrp, Count(t1.countycode) NumRecs , Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.64,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.05,2)))/(1+(1/count(t1.Id))*(((pow(1.64,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.05,2))-1))))) NumSampFROM HiDOTRtesHPMS2016BIKEFLDSMDS t1GROUP BY t1.yr, t1.HPMSVGrpORDER BY t1.yr,t1.HPMSVGrp,stddev_samp(t1.AADT)/avg(t1.AADT) desc,Count(t1.countycode) Desc

Year HPMSVGrp NumRecs CV NumSamp2016 1 68 0.503 2732016 2 138 0.157 272016 3 1165 0.217 512016 4 928 0.185 372016 5 1837 0.183 372016 6 2249 0.156 272016 7 1328 0.141 222016 8 924 0.124 172016 9 397 0.112 142016 10 118 0.085 82016 11 89 0.073 62016 12 88 0.195 41

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HPMS NUMBER OF SAMPLES SELECTION RURAL AREASSELECT t1.yr,t1.HPMSVGrp, floor(avg(t1.funclass)) FunClsAvg, Count(t1.countycode) NumRecs , Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.28,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.1,2)))/(1+(1/count(t1.Id))*(((pow(1.28,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.1,2))-1))))) NumSampFROM HiDOTRtesHPMS2016BIKEFLDSMDS t1where t1.Urbcode = 99999GROUP BY t1.yr, t1.HPMSVGrpORDER BY t1.yr,t1.HPMSVGrp,stddev_samp(t1.AADT)/avg(t1.AADT) desc,Count(t1.countycode) Desc

yr HPMSVGrp FunClsAvg NumRecs CV NumSamp2016 1 5 42 0.508 432016 2 5 68 0.147 42016 3 5 666 0.184 62016 4 4 181 0.187 62016 5 3 284 0.172 52016 6 3 134 0.15 42016 7 3 88 0.108 22016 8 4 7 0 0

74HPMS NUMBER OF SAMPLES SELECTION RURAL + SMALL URBAN COLLECTOR AND ABOVESELECT t1.yr,t1.HPMSVGrp, floor(avg(t1.funclass)) FunClsAvg, Count(t1.countycode) NumRecs , Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.28,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.1,2)))/(1+(1/count(t1.Id))*(((pow(1.28,2) * pow(stddev_samp(t1.AADT)/avg(t1.AADT),2)/(pow(0.1,2))-1))))) NumSampFROM HiDOTRtesHPMS2016BIKEFLDSMDS t1where t1.Urbcode in ( 99999,99998) and t1.funclass >= 5GROUP BY t1.yr, t1.HPMSVGrpORDER BY t1.yr,t1.HPMSVGrp,stddev_samp(t1.AADT)/avg(t1.AADT) desc,Count(t1.countycode) Desc

Yr HPMSVGrp FunClsAvg NumRecs CV NumSamp2016 1 5 60 0.463 362016 2 5 100 0.103 22016 3 5 779 0.173 52016 4 5 367 0.186 62016 5 5 395 0.19 62016 6 5 238 0.12 32016 7 6 10 0.172 52016 8 7 4 0 0

Today’s Participants• Coco Briseno, California Department of

Transportation, coco.briseno@dot.ca.gov• Maaza Mekuria, Hawaii Department of

Transportation, maaza.c.mekuria@hawaii.gov• Daniel Seedah, Texas A&M Transportation

Institute, d-seedah@tti.tamu.edu• Stephen Cropley, Transmetric America, Inc.,

stephen.cropley@transmetric.com

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