Building Evidence for Active Travel: Counting and Modeling Non-motorized Traffic

Scope of Talk

• Motivation for research – Need for evidence (measures of demand)

• Counting non-motorized traffic • Modeling non-motorized traffic • Opportunities for collaborative research

Motivation for Research

Ray Irvin, Indy Parks Greenways (1995):

How many people are on our trails?

Reasons to Count Bikes and Peds

• Improve systems planning • Document use of facilities (Indianapolis) • Allocate resources (Indiana) • Assess efficiency of investments • Assess exposure & increase safety (MSP) • Optimize facility operations & maintenance • Test hypotheses and build theory

Multi-use Trails in Indianapolis

Min Max Mean

Week Days 79 2,017 436

Weekend Days 105 3,670 834

3,500

150

500

1,500

Understand Spatial & Temporal Variation in

Trail Traffic, Indianapolis, IN (9/04)

Annual Trail Traffic

• Maximum: 606,900 • Minimum : 21,700 • Mean: 146,438 • Median: 101,578

Mean Daily Count

Monon Trail

Monon Trail

CBD

Allocate Resources • Indiana 2004-2005 Trail Funding Program

– $10-11 million available – $73 million requested; 66 applications; – Only 20 applicants estimated use in proposal

• Mean predicted annual traffic 22% > trail segment in Indianapolis with highest traffic

Improve Traffic Safety: Minneapolis Midtown Greenway Street-Trail Crossings

Street (mid-block

crossing)

Street Average

Daily Traffic

Bicycle Daily

Traffic

Recommendations For Stop Sign

Trail

Recommendations For Stop Sign

Street

Local 1 420 3,280 Remove Add Local 2 2,026 3,280 Remove Add Local 3 2,400 3,280 Remove Add Local 4 1,680 2,900 Remove Add Yield Sign Minor Arterial

7,267 2,740 Keep Add Yellow Flasher, Reduce lanes 4 to 2

Minneapolis Dept. of Public Works , Feb. 15, 2010

Intergovernmental Involvement

in Monitoring Traffic Motor Vehicle Traffic

• FHWA sets standards in Traffic Monitoring Guide (TMG) & helps fund

• State DOTs implement • Locals collaborate

Bike & Ped Traffic • Little federal support;

FHWA TMG in 2012 • Few state DOTs count:

CO, WA, MN, VT • National Bike & Ped

Documentation Project • National Academies Bike

and Pedestrian Data Subcommittee

• Locals lead!

Traffic Monitoring in Minnesota

Motor Vehicle Traffic

• MnDOT: 32,000 locations – +1,000 reference sites with

automated continuous monitoring

– +31,000 sites with automated short duration (48 hour) monitoring once per five years

• Local governments supplement MnDOT counts

Bike & Ped Traffic • MnDOT: 0 locations

– Launched pilot project; will establish 6-10 sites in 2013

• Local governments and nonprofits: < 500 sites – Minneapolis DPW & Transit

for Livable Communities: 350-400 sites: 2 hour counts

– UMN: 6 sites; continuous automated counts

– Met Council, park districts

Street Bike Blvd

Sidewalk Multi-use Trail

Counting Method With Bike Lane

Without Bike Lane

Street Shoulder

Manual field observations

B MN*

B MN*

B MN

B MN

P, B MN*

B, P MN

Video with manual processing

B B B B P, B MN

B, P MN

Inductive Loop detectors

B

B

B

B B MN*

Active Infrared MM MN*

Passive infrared P, MM MN

MM MN

Pressure Pads P

P, B?

Pneumatic tubes B

B B B B

Non-motorized Traffic Counting in Minnesota

Working with Counts • Objective is to understand patterns • Variation by mode

– bike vs. pedestrian (mixed mode) • Temporal variability

– Seasonality (monthly variation); day of week; time of day (peak hour)

• Spatial variability – Facility and location

Examples of Counting & Modeling

• Continuous counts: daily traffic model – Multi-use trail traffic (shared path)

• Indianapolis (mixed mode) • Minneapolis (mixed mode, bicycle)

• Short duration (2 hour manual counts): 12-hour traffic model – Street and sidewalk traffic

• Minneapolis (bicycle, pedestrian)

General Approach

Estimating Annual Traffic 1. Establish continuous traffic

monitoring sites 2. Implement quality assurance/control 3. Impute missing daily counts* 4. Estimate annual average daily

cyclists, pedestrians 5. Estimate adjustment factors (day of

week, monthly) 6. Conduct short duration counts (e.g.,

48 hours) at sample sites 7. Extrapolate short duration counts to

average annual traffic using adjustment factors from reference sites

Modeling Daily Traffic 1. Establish sample locations for

continuous or short duration counts 2. Implement quality assurance/control

(Censor partial or inaccurate counts)

3. Create databases of correlates of traffic (socio-demographic, urban form, weather, other variables)

4. Estimate traffic models (regression) 5. Evaluate quality of models (prediction,

validation)

* Models can be used to estimate daily traffic for days when counts missing.

Minneapolis (n=6; mixed mode, bike, ped) Indianapolis (n=30; mixed mode)

Trail Networks and Monitoring Locations

A Calibration Problem: Loop Detector Counts (bikes) > Infrared Counts (bikes & peds)

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Date

Hennepin Ave. Counter Site (Dec 2009 & Jan 2010)

loop detector

infrared

Quality Assurance / Control

Active Infrared Counters: Mixed Mode

Inductive Loop Counters: Bicycles

Hourly adjustment equation: (same across locations)

Hourly adjustment equation: (varies by location)

y=0.7018x

y=0.0002x2+1.0655x-1.2937

y=1.0328x

Temporal and Spatial Variation in Indianapolis Trail Traffic (mixed mode)

Daily Traffic vs Weekday / Weekend Traffic - 2004, Monon 67th St.

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Counts

Daily Traffic Weekday Traffic Weekend Traffic

Max:6155Min: 52

Average Daily Traffic by Day of Week, Monon 67th St.

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2001200220032004

Monthly Total Traffic at 4 Locations on Monon, 2004

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67th

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Mean Hourly Traffic on Weekday & Weekend, Sep 2004 67th on Monon & Mich on White River

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Temporal and Spatial Variation in Minneapolis Trail Traffic

Mixed mode: monthly mean daily traffic Mixed mode: monthly/annual daily traffic

Mixed mode Bikes

Peds

Bicycle: day of week / annual daily traffic

Lake trails

Greenway trails

Weekday and Weekend Time of Day Variation in Trail Traffic

Midtown-Hennepin Lake Calhoun Trail

Weekdays

Weekends

Mixed Mode Annual Trail Traffic

Indianapolis (2004)

Minneapolis (2011)

Monitoring sites 30* 6** Maximum traffic 606,906 1,308,643

Mean traffic 146,438 626,050 Minimum traffic 21,737 116,765 *Monitoring sites covered most of trail network at time. **Monitoring sites cover only 6 of 50 or more segments.

Location / Mode Estimated Total Annual Traffic

Estimated AADT

Percent of Traffic at

Site

Estimated Miles Traveled

(1) Hennepin Ave. & Midtown Greenway (1.7 m)

a. Bicycle 629,262 1,724 87% 1,069,745 b. Pedestrian 91,451 251 13% 155,467 c. Total – mixed-mode 720,714 1,975 100% 1,225,214

(2) West River Pkwy & MGW (1.5 m) a. Bicycle 320,198 877 96% 480,297 b. Pedestrian 13,196 36 4% 19,794 c. Total – mixed-mode 333,395 913 100% 500,093

(3) Cedar Ave. & MGW (2.3 m) a. Total – mixed-mode 738,336 2,023 100% 1,698,173

(4) Lake Calhoun Parkway* (1.2 m) a. Bicycle (outer) 494,209 1,354 38% 593,051 b. Pedestrian (inner) 814,434 2,231 62% 977,321 c. Total – mixed-mode 1,308,643 3,613 100% 1,570,372

(5) Lake Nokomis Parkway* (1.9 m) a. Bicycle (outer) 193,843 531 36% 368,302 b. Pedestrian (inner) 344,604 944 64% 654,748 c. Total – mixed-mode 538,448 1,475 100% 1,023,051

(6) Wirth Parkway – mixed-mode (1.5 m) 116,765 320 100% 175,148

Average Annual Daily Bicycle & Pedestrian Traffic

Factors that Affect Trail Traffic (similar approaches in Indianapolis, Minneapolis)

• Weather (daily, seasonal) • Day of week • Neighborhood socio-demographics • Built environment & urban form

– Neighborhood characteristics – Trail characteristics

Negative Binomial Models of Mixed Mode Trail Traffic Variables 1-General

Model

n=1898

2-Six-location Model

n=1898

Trail-specific Models 3-8

3-Hennepin n=427

4-WRP n=405

5-Cedar n=272

6-Calhoun n=269

7-Nokomis n=261

8 Wirth n=264

Pseudo-R2 0.1329 0.1329 0.1162 0.1283 0.1111 0.0986 0.1197 0.1596 (Constant) 1.297*** 4.331*** 6.221*** 5.397*** 6.448*** 6.611*** 6.029*** 4.166*** Social Demographic Characteristics blkpct 0.154*** - - - - - - - collegepct 0.032*** - - - - - - - yngoldpct -0.412*** - - - - - - - medincthd 0.144*** - - - - - - - Built Environment popden 0.001*** - - - - - - - Climate Conditions tmax 0.082*** 0.082*** 0.083*** 0.085*** 0.077*** 0.087*** 0.074*** 0.093*** maxdev -0.033*** -0.033*** -0.039*** -0.040* -0.044*** -0.017** -0.008 -0.043*** precip -0.213*** -0.213*** -0190*** -0.221*** -0.218*** -0.235*** -0.216*** -0.224*** windavg -0.017*** -0.017*** -0.152*** -0.017*** -0.011** -0.020*** -0.019*** -0.018*** Temporal Dummy weekend 0.294*** 0.294*** 0.202*** 0.282*** -0.076 0.571*** 0.417*** 0.423*** Location Dummies henn - 1.894*** - - - - - - wrp - 1.091*** - - - - - - cedar - 2.033*** - - - - - - calhoun - 2.377*** - - - - - - nokomis - 1.607*** - - - - - -

Asterisks (*, **, ***) indicate variable is significant at the 0.1, 0.05, and 0.01 levels, respectively. Coefficients in bold are consistent with expected signs.

Predicted and Actual Trail Traffic, Mixed Mode, Minneapolis

Indianapolis Models of Trail Traffic

• Best model: pedestrian access zones – 29 variables, all significant at 1% level – OLS; Adj. R2 = 0.7966

• Pragmatic, more general model: tract level analyses – 25 variables, all significant at 1% level – OLS; Adj. R2 = 0.7557

Predicted & Actual Trail Traffic, Mixed Mode, Indianapolis

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actual countspredicted counts

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Best Models - OLS Census Tract – no urban form

Magnitude of Prediction Error

City (monitoring sites)

Mixed Mode Trail Traffic Models

Range of Prediction Error (point estimates)

Mean Prediction Error (point estimates)

Minneapolis (6) General model; (Neg. Binomial)

10.5% - 23.5% 17.1%

Site Specific Models (Neg. Binomial)

11.4% - 19.4% 15.2%

Indianapolis (30; tested 2)

Best Specified Model (OLS)

18.2% - 25.1% 21.7%

Census tract Model (OLS)

20.7% - 39.2% 30%

Bike & Ped Counts in Minneapolis, MN

Count Description

Method of observation

Manual

Traffic observed Cyclist - separate

Pedestrian - separate

Locations in Minneapolis

On /off-street bike facilities and no bike facilities

(n=259)

Period of observation 2007-2010

Number of observations

436

Length of observations

12-hour (n=43) 2-hour peak period

(n=352) Other

Limitations Human error

Counts by road/facility type

Type of Street / Facility

Daily Auto Traffic Volume

% all count locations

% of count location type with bike facilities

Principal Arterial 15,000 - 100,000 1% 0% Minor Arterial 5,000 - 30,000 42% 25% Collector 1,000 - 15,000 18% 20% Local < 1,000 24% 18% Off-street trail 0 15% 100%

Daily patterns in Non-motorized Traffic (n=43)

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15%

12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM

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hour

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18:3

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Time of Day

Bicycle: Loop Detector Bicycle: Manual Count Pedestrian: Manual Count

Scaling Factors for “Daily” (12-hour) Bike and Ped Traffic

Bicycle Pedestrian

Time period

Percent of 12-hour count

Scale factor

R2 Percent of

12-hour count

Scale factor

R2

7-8am 7.5% 13.2 0.88 6.9% 14.5 0.91 8-9am 9.3% 10.7 0.90 5.3% 18.7 0.96 9-10am 7.8% 12.9 0.89 6.1% 16.4 0.97 10-11am 6.4% 15.6 0.89 5.9% 16.8 0.96 11-noon 5.9% 16.9 0.87 9.2% 10.9 0.99 noon-1pm 5.2% 19.1 0.77 9.7% 10.3 0.99 1-2pm 7.2% 14.0 0.88 8.7% 11.5 0.99 2-3pm 7.5% 13.3 0.84 8.8% 11.4 0.98 3-4pm 9.3% 10.8 0.90 7.8% 12.8 0.98 4-5pm 12.0% 8.4 0.93 10.4% 9.6 0.97 5-6pm 12.6% 7.9 0.89 12.3% 8.2 0.996

Mean Bike Traffic Volumes by Street & Facility Type

(Minneapolis, 12-hour observations (6:30 a.m. – 6:30 p.m.; n=458)

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Principal Arterial A-Minor B-Minor Collector Local

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Street Functional Class

On-Street Bike Lane None

Factors that Affect Bike & Ped Volumes Neighborhood SES • Percent white • Age • HH income • Percent w/ college degree • Crime

Neighborhood built environment • Population density • Land use mix • Distance to water • Distance to CBD • Employment access (by transit)

Weather • Max daily temp • Precipitation

Road type • Arterial • Collector • Local • Off-street

Bike facility • On-street facility

Temporal • Year of count

Estimated 12-hour pedestrian traffic

Actual and Predicted Bike & Ped Traffic

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Bicycle: Negative Binomial

Actual count

Model prediction

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Pedestrian: Negative Binomial

Actual count Model prediction

Pedestrian models (sidewalks) perform slightly better than bicycle models (street traffic), but both are less accurate than trail traffic models.

Observations about Trail Traffic • Multiple methods available for counting • All counts are “wrong” – all measurement methods

require calibration • Traffic varies temporally & spatially, but consistently • Traffic correlated with weather, day of week, location • Statistical models explain up to 80% of variation in

traffic • Models not fully specified; limited to monitoring sites • Correlation is not causation

Observations about Bicycle and Pedestrian Traffic

1. Bicycle traffic • Higher on streets with bike facilities • Increasing over time in Minneapolis

2. Pedestrian traffic • Varies by street functional class • Not increasing over time in Minneapolis

3. Models are useful planning tools • Street and sidewalk models less accurate

than trail models • Quality limited by sample limitations

Ongoing Research Initiatives

• MnDOT Bike and Ped Counting Initiative – Methodologies for Counting Bicyclists and Pedestrians in

Minnesota

• Minneapolis Parks and Recreation Board – Network sampling (50+ two-week trail segment

counts) • Minneapolis Dept. of Public Works

– Analyses of 800 traffic signal timing counts • Rails to Trails Conservancy

– National urban trail model (annual data, multiple locations in NOAA climatic regions)

Questions?

Thank you.

Using 2011Adjustment Factors to Extrapolate 2012 Short Duration Counts

Step in Process Example Value or Calculation

1. Obtain February 2012 sample short duration count • Fri: 175 • Sat: 250

2. Look up 2011 February day of week factors • Fri: 1.04 * monthly daily traffic • Sat: 1.27* monthly daily traffic

3. Calculate 48-hour adjustment factor Sample 48-hour factor =

(1.04+1.27) / 2 = 1.16

4. Calculate 2012 February monthly average daily traffic from 48-hour adjustment factor

(175 + 250) = 183 1.16

5. Look up 2011 February factor (Feb average daily traffic / annual average daily traffic)

0.18

6. Calculate the 2012 annual average daily traffic (183 / 0.18) = 1,023

7. Use 2012 annual average daily traffic to calculate annual traffic volume.

1,023*365 = 373,422

Correlates of Trail Traffic in Indianapolis

Temporal Hypothetical Effect Measured

Effect

Weekend positive positive

Jan – Nov positive positive

StateFair positive positive

Weather

Temperature Deviation from Normal positive positive

Precipitation Deviation from Normal negative negative

Snow Deviation from Normal negative negative

Sunshine Deviation from Normal positive positive

Correlates of Trail Traffic in Indianapolis Neighborhood

Socio-Demographics Hypothetical

Effect Measured

Effect % Population less than 5 and greater than 64 negative negative

% African American negative depends on

model

% other ethnicity, exclude White and African American negative depends on

model

Mean % Population 25+ with College Degree positive positive

Mean Median Household Income, in dollars positive positive

Neighborhood Urban Form

Population density in 1/2 mile network distance to monitor positive positive

Percentage of commercial land use in trail neighborhood positive positive

Parking lots (Square Feet) in trail neighborhood positive positive

Average length of network street segments within 1/2 mile of counter negative positive

Description of Trail Segment Characteristics

Hypothetical

Effect Measured Effect

Openness: Percent total area visible within ½ mile of trail segment positive positive

Interconnectedness: Average value of visual magnitude for segment positive positive

Land Use Diversity: Shannon’s Diversity Index of land use in viewshed positive positive

Greenness: Difference between mean NDVI in neighborhood and trail viewshed positive positive

Percent Not Paved: Percent trail length with non-paved surface (e.g., gravel) negative negative

Railroad Xing: Number of railroad crossings at grade negative negative

Trail Intersection positive negative

Amenity Density: Number art, bench, signs divided by segment length positive positive

Average slope along trail segments ? depends on model

Sinuosity of trail segment ? (positive for nature trails) depends on model

Road Xing Density: Segment length / number of road crossings at grade

negative (interrupts use)

positive (access)

depends on model

Correlates of Trail Traffic in Indianapolis: Trail Segment Characteristics