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Update on Bicyclist & Pedestrian Data Collection and Modeling Efforts

Transportation Research Board

January 2010

Charlie Denney, Associate

Michael Jones, Principal

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Four concurrent efforts

#1: Seamless Travel: 2.5 year study of San Diego County

For Caltrans with UC Berkeley Traffic Safety Center

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Four concurrent efforts

#2: National Bicycle & Pedestrian Documentation Project

Free, unfunded service

With ITE, Texas Transportation Institute, and others since 2002

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Four concurrent efforts

#3: Non-motorized Transportation Pilot Project

With Volpe National Transportation Systems Center since 2006

#4: Trip generation study with ITE: initiated in 2009

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Collected and Analyzed to Date

NBPD count/survey data from 320+ agencies nationwide

NHTS add-on for San Diego County (2010)

Count/survey data at over 150 locations for 4 NTPP communities + mail travel diary surveys

365-day/yr 24 hr counts for 2 years at 5 locations

Manual counts/intercept surveys at 80 locations over 2 years

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Result

Largest collection of usable count and intercept survey data in the U.S.

Count data = validation = model accuracy

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Key Seamless Findings

76% of walk and 29% of bicycle trips are for transportation (v. recreation)

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Integral parts of transportation system

Deserve more funding

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Key Seamless Findings

Multi use pathways carry the most transportation trips

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Should be funded as transportation projects

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Key Seamless Findings

Multi use pathway free flow capacity is 120 persons per hour per foot of width

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Pathway design should be based on projected volumes

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Key Seamless Findings

Multi use pathway ‘design day’ is July 4th, 11am-1pm

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Conduct counts on this date

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Key Seamless Findings

Given seasonal & regional variations, annual volumes should be standard unit of measurement

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Versus ADT, peak hour, etc.

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Key Seamless Findings

Low volumes = high variability High volumes = low variability

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Conduct multiple counts at low volume locations for model validation

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Key Seamless Findings

Monthly volumes highly related to regional variations

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Automatic counters needed in each region of the country to calibrate models

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Monthly Variation: East/Midwest

Multi-Use Paths: Monthly Variations in Use

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mo

nth

ly U

se

(% o

f A

nn

ual

To

tal U

se)

Indianapolis (30 locations) Monon Trail (4 locations) Rhode Island Average

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Monthly Variation: San Diego

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How can we model behavior?

Four types of models needed

Each with different data needs and uses

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Model #1

Aggregate Model

Measures overall trip making in an area

Used in Non-motorized Transportation Pilot Project

Cross checked with NHTS & U of Minnesota Surveys

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NBPD Aggregate Model

Work CommuteEmployed adults riding bicycles/walking (US Census)

School CommuteSchool children riding bicycles/walking (US Census and available sources)

College CommuteCollege students riding bicycles/walking (UC Census)

Utilitarian TripsNon-work or school trips by bicycle/walking (surveys, other)

Recreational/DiscretionaryRecreational/discretionary trips by bicycle/walking (surveys, studies)

Total daily estimated bicycle and walking trips

Average trip length, trip purpose

Replaced vehicle miles, health, transportation, other benefits

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Model #2

Trip Generation

Measures trip making by land use

Will be used as part of impact analysis, localized models

Data being collected by ITE

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Model #3

Gravity Model

Measures volumes using 4-step process

Usable at bottlenecks and where there is a regular street grid, developed bike network, and level terrain

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Can we use existing models?

Existing 4-step (gravity) travel models will not work for bicyclists and pedestrians for most areas

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4-Step Models

Most trips within a TAZ Most ped trips linked Most factors affecting trip

making can’t be modeled: Topography Abilities, interests, aesthetics Concerns about security & traffic Quality of facilities & network

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How can we model behavior?

GIS-based (Seamless) Model

Estimates bicyclist and pedestrian volumes anywhere in a community

Can be used to develop collision rates, prioritize improvements, plan and design facilities and communities

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Seamless Model (Bike Module)

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GIS-based Seamless Model

30+ variables correlated with counts

Highest = Employment density and population density

Misleading R2 factors. Over 50% of locations off by more than 100%

Refinement factors resulted in R2

of .94, with mean residuals of -21

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Summary

More information or to participate: Alta Planning + Designwww.altaplanning.com

mgjones@altaplanning.comMichael Jones(415) 482-8660