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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
[email protected] Jones(415) 482-8660