1 Update on Bicyclist & Pedestrian Data Collection and Modeling Efforts Transportation Research Board January 2010 Charlie Denney, Associate Michael Jones, Principal

2 Four concurrent efforts #1: Seamless Travel: 2.5 year study of San Diego County For Caltrans with UC Berkeley Traffic Safety Center

3 Four concurrent efforts #2: National Bicycle & Pedestrian Documentation Project Free, unfunded service With ITE, Texas Transportation Institute, and others since 2002

4 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

5 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

6 Result Largest collection of usable count and intercept survey data in the U.S. Count data = validation = model accuracy

7 Key Seamless Findings 76% of walk and 29% of bicycle trips are for transportation (v. recreation) = Integral parts of transportation system Deserve more funding

8 Key Seamless Findings Multi use pathways carry the most transportation trips = Should be funded as transportation projects

9 Key Seamless Findings Multi use pathway free flow capacity is 120 persons per hour per foot of width = Pathway design should be based on projected volumes

10 Key Seamless Findings Multi use pathway design day is July 4 th, 11am-1pm = Conduct counts on this date

11 Key Seamless Findings Given seasonal & regional variations, annual volumes should be standard unit of measurement = Versus ADT, peak hour, etc.

12 Key Seamless Findings Low volumes = high variability High volumes = low variability = Conduct multiple counts at low volume locations for model validation

13 Key Seamless Findings Monthly volumes highly related to regional variations = Automatic counters needed in each region of the country to calibrate models

14 Monthly Variation: East/Midwest

15 Monthly Variation: San Diego

16 How can we model behavior? Four types of models needed Each with different data needs and uses

17 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

18 NBPD Aggregate Model Work Commute Employed adults riding bicycles/walking (US Census) School Commute School children riding bicycles/walking (US Census and available sources) College Commute College students riding bicycles/walking (UC Census) Utilitarian Trips Non-work or school trips by bicycle/walking (surveys, other) Recreational/Discretionary Recreational/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

19 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

20 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

21 Can we use existing models? Existing 4-step (gravity) travel models will not work for bicyclists and pedestrians for most areas

22 4-Step Models Most trips within a TAZ Most ped trips linked Most factors affecting trip making cant be modeled: Topography Abilities, interests, aesthetics Concerns about security & traffic Quality of facilities & network

23 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

24 Seamless Model (Bike Module)

25 GIS-based Seamless Model 30+ variables correlated with counts Highest = Employment density and population density Misleading R 2 factors. Over 50% of locations off by more than 100% Refinement factors resulted in R 2 of.94, with mean residuals of -21

26 Summary More information or to participate: Alta Planning + Design Michael Jones (415)