A Trip Generation Model for Walking – by Guang

Report
A Walk Trip Generation
Model for Portland, OR
Guang Tian, Reid Ewing
Presented by
Guang Tian
Department of City & Metropolitan Planning
University of Utah
[email protected]
Walkable street, but not within
walkable distance
Walkable distance, but without
walkable street
Research Question
When assessing the benefits, costs, and priorities of
proposed pedestrian improvements for the government or
developers, it is frequently helpful to answer the following
questions:
• What kinds of built environments encourage people to
choose to travel on foot? (neighborhood scale or street
scale)
• If a new pedestrian facility is built, how many people will
use it?
Trip generation in practice
 Trip generation in Conventional Four-Step
Travel Demand Models
• TAZs, block group or
census tract, too big to
represent the actual built
environment for walk
• Interzonal trips are
usually ignored,
including walk trips
(Beimborn et al. 1997, p. 17 & p. 15)
 Trip Generation in Conventional Traffic Impact
Studies (ITE Methodology)
“Data were primarily collected at suburban
localities with little or no transit service,
nearby pedestrian amenities, or travel demand
management (TDM) programs.” (ITE 2012,
p.1)
Further, ITE advises: “At specific sites, the user
may want to modify the trip generation rates
presented in this document to reflect the presence
of public transportation service, ridesharing or
other TDM measures, enhanced pedestrian tripmaking opportunities, or other special
characteristics of the site or surrounding area.”
(ITE 2012, vol. 1, p. 1)
Trip generation in research
• There are many studies on the associations between
built environment and travel (Saelens and Handy, 2008; Ewing
and Cervero, 2010). Specifically, positive relationships
between walking for transportation and density, land use
mix, distance to destinations and street connectivity
(Besser and Dannenberg, 2010; Saelens et al., 2008; Sehatzadeh et al.,
2011).
• Challenges of walk behavior studies: sufficiently detailed
data on the built environment that can be spatially
matched to sufficiently detailed data on travel behavior
is challenge (Handy et al., 2002); more refined spatial unit (Liu
et al., 2012).
Conceptual Framework
“A city sidewalk by itself is nothing. It is an abstraction. It means
something only in conjunction with the buildings and other uses that
border it, or border other sidewalks very near it” (Jane Jacobs, the Death
and Life of Great American Cities, 1961, p. 29).
Study Area – Portland, OR
2011 Oregon Household
Travel and Activity Survey
Individual trips
Households info
Built environment data
Population, employment
Parcel land use data
Street network
Transit service
intersections
Sidewalk system
Traffic control device
……
Analysis
 Half-mile road network buffer around household location
Cumulative percentage
Mean
Walk
distance
0.31
Std.D
0.716
< 0.25 mile
< 0.5 mile
63.2%
85.6%
< 1.0 mile
96.4%
 Variables
Sociodempgraphic
characteristics
Number of
walk trips per
household
Neighborhood
development
Household size
Race
Number of workers
Income
Single-family vs. multifamily
Activity density
Job-housing balance
Land use mix
Transit stop density
Employment accessibility
Sidewalk quality (by PCA)
Street design
Intersection density
% of 4-way intersection
Sidewalk coverage
Improved corner for pedestrian
Traffic calming
Traffic signal
Slope
 Hurdle Models
• The stage 1: categorizes households as either having
any walk trips or not, and uses logistic regression.
• The stage 2: estimates the number of walk trips
generated by households with any (positive) walk
trips, and uses negative binomial regression.
o Stage 1 - Logistic regression
•
Dependent variable: households with any walk trips
(1=yes, 0=no)
Coefficient
Household size
Std. Error
T-ratio
P-value
0.431
0.054
7.955
< 0.001
-0.297
0.167
-1.784
0.074
0.321
0.077
4.187
< 0.001
-0.843
0.132
-6.383
<0.001
Activity density in thousand
0.035
0.008
4.400
<0.001
Job-housing balance
0.403
0.241
1.673
0.094
Sidewalk system quality
0.328
0.061
5.411
<0.001
-1.126
0.292
-3.862
<0.001
Race_dummy (1=white, 0=nonwhite)
Number of workers in household
Living (Single-family=1, multi-family=0)
Constant
Sample size: 1970
-2 log-likelihood ratio: 2404
Pseudo R2: 0.15 (Cox-Snell)
o Stage 2 - Negative binomial regression
•
Dependent variable : the number of walk trips for the
subset of households that make walk trips
Coefficient
Std. Error
T-ratio
P-value
Constant
0.922
0.128
7.187
< 0.001
Household size
0.143
0.031
4.555
< 0.001
Transit stop density
0.003
0.001
2.834
0.005
Land-use entropy
0.542
0.229
2.365
0.018
Sample size: 962
-2 log-likelihood Ratio: 34
Pseudo R2: 0.01 (McFadden)
Discussion and implements
 Socioeconomic variables are strong predictors of
walk trip generation.
•
•
•
•
Household size
Race
Number of workers
Living (single-family vs. multi-family)
According to the residential self-selection theory, “individuals with a
preference for walking consciously choose a neighborhood that is conducive
to walking” (Cao, Handy, and Mokhtarian 2006, p. 4). For the public policy
makers, in order to induce more walking trips by pedestrian facilities, the
people in neighborhoods might be considered.
 Positive influences of built environment
•
Higher activity density, good balance of jobs and housing supply,
more mixed use neighborhoods create more opportunities that the
destinations are within walkable distance.
•
Better transit services provides opportunities of combining
different travel modes.
•
Interconnected street network provides places to cross the street
and routing options to destinations, and generates more eventful
trips.
•
Sidewalk coverage and improved corners provide a safe and
comfortable travel way for pedestrians.
Conclusion
 There are two distinctions between this study
and earlier studies of walk trip generation.
•
The two-stage hurdle model
- Logistic regression: the decision of a household to include
walking among its mode choices
- Negative binominal regression: the decision on how many
walk trips to make
•
A full array of street design variables represented by a
principal component
Street design does not seem to affect the number of walk
trips made by a household, but has a significant effect on the
decision to walk at all.
 Limitations
•
One city (limits the external validity of the findings)
•
Other factors (weather condition and season changes;
parking supplies and prices; residential attitudes)
•
Endogenous vs. exogenous (pedestrian facilities causes
walk trips or walk trips causes pedestrian facilities )
•
Pedestrian network buffer ( instead of street network
buffer)
Any questions
and comments are
welcome !

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