Characteristics of premium transit services that affect mode choice

Outwater, Spitz, Lobb, Campbell, Sana, Pendyala, Woodford, 2011, in Transportation

doi:10.1007/s11116-011-9334-0
Location Salt Lake City, UT
Population General
Sample size 1804
Factor analysis type nan, nan rotation
Stepwise regression no
Removal of insignificant variables yes
Reviewed by LCM

Abstract

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city. © 2011 Springer Science+Business Media, LLC.

Factors

Variable Pattern loading
nan () nan
Variable Pattern loading
nan () nan
Variable Pattern loading
nan () nan
Variable Pattern loading
nan () nan

Models

Dependent variable Likelihood of using a given mode to commute
Model type Nested logit choice model
Sample size 32616.0
R2 0.455
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -5839.59
Auto
Variable Coefficient p-value
IVTT_A (min) -0.033 0.0
Trip gas cost ($) -0.175 0.0
Parking cost ($/day) -0.235 0.0
Reliability -0.018 0.003
Male (0=no, 1=yes) -0.121 0.071
HH income less than 125K -0.236 0.017
Origin TAZ is rural (0=no, 1=yes) -0.965 0.051
Convenience/inclination (transit user) -0.115 0.004
Service availability (transit user) -0.505 0.0
Auto constant 0.71 0.0
Auto nest 1.0 nan
Bus
Variable Coefficient p-value
IVTT_Transit (min) -0.039 0.0
Access time (min) -0.054 0.0
Wait time (min) -0.053 0.0
Fare ($ one-way) -0.405 0.0
Transfers (0=no, 1=yes) -0.351 0.0
Reliability -0.018 0.003
Transit info (0=none, 1=real-time) 0.185 0.001
Stop design (0=standard, 1=modern) 0.167 0.0
On-board amenities (0=std, 1=modern) 0.125 0.016
Bus constant 0.0 nan
Transit nest 0.651 0.0
Train
Variable Coefficient p-value
IVTT_Transit (min) -0.039 0.0
Access time (min) -0.054 0.0
Wait time (min) -0.053 0.0
Fare ($ one-way) -0.405 0.0
Transfers (0=no, 1=yes) -0.351 0.0
Reliability -0.018 0.003
Transit info (0=none, 1=real-time) 0.185 0.001
Stop design (0=standard, 1=modern) 0.167 0.0
On-board amenities (0=std, 1=modern) 0.125 0.016
IVTT (min) with modern on-board amen. 0.005 0.012
Wait time (min) with real-time information 0.014 0.02
HH income 125K or more 0.192 0.004
Origin TAZ is rural (0=no, 1=yes) 0.855 0.026
Option to work from home 0.905 0.0
Train constant 0.002 0.974
Transit nest 0.651 0.0

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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