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 |