Integrated Choice and Latent Variable Models for evaluating Flexible Transport Mode choice

Polotis, I., Papaioannou, P., Basbas, S., 2012, in Research in Transportation Business & Management

doi:10.1016/j.rtbm.2012.06.007
Location Thessaloniki, Greece
Population Other (specify)
Sample size 450
Factor analysis type principal components, varimax rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by LCM

Abstract

Flexible Transport Modes and Services are primarily demand oriented initiatives that presuppose the self motivation of each individual in order to be preferred in a constant way. Given that fact, the examination of the potential success of such interventions should take into account not only quantitative data but also qualitative /behavioural parameters that participate in the mode choice procedure. This examination should follow specific guidelines in order to have common accepted evaluation procedures. In this paper, an Integrated Framework for the ex ante evaluation of a Flexible Transport Mode Schemes, is presented. The proposed framework is implemented in a real life problem: the introduction of Flexible Transport Mode scheme for commuting trips. Following the theories and concepts of the Framework, ICLV (Integrated Choice and Latent Variables) models were developed, in order to estimate the importance of a set of variables into mode choice process, for four alternative to the car modes The models that were developed though the usage of Structural Equation Modeling techniques are hybrid binary choice models and the discrepancy function that was used was the Bayesian estimation. The analysis showed that latent variables can significantly contribute in the process of interpreting the mode choice decision.

Factors

Models

Dependent variable Choice of car vs. bus
Model type ICLV
Sample size 282000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Choice model
Variable Coefficient p-value
DTinmode -0.025 0.211
DToutmode -0.024 0.549
Dpercost -0.019 0.527
Personal beliefs -0.7 0.0
Traveler characteristics -0.14 0.046
Latent variable model
Variable Coefficient p-value
env1 -0.122 0.015
env2 -0.144 0.004
env3 -0.036 0.368
com1 -0.18 0.0
com2 -0.034 0.571
com3 0.116 0.097
flex1 -0.201 0.0
flex2 -0.658 0.0
flex3 -0.491 0.0
age -0.136 0.052
behstage 0.172 0.455
income -0.499 0.013
numcar -0.044 0.142
Variable Coefficient p-value
Intercept 0.53 0.077
Dependent variable Choice of car vs. taxi
Model type ICLV
Sample size 179000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Choice model
Variable Coefficient p-value
DTinmode -0.019 0.057
DToutmode -0.026 0.194
Dpercost -0.07 0.243
Personal beliefs -0.07 0.317
Traveler characteristics -0.53 0.0
Latent variable model
Variable Coefficient p-value
env1 -0.025 0.532
env2 -0.025 0.405
env3 0.001 0.98
com1 -0.035 0.243
com2 -0.043 0.282
com3 -0.018 0.719
flex1 -0.043 0.282
flex2 -0.039 0.33
flex3 -0.056 0.351
age -0.54 0.0
behstage 0.228 0.079
income -1.251 0.0
numcar -0.122 0.0
Variable Coefficient p-value
Intercept 0.51 0.0
Dependent variable Choice of solo car vs. carpool
Model type ICLV
Sample size 712000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Choice model
Variable Coefficient p-value
DTinmode -0.023 0.021
DToutmode -0.019 0.342
Dpercost -0.032 0.424
Personal beliefs -0.24 0.0
Traveler characteristics -0.38 0.0
Latent variable model
Variable Coefficient p-value
env1 -0.127 0.011
env2 -0.126 0.012
env3 0.008 0.841
com1 -0.115 0.021
com2 -0.206 0.003
com3 -0.087 0.147
flex1 -0.17 0.001
flex2 -0.145 0.016
flex3 -0.234 0.003
age -0.122 0.015
behstage 0.331 0.052
income -0.645 0.0
numcar -0.119 0.003
Variable Coefficient p-value
Intercept 0.59 0.001
Dependent variable Choice of car vs. metro
Model type ICLV
Sample size 408000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Choice model
Variable Coefficient p-value
DTinmode -0.029 0.004
DToutmode -0.012 0.549
Dpercost -0.122 0.0
Personal beliefs 0.1 0.153
Traveler characteristics 0.31 0.0
Latent variable model
Variable Coefficient p-value
env1 0.009 0.764
env2 0.016 0.594
env3 -0.008 0.79
com1 0.053 0.185
com2 0.058 0.246
com3 0.002 0.968
flex1 0.034 0.395
flex2 0.068 0.174
flex3 0.074 0.139
age 0.375 0.004
behstage 0.141 0.407
income 0.356 0.011
numcar 0.067 0.094
Variable Coefficient p-value
Intercept 0.02 0.92

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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