01479221

Component

The purpose of the current research effort is to develop a framework for a better understanding of commuter train users’ mode and station choice behavior. Typically, mode and station choice for commuter train users is modeled as a hierarchical choice with mode being considered as the first choice in the sequence. The current study proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1 - station first and mode second and Segment 2 – mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The proposed approach offers many advantages compared to the traditional alternatives. First, we gain a better understanding of the decision processes by examining who are the individuals who choose the station (or mode) first. Second, the approach proposed is free from simulation and easy to implement. Third, the results from our analysis will provide insights to transit agencies on how to improve transit service to reduce the automobile travel to commuter train stations. The proposed latent segment model is estimated using data from an on-board survey conducted by the Agence Métropolitaine de Transport (AMT) for commuter train users in Montreal region. The proposed model is employed to investigate the role of socio-demographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior. The model results are employed for prediction of commuter train user behavior on a hold-out validation sample. Our data validation clearly illustrates the enhanced predictive power offered by the latent segmentation model.

This paper was sponsored by TRB committee AP070 Commuter Rail Transportation.

01470560

13-2688

English

22p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

Figures; References; Tables

Choice models; Decision making; Mode choice; Rail transit stations; Railroad commuter service

Data and Information Technology; Public Transportation; I70: Traffic and Transport

Transportation Research Board Annual Meeting 2013 Paper #13-2688

TRIS, TRB, ATRI

Feb 5 2013 12:33PM