Enhanced Econometric Model Structures: Application to Travel Behavior and Transportation Modeling

In this presentation we introduce two innovative econometric model structures that allow us to better analyze discrete outcome processes. The first part of the presentation describes the development of a behavioral framework for analyzing commuter train users’ access mode and station choice. Typically, access 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. We discuss a latent segmentation based approach that 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 sociodemographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The 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 model is employed to investigate the role of sociodemographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior.