01559065

Component

The research presented in this paper develops a hierarchical two-level heterogeneous route choice model based on learning using longitudinal, real-world experimental data. The study addresses two limitations in route choice literature, namely: driver heterogeneity and experiment reality. Specifically, aside from random error components, almost all route choice models used in transportation engineering practice assume that drivers are homogeneous in the way they make their route choices and in the way they respond to information. Although this paper studies only the way drivers make route choices, the proposed framework is capable of incorporating the heterogeneity of driver responses to information. The models developed in this paper are based on a sample of 20 drivers who collectively made more than 2,000 real-world route choices. In the proposed model, the first level model uses driver demographic and personality traits together with the characteristics of the choice situation to predict a behavior type. Within the context of this paper, a behavior type connotes a metaphoric measure of driver aggressiveness in route switching behavior, and captures driver behavior heterogeneity. The second level of the model uses the predicted behavior type and the travel experiences of the driver to predict the driver’s route choice. The results of the developed models indicate that in general: 1) behavior types can be predicted from driver demographics, personality traits, and choice situation characteristics, 2) the predicted behavior types are significant in route choice models, and 3) route choice models based on the proposed framework demonstrate better fits than state-of-the-art general models.

This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting.

01550057

15-3135

English

20p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Choice models; Demographics; Drivers; Heterogeneity; Route choice; Travel behavior

Data and Information Technology; Highways; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-3135

TRIS, TRB, ATRI

Dec 30 2014 1:03PM