01478101

Component

This paper presents a Bayesian approach for modeling and calibrating drivers' en-route route changing decision with behavior data collected from laboratory driving simulators and field blue-tooth detectors. The behavior models are not based on assumptions of perfect rationality. Instead, a novel descriptive approach based on naive Bayes rules is proposed and demonstrated. The en-route diversion model is first estimated with behavior data from a driving simulator. Subsequently, the model is re-calibrated for Maryland, based on blue-tooth detector data, and applied to analyze two dynamic message sign (DMS) scenarios on I-95 and I-895. This calibration method allows researchers and practitioners to transfer the en-route diversion model to other regions based on local observations. Future research can integrate this en-route diversion model with microscopic traffic simulators, dynamic traffic assignment models, and/or activity/agent-based travel demand models for various traffic operations and transportation planning applications.

This paper was sponsored by TRB committee ADB10 Traveler Behavior and Values.

01470560

13-3334

English

14p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

Figures; Maps; References; Tables

Bluetooth technology; Driving simulators; Highway operations; Route choice; Traffic assignment; Traffic simulation; Travel demand; Variable message signs

Maryland

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2013 Paper #13-3334

TRIS, TRB, ATRI

Feb 5 2013 12:40PM