Modeling En-Route Driver Diversion Behavior with Advanced Traveler Information System: A Descriptive Bayesian Approach

摘要

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 integrated 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.