A Descriptive Bayesian Approach to Modeling and Calibrating Drivers’ En-Route Diversion Behavior

摘要

This paper presents a Bayesian approach for modeling and calibrating drivers’ en-route routechanging decision with behavior data collected from laboratory driving simulators and field bluetoothdetectors. The behavior models are not based on assumptions of perfect rationality. Instead,a novel descriptive approach based on naïve Bayes rules is proposed and demonstrated. The enroutediversion 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, andapplied to analyze two dynamic message sign (DMS) scenarios on I-95 and I-895. Thiscalibration method allows researchers and practitioners to transfer the en-route diversion modelto other regions based on local observations. Future research can integrate this en-route diversionmodel with microscopic traffic simulators, dynamic traffic assignment models, and/oractivity/agent-based travel demand models for various traffic operations and transportationplanning applications.