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 route changing decision with behavior data collected from laboratory driving simulators and field Bluetooth 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 recalibrated for Maryland, based on Bluetooth detector data, and applied to analyze two dynamic message sign 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-based/agent-based travel demand models for various traffic operations and transportation planning applications.