Automatic calibration of behavioral parameters for variable message sign-based route guidance consistent with driver behavior

Variable message signs (VMSs) can be used to divert traffic to less congested areas of road networks and to enhance network performance. Central to these goals are increasing the acceptability of the suggested guidance and improving the credibility of VMS information among drivers. This study develops a new model for quantifying the effect of VMS messages on drivers' en route diversion behavior for real-time VMS-based route guidance that is consistent with driver behavior. The model incorporates attributes that can be obtained online, including traffic information, traffic flow, weather, and incident duration. The parameters, observation noise, and system noise characterizing this model are calibrated dynamically according to real-time detected traffic data and adaptive Kalman filtering theory. In the calibration, the consistency between time-dependent actual and predicted traffic states is verified. The results indicate that the calibration model enhances the accuracy of traffic state prediction and thereby increases the effectiveness of the proposed route guidance. The model is also suitable for application to real-world situations because it can more accurately estimate and predict the likely route choices of drivers using aggregate-level traffic data. Such data can be used to track the evolutionary process of en route diversion behavior, as well as design meaningful route guidance control goals and strategies for traffic decision makers. Science Foundation.