A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems

Abstract

ABSTRACTConsiderable effort has been devoted to developing real-time traffic network management systems for congestion mitigation in large metropolitan areas. These systems usually adopt high-resolution simulation models to provide real-time traffic network state estimation and short-term prediction capabilities. If the simulation results deviate from their corresponding real-world observations, online adjustment to the parameters of the simulation model is recommended to maintain its consistency. For that purpose, several consistency checking and online adjustment modules could be integrated with the simulation model and activated periodically to maintain the model consistency. This paper presents a multi-agent learning methodology for consistency checking and online calibration of real-time traffic network simulation models. The methodology allows multiple online adjustment modules to learn based on their historical performance. Consequently, an adjustment module is activated only if its activation is expected to reduce any detected model inconsistency. The methodology eliminates computation burden associated with the unnecessary activation of these adjustment modules, which might affect the system’s real-time execution requirement. The performance of the methodology is examined using real-world data. The results show that the methodology is promising as an efficient mechanism for maintaining model estimation consistency.