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

机译：用于实时交通网络管理系统的在线校准和一致性检查的多代理学习方法

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摘要

Considerable 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.
机译：已经投入了大量的精力来开发实时交通网络管理系统，以减轻大都市区的拥堵。这些系统通常采用高分辨率仿真模型来提供实时交通网络状态估计和短期预测功能。如果仿真结果偏离其相应的实际观察结果，建议在线调整仿真模型的参数以保持其一致性。为此，可以将几个一致性检查和在线调整模块与仿真模型集成在一起，并定期激活以保持模型的一致性。本文提出了一种多智能体学习方法，用于实时交通网络仿真模型的一致性检查和在线校准。该方法允许多个在线调整模块根据其历史表现进行学习。因此，仅在期望激活调整模块以减少任何检测到的模型不一致时，才激活调整模块。该方法消除了与不必要地激活这些调整模块相关的计算负担，这可能会影响系统的实时执行要求。使用实际数据检查方法的性能。结果表明，该方法有望成为维持模型估计一致性的有效机制。

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