This paper presents an adaptive traffic controller for stochastic road networks with an integrated model-based and data-driven solution framework. The model-based optimisation component operates based upon an underlying kinematic wave model driven by stochastic demand within a prediction horizon. The data-driven optimisation component operates based upon an approximate dynamic programming (ADP) formulation which approximates the state-control interactions over future stages with a parametric approximator. The approximator reduces the computational complexity of the adaptive control problem by parameterising the state and decision space. The parametric approximator is to be iteratively updated with online feeding of realisations of traffic states via a temporal difference (TD) learning process. Our results reveal that incorporation of the model-based component facilitates the training of the ADP-based state approximator, and hence improve the overall performance of the control system. We further develop a decentralised solution approach in which individual intersections are allowed to derive their own control policies in an asynchronous manner. The data-driven ADP approximator would serve as a central agent coordinating the control policies derived at individual intersections in the network. This is shown to be able to improve and stabilise the performance of the overall control system even under congested conditions. This is a significant progress in adaptive control system design with use of decentralised optimisation techniques. The present study contributes to the adaptive network traffic control with uncertainties through use of advanced modelling and optimisation methods.

本文提出了一种用于随机道路网络的自适应交通控制器，该控制器具有基于模型和数据驱动的集成解决方案框架。基于模型的优化组件基于由预测范围内的随机需求驱动的基础运动波模型进行操作。数据驱动的优化组件基于近似动态编程（ADP）公式进行操作，该公式使用参数逼近器来逼近未来阶段的状态控制交互。近似器通过参数化状态和决策空间来降低自适应控制问题的计算复杂度。通过时间差（TD）学习过程在线提供交通状态的实现，将迭代更新参数逼近器。我们的结果表明，结合基于模型的组件有助于训练基于ADP的状态逼近器，从而提高控制系统的整体性能。我们进一步开发了一种分散式解决方案方法，其中允许各个路口以异步方式导出其自己的控制策略。数据驱动的ADP逼近器将充当协调网络中各个交叉点得出的控制策略的中央代理。这表明即使在拥挤的情况下，也能够改善和稳定整个控制系统的性能。这是使用分散优化技术在自适应控制系统设计中的重大进步。