Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method

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.