This paper proposes a network traffic control method to enhance model-based traffic control with data-driven online adaptive optimization. A macroscopic traffic flow model is first developed for the model prediction. Then, the model is further enhanced in real time based on the performance measurements using the adaptive optimization or learning algorithm. Integrating the data-driven optimization into the model-based predictive control, the proposed control method is able to identify the key model parameters and optimize the actual network performance. Experiments on a toy network are conducted to test the efficiency of the proposed control method. Simulation results show that the proposed method generates better control performance than the general model-based control method.
Enhancing Model-Based Traffic Signal Control with Data-Driven Adaptive Optimization
22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China
CICTP 2022 ; 346-356
2022-09-08
Conference paper
Electronic Resource
English
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