铁道科学与工程学报2024，Vol.21Issue(6)：2229-2240,12.DOI:10.19713/j.cnki.43-1423/u.T20231561
基于模型偏差学习的交通信号自适应优化方法
A model bias learning approach for adaptive traffic signal control
摘要
Abstract
Urban traffic signal control is an indispensable part of traffic management,which affects the traffic operation of the whole system.Traditional model-based control cannot effectively make use of the real-time traffic data.Moreover,the inherent prediction errors between model and reality usually lead to unreasonable signal timing plans.This paper proposed a model bias learning approach for adaptive optimization of traffic signal control that integrates the model-based method with the data-driven method.First,aiming at the deviation of the traffic flow prediction model,a model bias function was introduced to represent the error between the prediction model and the actual traffic flow state.Second,the model bias function was formulated based on the radial basis function(RBF)neural network,and the model deviation was learnt by combining the actual traffic flow data to improve the fitting effect of the deviation function.Furthermore,an adaptive optimization method considering the model bias information was proposed to improve the signal control performance.By taking the small test road network and the actual road network as examples,the model deviation learning method proposed in this paper was verified based on SUMO simulation.Considering different traffic flow conditions,the control performance indices were analyzed by comparing with fixed timing and model predictive control.Numerical results show that our proposed model bias learning approach can effectively improve the accuracy of the prediction model and reduce the total time spend of the system.Compared with the model-based control method and the model predictive control method,our proposed method reduces the cumulative network total travel time by 38.3%and 25.6%on average,respectively,improving the performance of the optimal control.Finally,the effectiveness of the method was verified on the Xuancheng network.关键词
城市道路交通/信号控制/模型与数据融合驱动/模型偏差/自适应优化Key words
urban road traffic/signal control/integrated model-based and data-driven/model bias/adaptive optimization分类
交通工程引用本文复制引用
黄玮,张轩宇,李世昌,赵靖..基于模型偏差学习的交通信号自适应优化方法[J].铁道科学与工程学报,2024,21(6):2229-2240,12.基金项目
国家自然科学基金资助项目(52102401,52122215) （52102401,52122215）
上海市曙光计划项目(22SG45) （22SG45）
上海市创新行动计划(23692112200) （23692112200）
