Proactive traffic responsive control based on state-space neural network and extended Kalman filter  

基于状态空间神经网络和扩展卡尔曼滤波的主动交通感应控制(英文)

作　　者：过秀成[1] 李岩[1] 杨洁[1] 

机构地区：[1]东南大学交通学院,南京210096

出　　处：《Journal of Southeast University(English Edition)》2010年第3期466-470,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China (No.50422283);the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)

摘　　要：The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.为改善方案选择式交通感应控制输出的交通信号配时方案滞后于实时交通状态的缺点,提出用状态空间神经网络和扩展卡尔曼滤波模型预测未来交通状态的优化配时方案.采用能反映道路网络几何特征的状态空间神经网络拓扑结构,结合当前时段和前一时段的路段交通状态,预测下一时段交通状况并选择与其相匹配的信号配时方案;应用扩展卡尔曼滤波训练状态空间神经网络,提高其训练效率及精度.选用南京市广州路的实测交通数据和由多目标遗传算法得出的最优信号控制方案验证模型的有效性.研究结果表明,与BP神经网络和状态空间神经网络相比,所提出的模型能够根据道路状况选择合适的交通控制方案.

关 键 词：state-space neural network extended Kalman filter traffic responsive control timing plan traffic state prediction 

分 类 号：U491[交通运输工程—交通运输规划与管理]