基于RBF神经网络预测的交叉口信号周期时长优化  

Optimization of Cycle Length of Intersection SignalBased on RBF Neural Network Prediction

作　　者：成卫[1] 刘翔 雷建明 CHENG Wei;LIU Xiang;LEI Jianming(School of Traffic Engineering,Kunming University of Science and Technology,Kunming 650504,Yunnan,China;Traffic Police Detachment of Yuxi Public Security Bureau,Yuxi 653100,Yunnan,China)

机构地区：[1]昆明理工大学交通工程学院,云南昆明650504 [2]玉溪市公安局交通警察支队,云南玉溪653100

出　　处：《重庆交通大学学报（自然科学版）》2021年第4期13-18,共6页Journal of Chongqing Jiaotong University(Natural Science)

摘　　要：针对交叉口信号配时仅仅依靠单一历史数据进行计算的现状,为了使交叉口信号周期时长能够充分利用过往历史数据,根据城市交通流量具有周期性与不确定性的特点,提出了一种基于RBF神经网络预测的交叉口信号周期时长优化方法。根据交叉口晚高峰历史交通流量对RBF神经网络进行训练,用训练好的RBF神经网络去预测未来某一天交叉口晚高峰的交通流量;结合直行当量系数法将预测得到的交通流量转换为等效直行车流量,建立以平均车辆延误及车辆平均停车率为主要控制目标的多目标优化模型,用MATLAB建立遗传算法进行求解;以平均车辆延误与车辆停车率为评价指标对比分析了优化方法、Webster法和实际配时3种方法得到的结果。结果表明:提出的优化方法相较于Webster法和实际配时,在工作日分别减少12%、2%的平均车辆延误,在非工作日分别减少20%、18%的平均车辆延误,提高了交叉口的通行效率。Aiming at the situation that the existing intersection signal timing only depended on a single historical data,an optimization method of intersection signal cycle length based on RBF neural network prediction was proposed in order to make full use of the historical data,according to the two characteristics of urban traffic flow,such as periodicity and uncertainty.Firstly,the RBF neural network was trained according to the historical traffic flow of the intersection in the evening peak,and the trained RBF neural network was used to predict the traffic flow of the intersection in the evening peak of the next day;then the predicted traffic flow was converted into the equivalent straight traffic flow by combining with the straight equivalent coefficient method;and then a multi-objective optimization model with average vehicle delay and average vehicle parking rate as the main control objectives was established,and a genetic algorithm was established in MATLAB to solve the problem.Finally,the results obtained by the proposed method were compared with those obtained by the Webster method and the measured traffic flow in actual time allocation with the average vehicle delay and vehicle parking rate as the evaluation index.The results show that:compared with Webster method and actual time allocation,the proposed method can reduce the average vehicle delay by 12%and 2%on weekdays,and 20%and 18%on non-weekdays,which effectively improves the traffic efficiency of intersections.

关 键 词：交通工程 预测 RBF神经网络 交叉口信号周期时长 多目标优化 遗传算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]