基于关键本征模态函数的道路交通信号控制时段划分方法  

A Time-of-the-day Partitioning Method for Traffic Signal Control Based on Key Intrinsic Mode Functions

作　　者：冯斌 徐建闽[1] 林永杰 FENG Bin;XU Jianmin;LIN Yongjie(School of Civil Engineering&Transportation,South China University of Technology,Guangzhou 510641,China;Guangzhou Institute of Modern Industrial Technology,Guangzhou 511458,China)

机构地区：[1]华南理工大学土木与交通学院,广州510641 [2]广州现代产业技术研究院,广州511458

出　　处：《交通信息与安全》2023年第1期75-84,共10页Journal of Transport Information and Safety

基　　金：国家自然科学基金项目(61873098);广州市南沙区科技计划项目(2021MS018)资助。

摘　　要：交通信号控制是缓解城市交通拥堵的重要手段,时段划分是信号灯控交叉口多时段控制的基础,合理的划分方法有助于提高信号控制效率。对于固定配时的信号灯控交叉口,传统时段划分方法主要借助于路口历史交通流量数据,依据人工经验或者简单聚类算法,直接进行时段划分,未能充分考虑交通流的时序性和随机性问题,不利于交通控制整体效益。综合考虑交通流中随机因素和时序性对时段划分的影响,本文研究了基于经验集合模态分解和有序聚类的时段划分方法。利用集合经验模态分解处理交叉口流量数据,提取了若干个本征模态函数及1个余项。借助皮尔逊相关系数分析原始流量数据、本征模态函数、余项这三者之间的关系,优选与原始流量相关性最高的本征模态函数或余项作为交通流的关键成分,使用关键成分代替流量数据进行有序聚类,完成时段划分。通过寻找不同分割个数下最小损失值突变点,获取最佳分割数,并得到最佳方案。以广东省中山市一个路口为案例对本文提出的时段划分方法进行算例分析,VISSIM仿真结果表明:(1)相比于现状,提出的方法在工作日和非工作日分别能提高路口通过车辆数11.32%和2.62%,缩短排队长度18.67%和12.02%;(2)非工作日车均延误减少6.80%,停车延误减少5.87%,工作日车均延误和停车延误变化不大。Traffic signal control is an important tool to relieve urban traffic congestion and time-of-the-day partition is the basis for optimizing multi-period signal control at isolated signalized intersections in that a proper partition can significantly improve the efficiency of traffic control.For an intersection with a fixed-timing signal control strate-gy,traditional methods for time-of-the-day partition are usually based on experiences or simple clustering algorithms.These methods use historical traffic flow data to directly divide a day into several time periods,which fail to consider the stochasticity of traffic flow and the regularity of time sequence and lead to no contributions to the overall effec-tiveness of traffic control.To overcome this problem,this study proposes a new method for time-of-the-day partition,which uses an ensemble empirical mode decomposition(EEMD)and a fisher clustering algorithm.The intrinsic mode function(IMF)and corresponding residual from traffic flow data are extracted using EEMD.The Pearson cor-relation coefficient is calculated to analyze the relationship between the IMF,the residual,and the original traffic flow.The IMF or the residual that gives the highest correlation coefficient is identified as the key component,which replaces the traffic flows in the fisher clustering and partitioning process.The optimal number of clusters is deter-mined by identifying the elbow point of the minimum loss values with different numbers of clusters,and the opti-mal time-of-the-day partition plan is obtained.A case study based on an intersection in the City of Zhongshan,Guangdong Province,is conducted to verify the proposed method.Simulations are carried out using the VISSIM software and study results show that①Compares to the current situation,the proposed method can increase the number of vehicles going through the intersections by about 11.32% and 2.62% and can reduce the queue length by about 18.67% and 12.02%on weekdays and weekends,respectively.②The proposed method also can reduce the a

关 键 词：交通信号控制 时段划分 集合经验模态分解 有序聚类 

分 类 号：U491.54[交通运输工程—交通运输规划与管理]