返回
类型 基础研究 预答辩日期 2018-01-23
开始(开题)日期 2015-12-07 论文结束日期 2017-11-16
地点 交通学院303会议室 论文选题来源 省(自治区、直辖市)项目    论文字数 9.8 (万字)
题目 面向城市道路网交通瓶颈预警的信号控制关键技术研究
主题词 城市道路,交通瓶颈预警,短时交通流预测,车头时距,信号协调控制
摘要 交通拥堵预防与治理是城市道路交通管理中长期存在的难题,而交通瓶颈往往是交通拥堵的引发点和始发点,因此,研究城市道路网交通瓶颈具有重要意义。交通瓶颈预警正是在交通拥堵出现前,通过分析当前交通状态、研判发展趋势,进而识别城市道路网中可能发展成为交通瓶颈的交通节点,明确诱发交通瓶颈的原因,为交通管理措施的制定或调整提供决策依据,最终实现延缓甚至避免交通拥堵出现的目的。交叉口是多方向交通流的汇聚点,导致各方向交通流通过效率低;信号控制是保障交叉口通行效率和减少交通冲突的最有效方式,且在城市道路网中覆盖率广;因此,信号交叉口将作为城市道路网交通瓶颈预警的主要对象。本文立足于城市道路网交通瓶颈预警研究需求,以信号交叉口为主要研究对象,围绕城市道路网交通瓶颈预警流程以及关键支撑技术开展研究,以期为城市道路网交通瓶颈预警提供理论和技术支持。 本文以交通瓶颈研究现状为基础,以交通瓶颈管理需求为导向,制定城市道路网交通瓶颈预警流程,提出交通瓶颈预警关键技术需求,并分别对城市道路短时交通流预测、信号交叉口特征参数和控制方法、区域信号协调控制等关键技术开展研究。具体研究内容如下: (1)在对交通瓶颈特征、交通管理需求分析的基础上,提出城市道路网交通瓶颈预警流程和关键技术需求。 通过梳理交通瓶颈类型及其基本特征,以城市道路交通瓶颈管理需求为导向,将城市道路网交通瓶颈预警流程划分为四个部分,分别为交通基础信息采集与分析、潜在交通瓶颈识别、潜在交通瓶颈诱因分析和交通瓶颈预警信息发布。在支撑交通瓶颈预警的关键技术中,城市道路短时交通流预测技术是潜在交通瓶颈识别的重要工具,信号交叉口交通特征分析技术是基于交通流实时状态的潜在交通瓶颈识别方法和提升信号控制效率的基础,协调式信号控制方法是分析潜在交通瓶颈诱因和进行潜在交通瓶颈改善的重要手段。以上关键技术虽然已取得一定研究成果,但仍具有很大提升空间,本文将对上述技术进一步研究和改进。 (2)基于城市道路网交通流数据的时空相关性,提出城市道路交通流时空数据的降维方法,提高既有短时交通流预测模型的准确性。 既有短时交通流预测模型主要应用于公路交通流预测,直接应用于城市道路短时交通流预测难以适应城市道路交通流的波动性,预测结果准确性较低。本文基于城市道路交通流的时空相关性,提出城市道路交通流时空数据的降维方法,旨在提高既有短时交通流预测模型在城市道路交通流预测中的精度,以定性分析进行影响路段的初选、以多维尺度法对初选影响路段进行聚类分组、以相关性分析筛选最终影响路段。分别利用BP神经网络、多元线性回归预测模型验证提出的交通流数据降维方法对预测结果的影响,结果表明提出的降维方法能提高城市道路短时交通流预测模型的预测精度和运算速度。 (3)在分析信号交叉口特征参数敏感度和标定准确性的基础上,通过研究信号交叉口进口道排队车辆车头时距分布,提出信号交叉口特征参数标定方法和信号控制方法。 以信号损失时间、饱和流率和车头时距为信号交叉口主要特征参数,通过特征参数的敏感度和标定准确性分析,得出特征参数现有标定方式的不足,提出从车头时距分布出发对特征参数的标定进行优化;通过对车头时距分布的研究可知,固定位置处车头时距服从对数正态分布,连续排队位置车头时距服从对数分布,且不宜采用固定分位点标定每个排队位置的车头时距;以车头时距分布为基础,探索信号交叉口特征参数标定以及不以饱和流率和总损失时间为变量的信号控制方法,提出最佳绿灯时间控制方法、交叉口动态通行能力计算方法以及不以车道通行能力和信号损失时间为变量的信号配时算法。 (4)以联合树法和强化学习的结合算法进行区域交通信号协调控制,通过基本理论优化提高算法对交通信号控制的适用性,通过应用功能优化满足交通瓶颈预警对信号协调控制的需求。 为满足交通瓶颈预警需求,提出结合联合树法和强化学习算法对区域交通信号进行协调控制,但现有研究成果在信息传递方法、参数设置、动作选择策略、最大生成树分析、单个交叉口运行效果方面还存在不足,难以满足交通瓶颈预警需求。在现有研究基础上,提出从基本理论优化和应用功能优化两方面对基于联合树法的区域信号协调强化学习算法进行优化;基本理论优化包括核心参数优化、基本规则优化以及JTA信息传递模式优化,应用功能优化包括基于单个交叉口运行效果的算法优化和基于交通瓶颈识别需求的回报函数优化;构建测试环境对改进后的算法进行验证,结果表明改进后的算法不仅能更好地适应交通信号控制的基本需求,亦可满足交通瓶颈预警的需求。 (5)以实际城市交通环境为背景,对研究成果进行应用分析。 以常熟市局部道路网及交通流数据为基础,构建交通模拟环境;以多种车道的车头时距数据验证车头时距分布及信号交叉口动态通行能力;以交通模拟环境为平台,应用基于交通流实时状态的潜在交通瓶颈识别方法,利用不同评价指标识别潜在交通瓶颈;应用面向短时交通流预测需求的交通流时空数据降维方法,验证交通流时空数据降维方法对城市道路短时交通流预测精度和运算速度的改善作用;在潜在交通瓶颈识别的基础上,应用基于联合树法的区域信号协调强化学习算法分析交通瓶颈的诱发原因。
英文题目 RESEARCH ON KEY TECHNOLOGIES OF SIGNAL CONTROL FOR TRAFFIC BOTTLENECK WARNING IN URBAN ROAD NETWORK
英文主题词 urban road, traffic bottleneck, warning, short-term traffic flow prediction, dimension reduction, headway, joint tree method, reinforcement learning algorithm, signal coordination control
英文摘要 Traffic congestion prevention and control are the long-term problems in urban road traffic management. Traffic bottleneck is often the starting point of traffic congestion. Therefore, it is of great significance to study traffic bottlenecks in urban road network. Traffic bottlenecks warning is mean to identify traffic nodes in urban road network that may develop into traffic bottlenecks before traffic congestion, by analyzing the current traffic condition, determining the development trend, and studying the cause of traffic bottlenecks. Traffic bottlenecks warning provide a basis for decision-making on formulating and adjusting traffic management measures, and achieve the purpose of delaying or even avoiding traffic jams. Intersection is the convergence point of multi-directional traffic flow, resulting in low efficiency of traffic flow in all directions. Signal control is the most effective way to ensure traffic efficiency and reduce traffic conflicts at intersections, and the coverage in urban road network is wide. Therefore, signal intersections will serve as the main target for traffic bottlenecks warning in urban road networks. In order to provide theoretical and technical support for urban road network traffic bottleneck warning, based on the requirement of traffic bottleneck warning research, the paper takes the signalized intersection as the main research object, and studies the traffic bottleneck warning process and key support technologies. Based on the current research on traffic bottleneck and the demand of traffic bottleneck management, this paper puts forward the traffic bottleneck warning process in urban road network and key technical requirements of traffic bottleneck warning, and analyzes the short-term traffic flow prediction of urban roads, the characteristic parameters of signalized intersections and control methods, regional coordination of signal control and other key technologies to carry out research. The specific research contents are as follows. (1) Based on the analysis of traffic bottlenecks characteristics and traffic management requirements, this paper puts forward the traffic bottleneck warning process and key technical demand in urban road network. By analysing traffic bottleneck’s style and basic characteristics, taking urban road traffic bottleneck management as a guideline, the traffic bottleneck warning process is divided into four parts, which are traffic information collection and analysis, potential traffic bottleneck identification, potential traffic bottleneck incentives analysis and traffic bottlenecks warning information release. Among the key technologies that support traffic bottleneck warning, short-term traffic flow prediction technology is an important tool for potential traffic bottlenecks identification, traffic characteristics analysis technology of signalized intersections is the basis for potential traffic bottleneck identification method and signal control efficiency improvement, coordinated signal control is an important means of analyzing and improving potential traffic bottlenecks. Although the above key technologies have achieved some results, they still have much room for improvement. This paper will further study and improve the above technologies. (2) Based on the spatiotemporal correlation of traffic flow data in urban road network, a dimensionality reduction method of urban road traffic flow temporal-spatial data is proposed to improve the accuracy of the existing short-term traffic flow prediction model. Existing short-term traffic flow prediction models are mainly applied to the prediction of highway traffic flow. The direct application to urban road is difficult to adapt to the fluctuation of urban road traffic flow, and the prediction accuracy is low. Based on the spatial and temporal correlation of urban road traffic flow, this paper proposes a dimensionality reduction method for urban road traffic flow in order to improve the accuracy of applying existing short-term traffic flow prediction models in urban road. Qualitative analysis is used for primary selection of road sections, the multi-dimensional scale is used to cluster and group primary selected road sections, the correlation analysis is used to decide the final section. The effects of the proposed method on the forecasting results are verified by using BP neural network and multivariate linear regression model respectively. The results show that the proposed dimensionality reduction method can improve the prediction accuracy and speed of urban road short-term traffic flow forecasting model. (3) Based on the analysis of the sensitivity and calibration accuracy of characteristic parameter of the signalized intersection, the paper proposes the calibration method and the signal control method of the signalized intersection by studying the distribution of the headway of the queued vehicles at the signalized intersection. The signal loss time, saturation flow rate and headway are the main characteristic parameters of the signalized intersection. Through the analysis of the sensitivity and calibration accuracy of the characteristic parameters, the drawback of existing calibration methods are obtained. The method to calibrate the characteristic parameters through the distribution of the headway is advised. Through the study of the headway distribution, it can see that the headway distance at a fixed position obeys the logarithmic normal distribution and the headway at a continuous queue position obeys a logarithmic distribution, and it is not advisable to calibrate the headway of each queuing position by a fixed quantile. Based on the distribution of headway, the paper discusses the calibration method of signalized intersection characteristic parameters and the method of signal control which does not take saturated flow rate and total loss time as variables, and proposes the optimal green light time control method, the calculation method of dynamic capacity at intersections, and the signal timing algorithm that not relies on traffic capacity and signal loss time. (4) The regional traffic signal coordination control is carried out by combining the joint tree method and reinforcement learning algorithm. The applicability of the algorithm to the traffic signal control is improved by the basic theory optimization. The requirement of traffic bottleneck warning on signal coordination control is to be satisfied by the application function optimization. In order to meet the demand of traffic bottleneck warning, this paper proposes a coordinated tree control method and an enhanced learning algorithm for coordinated control of regional traffic signals. However, existing researches are still insufficient in information transmission, parameter settings, action selection strategies, maximum spanning tree analysis, and single intersection operation; it is difficult to meet traffic bottlenecks warning. Based on the existing research, this paper proposes to optimize the regional signal coordination algorithm from the aspects of optimization of basic theory and application of functional optimization. Optimization of basic theory includes optimization of core parameters, optimization of basic rules and optimization of JTA information delivery modes. Optimization of application functions includes algorithm optimization based on the effect of single intersection operation and reward function optimization based on identification of traffic bottlenecks. The test environment is constructed to validate the improved algorithm. The results show that the improved algorithm not only can better meet the basic needs of traffic signal control, but also meet the traffic bottleneck warning. (5) Based on the actual urban traffic environment, the application of the research results is analyzed. Traffic simulation environment is constructed based on part of the road network and traffic flow data of Changshu City. Headway distribution and dynamic traffic capacity at signalized intersections is verified by using headway data of various lanes. Taking traffic simulation environment as a platform, the potential traffic bottleneck identification method based on real-time traffic flow status is applied, and different evaluation indexes are used to identify potential traffic bottlenecks. The method of dimensionality reduction of traffic flow spatial data for short-term traffic flow prediction is applied to verify the effect of dimension reduction method on the prediction accuracy and speed of urban short-term traffic flow. Based on the identification of potential traffic bottlenecks, the reason of the traffic bottleneck is analyzed by using the regional signal coordination and reinforcement learning algorithm based on the joint tree method.
学术讨论
主办单位时间地点报告人报告主题
东南大学 2014.4.16 东南大学中山院 刘攀教授 交通安全科研发展的历史与展望
东南大学 2014.11.28 东南院102室 徐铖铖博士 研究、撰写和发表高质量SCI论文的方法和经验
东南大学 2015.11.11 交通学院3楼会议室 杨超教授 基于手机数据的个体活动特性研究
东南大学 2015.12.11 交通学院3楼会议室 曲晓波博士 On the fundamental diagram for freeway traffic
东南大学 2016.4.29 交通学院3楼会议室 张学孔教授 智慧出行绿色交通
东南大学 2013.11.15 306工作室 赵顗 城市道路交通拥堵收费可行性评价
东南大学 2013.10.22 交通学院3楼会议室 赵顗 南京市都市公交发展模式研究
东南大学 2016.10.16 302 工作室 赵顗 大数据支撑的公交供给侧改革
东南大学 2017.08.27 交通学院3楼会议室 赵顗 普渡大学联合培养组内学习
     
学术会议
会议名称时间地点本人报告本人报告题目
Transportation Research Board 2015.01.08 Washington Convention Center Network Capacity with Stochastic User Equilibrium Problem: a Comparison between Logit Type Model and Probit Type Model
Transportation Research Board 2017.01.09 Washington Convention Center Identification and Prediction of Potential Traffic Bottlenecks in the Urban Road Network
     
代表作
论文名称
Sensitivity Analysis for Link Capacity based on Elastic Demand Equilibrium with Queuing
基于Logit模型的道路拥堵收费策略可行性研究
Estimation of Saturation Flow Rate and Start-Up Lost Time for Signal Timing Based on Headway Distrib
Variability of Green Time to Discharge a Specified Number of Queued Vehicles at a Signalized Interse
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
张卫华 正高 教授 博导 合肥工业大学
陈淑燕 正高 教授 博导 东南大学
郑长江 正高 教授 博导 河海大学
王昊 正高 教授 博导 东南大学
季彦婕 正高 副教授 博导 东南大学
      
答辩秘书信息
姓名职称工作单位备注
梁衡宏 其他 东南大学