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类型 基础研究 预答辩日期 2017-11-20
开始(开题)日期 2015-01-20 论文结束日期 2017-06-23
地点 东南大学交通学院三楼会议室 论文选题来源 973、863项目     论文字数 9 (万字)
题目 智慧高速公路交通检测器组合布设方法研究
主题词 智慧高速公路,交通检测器,组合布设,交通状态估计,样本量
摘要 智慧高速公路是传统的高速公路机电系统的延伸和创新。传统的高速公路机电系统以功能实现为导向,而智慧高速公路则以管理和服务为导向,强调对数据的应用和发布。智慧高速公路综合采用线圈、微波、视频、地磁等固定式检测手段,结合手机、GPS等车载定位和无线通讯系统的浮动车技术,实现路网断面和纵剖面的交通信息要素的全天候实时获取。当前,我国的高速公路虽已布设了一定数量的交通检测器,但由于现有检测设备在可靠性、抗干扰性、数据精度和时效性方面并不能够较好的满足动态交通数据获取的需求,再加上布设的交通检测器数量有限,布设方案不够完善,导致检测器的交通数据获取效果不理想。因此,本文针对高速公路上的交通信息检测技术进行研究,提出组合布设多类交通检测器的方法来提高路段交通信息的检测精度,为构建智慧高速公路打下坚实的数据基础。 (1)通过详细的文献综述,对交通检测技术的发展、检测器布设方法研究以及探测车样本量求解方法等国内外研究现状进行了总结和评述,并指出现有研究的不足在于:对单一类型检测器的研究较多,较少对多类型检测器组合应用进行研究;对未布设检测器路段研究较多,较少对已布设路段加密布设新型检测器进行研究;对于多类型检测器组合布设的研究缺乏定量分析,缺少对检测器布设类型和具体布设位置的研究。针对现有研究的不足和检测器布设存在的问题,界定了本文的研究目标和研究内容,并设计了论文研究的框架结构。 (2)提出了多类型检测器组合布设的方案。基于几种常用的固定式交通检测器如感应线圈、地磁、微波、视频、红外以及超声波检测器等,分析了15种固定式-固定式检测器的组合方案;结合GPS探测车、手机探测车、自动车辆定位技术(Automatic Vehicles Location-AVL)、自动车辆识别技术((Automatic Vehicles Identification-AVI)等移动式检测手段,分析了4种固定式-移动式检测器组合方案。依据各检测器的交通特性,分别从检测参数、优缺点以及适用性四方面对各组合方案进行了分析。综合考虑道路交通流、道路物理结构、气候条件等影响检测器工作的因素,制定了检测器组合布设的原则。 (3)构建了固定式-固定式检测器组合布设模型。针对未布设固定检测器路段,构建了整数规划-基因算法、聚类算法和两阶段算法来求解检测器组合布设问题。经过算例分析,选出了最优的检测器组合布设方法即整数规划-基因算法。基于该算法,本文又研究了已布设检测器路段加密布设新类型检测器的方法。通过构建单目标优化模型来确定加密布设的检测器的布设位置,利用改进的基因算法求解该模型。经过算例分析,得出以下结论:增加检测器的数量能够提高交通数据的估计精度,然而优化检测器的位置比单纯的增加检测器的数量在改善数据精度方面更有效;检测器的数量存在一个最优值,增加该值对数据的检测精度提高不大但会增加检测器的冗余,而降低该值则会影响数据的检测精度。 (4)设计了固定式-移动式检测器组合布设的方法。结合固定检测器的布设方法和探测车样本量的求解方法,提出了固定式-移动式检测器组合布设的方法。对于已布设有固定检测器的路段,仅需确定探测车的取样比例。通过仿真分析,选出最优的探测车取样比例,即增加其值不能改善数据的精度,但减小其值却会影响数据精度。通过BP神经网络融合固定检测器数据和移动检测器数据,得出在获取同样数据精度的前提下,已布设有固定检测器的路段上探测车的样本量比未布设固定检测器路段降低1%。对于未布设路段,提出了固定式-移动式检测器组合布设的方法。首先分别求出单独布设两类检测器时的检测器数量和固定检测器位置,选取较优的数值作为参照值。以此值为基础,将两类交通检测器进行组合,形成几组组合方案。对比各组合方案下的检测数据精度,选定数据误差最小的组合方案为最优方案。 (5)分析了组合布设检测器的多源数据融合方法。基于现阶段高速公路上可获取的数据种类,选定了本文融合的数据类型,即实时监控数据和GPS定位数据。提出了基于聚类算法的路段划分方法,根据划分路段估计路段交通参数。分别采用自适应加权融合算法和BP神经网络算法对两类实时监控数据和GPS定位数据进行融合处理。通过对比融合数据的误差,发现BP神经网络的融合效果要优于自适应加权融合算法。将融合所得的车速数据和路段密度数据结合起来,依据我国高速公路服务水平的划分标准,将高速路交通状态分为畅通、基本畅通、一般拥堵、严重拥堵四种,并根据速度和密度数值的大小对路段的交通状态进行了判别。通过分析采用速度数据、密度数据、速度和密度数据等这三种情况下的交通状态估计情况可知,相对采用速度和密度两种数据而言,仅采用速度数据或密度数据对交通状态的估计会产生较大的误差,出现错判或误判情况。
英文题目 METHODS FOR COMBINATION PLACEMENT OF MULTI-TYPE TRAFFIC SENSORS ON SMART HIGHWAYS
英文主题词 Smart highway, traffic sensor, combinational placement of multi-type sensors, traffic state estimation, sample size of probe vehicles
英文摘要 Smart highway is an extension and innovation of the traditional highway electro-mechanical system. The normal operation of the traditional highway electro-mechanical system is mainly for guaranteeing the fast, safe and efficient operation of highway. The smart highway emphasizes the application and transmission of traffic data for the highway management and service. It adopted the fixed traffic sensors, i.e., loop detector, microwave sensor, video, and geomagnetic vehicle detector, and probe vehicles including the GPS/GSM based vehicles, to obtain traffic data on the vertical and horizontal section of the road in real time all day and night. Today, freeways in China are already deployed with certain amount of traffic sensors. However, these sensors are unable to meet the requirements for the dynamic traffic data in reliability, anti-interference, data accuracy and timeliness. In addition, traffic data obtained by these sensors is not satisfactory since the number of sensors is limited and the layout scheme is not reasonable. Therefore, this article focuses on studying the combination of multi-type sensors on the freeway to improve the accuracy of traffic data. The development of traffic detection technologies, the methods for deploying traffic sensors and for seeking the sample size of probe vehicles have been reviewed extensively based on the detailed investigation of the related literatures. Most of the studies focused on the placement of single-type sensors, few studies explored the combination placement of multi-type sensors; most studies focused on the placement of sensors on the freeway without sensors, but few studies investigated the method of adding another type of sensors to the deployed freeway; though the combination placement of multi-type sensors has been considered in some literature, they did not explore the specific locations of these sensors. With the consideration of these factors, the object and the content of this article are determined, followed by the structure of the article. Based on the characteristics of the fixed sensors, 15 schemes which combined two types of fixed sensors on a freeway section are proposed. For probe vehicles, this research combined the fixed sensors with the probe vehicles. 4 schemes of such kind are listed. Each scheme is analyzed from several aspects, such as detection data, advantages and disadvantages, and feasibility and so on. Based on the conditions impacting sensor working which may include traffic volume, the road physical structure, the climate of the road area, the principles of the combination placement of multi-type sensors are proposed. Three methods, the integer programming-genetic algorithm (IP-GA), the clustering algorithm and the two-stage algorithm are applied to solve the multi-type fixed sensor deployment problem. After analyzing numerical examples, the optimal method for deploying multi-type sensors is determined, which is the IP-GA method. Based on this method, the approach of adding another type of fixed sensors on the way that deployed with fixed sensors already is discussed. A single objective optimization model was constructed to determine the locations of the add-on sensors. The genetic algorithm is used to solve the model. The analysis results show that: data estimation error can be reduced by increasing the number of sensors. However, optimizing the locations of sensors is more effective in improving data accuracy than by just increasing the number of sensors. There exists an optimal sensor number, by increasing which will not greatly improve data accuracy, but by reducing which will affect the data accuracy largely. Based on the method of deploying single-type fixed sensors on a freeway and the method of choosing the sample size of probe vehicles, the method of combing the fixed-mobile sensors to detect traffic data is designed. For the road deployed with loop detectors, only the sample size of probe vehicles need to be determined. By using the simulation analysis method, the optimal sample size is selected. For fusing loop detector data with the probe data, the BP neural network method is adopted. The results show that under the premise of obtaining the same precision of data, the sample size of probe vehicles on the freeway with fixed detectors is about 2% lower than the one on the freeway without fixed sensors. For the freeway without fixed sensors, a method is proposed to combine these two kinds of sensors to detect traffic sensors. Firstly, the number of fixed sensors and the sample size of probe vehicles are chosen by analyzing the scheme of deploying any one type of sensors on a freeway. Numbers of schemes are combined based on these values. By comparing data errors of each scheme, the optimal combination scheme is selected. By analyzing the data that can be collected on the freeway, this study chose the real-time monitoring data (two types of fixed sensor detection data) and the GPS location data to estimate the traffic state on a freeway. A link spilt method——the clustering algorithm——is proposed to separate the freeway into links for state estimation. By applying the adaptive weighted fusion algorithm and BP neural network algorithm, these three kinds of data are fused. The analysis results show that the BP neural network algorithm is superior to the adaptive weighted fusion algorithm. According to the highway service level standard in our country, traffic state is divided into four kinds: free-flow, unimpeded flow, general congestion, and serious congestion. By combining the speed and density together, traffic state is estimated. By using the speed data, the density data, and the combinantion of these two data separately, traffic state is estimated by referring to the highway service standard. The results indicate that only using the velocity data or the density data can result in false judgement of traffic condition.
学术讨论
主办单位时间地点报告人报告主题
东南大学物联网应用研究中心 2013.06 交通学院215 展凤萍 物联网知识介绍
东南大学物联网应用研究中心 2014.10 交通学院215 展凤萍 交通检测器介绍
东南大学物联网应用研究中心 2015.04.09 交通学院215 展凤萍 TRB论文撰写经验及论文介绍
UW-Madison工学院 2015.10 UW-Madison工学院 展凤萍 Method to identify the locations of dual-type detectors on freeway corridor
东南大学物联网应用研究中心 2017.3.23 交通学院215 展凤萍 高速公路交通检测器组合布设研究
东南大学物联网应用研究中心 2016.12.29 交通学院215 曲栩 高速公路可变限速控制
东南大学物联网应用研究中心 2017.3.16 交通学院215 何赏璐 网联车、自动驾驶和智能网联交通系统基础与思考
东南大学交通学院 2017.4.21 交通学院 李力 Headway Distribution: A Revisit
     
学术会议
会议名称时间地点本人报告本人报告题目
第十五届COTA国际交通科技年会(CICTP2015) 2015.7 北京 Optimal spacing of traffic counting stations on a high-volume highway based on queuing models
The Transportation Research Board (TRB) 94th Annual Meeting 2015.1 美国华盛顿 Sample size reduction method based on data fusion for freeways with fixed detectors
The 3rd International Conference on Transportation Information and Safety 2015.6 武汉
第十四届COTA国际交通科技年会(CICTP2014 2014.7 长沙
The Transportation Research Board (TRB) 95th Annual Meeting 2016.1 美国华盛顿
     
代表作
论文名称
Optimal Spacing of Traffic Counting Points on a High-Volume Highway Based on Queuing Models
Study on the Construction and Operation Mechanisms of Urban Intelligent Transportation System in Chi
Sample size reduction method based on data fusion for freeways with fixed detectors
Determining the sample size of probe vehicles in different traffic conditions on freeway
 
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
李文权 正高 教授 博导 东南大学
季锦章 正高 其他 中设设计集团股份有限公司
赵佳军 正高 研究员级高级工程师 其他 江苏高速公路联网营运管理有限公司
杨敏 正高 教授 博导 东南大学
何杰 正高 博导 东南大学
      
答辩秘书信息
姓名职称工作单位备注
曲栩 其他 讲师 东南大学