基于交通流数据的城市交通拥堵检测方案研究

发布时间：2018-12-14 02:35

【摘要】：目前城市交通拥堵问题成了阻碍世界经济发展以及城市现代化建设的难题之一。而我国更是由于经济发展速度过快,导致城市建设与道路网络构建无法跟上国家发展的脚步,由此所造成的社会问题、环境问题尤为严重。实时快速的城市交通拥堵检测,成为目前解决城市交通拥堵问题的一个主要手段。利用现在技术手段实时收集交通数据,构建交通拥堵检测模型,从而检测出城市交通拥堵的发生,为城市交通处理系统提供处理依据,方便交通拥堵问题得到及时的解决。近年来对于城市交通拥堵检测方案的研究发展迅速,依靠现代先进的交通检测、监测设备,能够快速的收集相关交通数据,利用各种特征提取算法来提取交通车辆速度变化等拥堵特征,从而设计各种拥堵检测方案,但是面对越来越多的交通原始数据以及低效的数据收集手段,仅仅的依靠车辆速度等少数交通数据特征已经无法得到及时准确的交通拥堵检测结果。为了能够更及时准确的检测出城市交通拥堵的发生,本文提出了基于交通流数据的城市交通拥堵检测方案。通过对交通流数据进行分类,提取多维拥堵特征,构建出三层拥堵检测模型,采用触发式的拥堵检测过程,减少检测过程的时间消耗,并对拥堵程度进行估计,更准确的检测过交通拥堵的发生。同时本文还介绍了另外一种目前快速发展的城市交通拥堵检测技术——VANET(车辆自组织网络),通过对该技术的研究探索未来解决城市交通拥堵问题的方向。最后利用模拟仿真实验评估了两种拥堵检测方案的性能,并对以后城市交通拥堵检测研究方向进行了阐述。
[Abstract]:At present, the problem of urban traffic congestion has become one of the problems hindering the development of world economy and the construction of urban modernization. Because of the rapid economic development in China, urban construction and road network construction can not keep up with the pace of national development, resulting in social problems, environmental problems are particularly serious. Real-time and fast detection of urban traffic congestion has become a major means to solve the problem of urban traffic congestion. In order to detect the occurrence of urban traffic congestion and provide the basis for urban traffic processing system, it is convenient to solve the traffic congestion problem in time by collecting traffic data in real time and constructing a traffic congestion detection model. In recent years, the research on urban traffic congestion detection schemes has developed rapidly. Relying on modern and advanced traffic detection and monitoring equipment, it can quickly collect relevant traffic data. Various feature extraction algorithms are used to extract traffic congestion features such as vehicle speed change, so as to design a variety of congestion detection schemes, but in the face of more and more traffic raw data and inefficient data collection means, Only relying on the vehicle speed and other few traffic data features can not get a timely and accurate traffic congestion detection results. In order to detect urban traffic congestion more timely and accurately, this paper proposes a traffic congestion detection scheme based on traffic flow data. By classifying the traffic flow data, extracting the multi-dimensional congestion characteristics, constructing a three-layer congestion detection model, adopting the trigger congestion detection process, reducing the time consumption of the detection process, and estimating the congestion degree. More accurate detection of traffic jams. At the same time, this paper also introduces another rapid development of urban traffic congestion detection technology-VANET (vehicle Ad Hoc Network), through the research of this technology to explore the future direction of solving urban traffic congestion problem. Finally, the performance of two traffic congestion detection schemes is evaluated by simulation experiments, and the future research direction of urban traffic congestion detection is described.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491

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