【作者】 牛爽；

【导师】 吴建平；

【作者基本信息】 北京交通大学 ， 智能交通工程， 2008， 硕士

【摘要】 随着ITS(Intelligence Transportation System,智能交通系统)的进步和发展,人们逐渐认识到交通对于日常生活以及城市建设和发展的重要性,越来越多研究者将先进的技术和手段融合到交通管理和控制领域中,希望可以借此优化和改善城市道路交通状况,交通异常事件检测就是其中的重要内容。目前,国内外存在的交通异常事件检测的模型和算法很多,体系庞大,各种算法针对不同的检测器和道路环境具有其不同的优缺点,适用范围也不同。另一方面,随着浮动车技术的发展,国外越来越多的研究者看到了它在交通信息采集方面的潜力,开始开发和研究其应用价值,并将它应用到交通异常事件检测中。这使得城市道路交通异常事件检测有了新的方向。但在国内对于浮动车技术的开发和应用却并不多,主要集中在北京、杭州等大城市中,有待进一步的发展。因此,将浮动车技术融合到交通异常事件检测中,将使城市道路的交通异常事件检测有更长足的发展。基于上述内容,本论文的研究目的是利用浮动车采集的交通信息数据,通过有效的交通异常事件检测算法,及时发现道路上的异常事件,使有关部门获得有效信息,并做出快速处理,令交通流得到及时疏导,尽快恢复道路交通的畅通。本论文完成的主要工作包括以下五个部分:(1)本论文在大量的文献阅读和算法研究的基础上,提出了一种交通异常事件检测的综合模型,其主要由车速预测模型和预测偏差分析模型组成。(2)阐述车速预测模型——改进型ARIMA模型的基本框架,并推导了模型中相关的计算公式。(3)介绍预测偏差分析模型的基本原理,并分析了模型关键参数的确定方法。(4)对车速预测模型和预测偏差分析模型的关键计算步骤进行了程序编写,并利用程序对数据进行计算和分析。(5)利用浮动车采集的实际数据,结合Flowsim交通仿真的数据,对综合模型进行了分析和验证,并对模型有效性做出了评价。更多还原

【Abstract】 With the progress and development of ITS (Intelligence Transportation System), there is a growing awareness of the importance of traffic, as well as the daily lives of urban construction and development. Advanced technology and tools are integrated into the management and control of traffic by more and more researchers. It is wished that we can take this path to optimize and improve the urban traffic conditions. Traffic abnormal incident detection is one of the important elements in the field. At present, there are many of models and algorithms and is a huge system on traffic incident detection at home and abroad. Each algorithm has its advantages, disadvantages and different scope of application for different detector and the environment. On the other hand, with the development of floating car, more and more foreign researchers apply floating car to detect traffic abnormal incident while they discover its potential in traffic information collection and work on researching the development and application of floating car. However, in the domestic, the development and application for floating car mainly concentrate in Beijing, Hangzhou, and other big cities, and further development is pending. Therefore, there will be a more substantial development on the urban road traffic abnormal incident detection after floating car technology is integrated into traffic abnormal incident detection.The purpose of this paper is to discover unusual events on the road using data collected by floating cars and effective incident detection algorithm in order to take an effective and rapid treatment to receive ease traffic flow timely and restore free trafficflow.The completed major work in this paper includes the following five sections:First of all, on the basis of a great deal of reading and researching literature and algorithm, a abnormal traffic incident detection integrated model that is mainly composed by short-term traffic flow forecasting model and prediction error analysis model is presented.Second, the basic framework of short-term traffic flow forecasting model, which is the ARIMA model to improve, is elaborated, and calculation formula related to the model is derived.Third, the basic principle of prediction error analysis model is introduced, and the key parameters of the model are analyzed.Fourth, the procedures for the key steps of model calculation are prepared and used to calculate and analyze data.Finally, with the actual data collected by floating vehicles and data obtained through Flowsim simulation, analysis and verification of the integrated model are made, and model validity is evaluated.更多还原