城市道路交通流预测与状态判别关键技术研究

发布时间：2018-04-10 17:02

本文选题：交通流预测 + 多模型融合　；参考：《华南理工大学》2014年硕士论文

【摘要】：随着我国经济的持续稳步快速发展，城市化进程的步伐日益加快，城市道路交通基础设施供给速度的缓慢与城市的交通需求的飞速发展之间的矛盾日益突出，交通拥堵问题已经严重阻碍了城市的可持续发展，对人们的日常生活与工作造成了严重影响。国内外发展实践表明，在现有交通设施的基础上通过更加信息化、智能化的管理系统来提高交通管理水平，是从根本上解决城市道路交通问题的有效手段。南沙区智能交通管控平台项目以“提高管理水平、保障城市畅通、改善交通秩序、提升服务水平”为目标。城市道路交通流的精确预测以及交通状态判别是该系统中的重要组成部分。本文选题来自南沙区智能交通管控平台项目。本文研究的重点是短时交通流的预测以及道路交通状态的判别这两方面，，本文研究的目的是为智能交通管控平台提供有力的技术支持，使得管理更具有科学性。主要包括以下几方面的研究：（1）根据交通流预测以及状态判别的需要，提出了交通数据预处理的方法。文章简要介绍了故障数据的识别以及处理技术，通过预处理保证了数据的质量，使得交通流预测以及状态判别结果的准确性有所保证。（2）前期的大量研究表明，仅仅选用单一预测模型很难满足预期的精度要求。本文在以BP神经网络进行短时交通流预测研究的基础上，详述了其建模过程，并对其中的不足给出了改进的方法。然后选用小波神经网络建立短时交通流预测模型，最后提出了基于数据融合的多模型融合预测算法。通过运用MATLAB进行仿真对比，从而验证了多模型融合预测算法的有效性。（3）根据道路交通状态的模糊不确定性，本文通过模糊综合评价的方法来对道路交通状态进行判别，并选用模糊层次分析法对其中的权重系数进行确定，以此降低人为因素的影响,最后通过实例分析验证了算法的可行性。（4）以南沙区智能交通管控平台项目为基础，介绍了其中交通流预测与状态判别的实现过程。为大中城市解决相关问题提供了参考。
[Abstract]:With the steady and rapid development of our economy, the pace of urbanization is accelerating day by day, and the contradiction between the slow supply of urban road traffic infrastructure and the rapid development of urban traffic demand is becoming increasingly prominent.The problem of traffic congestion has seriously hindered the sustainable development of the city and has a serious impact on people's daily life and work.The development practice at home and abroad shows that improving the level of traffic management through more information and intelligent management system on the basis of existing traffic facilities is an effective means to fundamentally solve urban road traffic problems.Nansha intelligent traffic control platform project aims to improve the management level, ensure the smooth flow of the city, improve the traffic order and improve the service level.The accurate prediction of urban road traffic flow and the identification of traffic state are important parts of the system.This paper selected topics from the Nansha District Intelligent Transportation Control platform project.This paper focuses on the prediction of short-term traffic flow and the identification of road traffic status. The purpose of this study is to provide strong technical support for the intelligent traffic control platform and make the management more scientific.Mainly includes the following aspects of research:1) according to the need of traffic flow prediction and state discrimination, a method of traffic data preprocessing is proposed.This paper briefly introduces the identification and processing technology of the fault data. The quality of the data is guaranteed by preprocessing, which ensures the accuracy of the traffic flow prediction and the result of state discrimination.2) A large number of previous studies have shown that it is difficult to meet the expected accuracy requirements by using a single prediction model.Based on the research of short time traffic flow prediction based on BP neural network, the modeling process of BP neural network is described in detail, and an improved method is given for its shortcomings.Then wavelet neural network is used to build a short-term traffic flow prediction model. Finally, a multi-model fusion prediction algorithm based on data fusion is proposed.The effectiveness of the multi-model fusion prediction algorithm is verified by simulation and comparison with MATLAB.3) according to the fuzzy uncertainty of the road traffic state, this paper uses the fuzzy comprehensive evaluation method to distinguish the road traffic state, and selects the fuzzy analytic hierarchy process (FAHP) to determine the weight coefficient of the road traffic state.In order to reduce the influence of human factors, the feasibility of the algorithm is verified by an example.4) based on the intelligent traffic control platform project in Nansha District, the realization process of traffic flow prediction and state discrimination is introduced.It provides a reference for large and medium cities to solve related problems.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.1

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