城市道路偶发性局部拥堵快速识别办法研究

发布时间：2018-06-10 12:05

本文选题：城市道路 + 偶发性拥堵　；参考：《长沙理工大学》2014年硕士论文

【摘要】：交通因为其巨大的作用,在城市发展中占据着难以忽视的地位,然而城市交通设施的建设相对比较缓慢,就造成了城市交通已经不能满足人们强烈的需求,交通拥堵已经成为了城市亟待解决的难题。常发性拥堵具有一定的规律性,可以通过日常的规律统计可以对其进行识别。而偶发性拥堵则是随机产生的,具有相当大的偶然性,正是因为难以确定其发生的时间和地点,这就给拥堵的疏散带来了很大的难度,而且发生交通事件给人们生命和财产安全带来了非常大的威胁。许多国内外学者对偶发性拥堵的识别和控制进行了大量的研究,但是效果都不佳,主要因为识别受到了快速性和有效性方面的局限。因此,必须对偶发性拥堵的识别问题展开研究,以能够及时控制拥堵。本文在很多前人对交通的研究基础上,展开了对偶发性拥堵的研究。首先对城市道路交通拥堵进行了概述,提出了交通拥堵的含义以及在一定程度上分析了造成交通拥堵的原因,分析了城市交通拥挤机理,介绍了交通流的几种常用参数,全面分析了交通流特性和交通网络特性。再重点介绍了对交通拥堵的几种经典判别算法以及算法的判别条件,全面分析比较了这几种算法的识别性能。最后在人工神经网络的基础上提出了BP神经网络的理念,通过对网络模型的改进用于对城市道路偶发性拥堵的识别,运用采集得到的数据通过VISSIM仿真软件进行交通仿真,将得到的交通流量、平均车速和占有率三个交通流参数数据输入到网络模型,以用于识别偶发性交通拥堵,通过实例分析说明了该方法具有较高的准确率,并且具有很强的实用性。本文通过对城市道路偶发性局部拥堵进行的深入研究,在一定程度上可以为偶发性交通拥堵的管理控制和制定合理的疏散方法提供了帮助。
[Abstract]:Traffic plays an important role in urban development. However, the construction of urban transportation facilities is relatively slow, resulting in the city traffic can no longer meet the strong needs of people. Traffic congestion has become an urgent problem in cities. Regular congestion has certain regularity, which can be recognized by daily statistics. The accidental congestion is random and has a considerable chance. It is precisely because it is difficult to determine the time and place of its occurrence, which brings great difficulty to the evacuation of the congestion. Moreover, traffic accidents have brought a great threat to the safety of people's lives and property. Many scholars at home and abroad have done a lot of research on the identification and control of accidental congestion, but the effect is not good, mainly because the recognition is limited in speed and effectiveness. Therefore, it is necessary to study the identification of accidental congestion in order to control congestion in time. On the basis of many previous researches on traffic, this paper studies accidental congestion. Firstly, this paper summarizes the traffic congestion on urban roads, puts forward the meaning of traffic congestion and analyzes the causes of traffic congestion to a certain extent, analyzes the mechanism of urban traffic congestion, and introduces several common parameters of traffic flow. The characteristics of traffic flow and traffic network are analyzed. Several classical discriminant algorithms for traffic congestion and their discriminant conditions are introduced, and the recognition performance of these algorithms is analyzed and compared comprehensively. Finally, on the basis of artificial neural network, the idea of BP neural network is put forward. The improved network model is used to identify the accidental congestion on urban roads, and the collected data is used to simulate traffic through Visual IM simulation software. The data of traffic flow, average speed and occupancy are input into the network model to identify accidental traffic congestion. The example shows that the method has a high accuracy. And has very strong practicability. In this paper, through the in-depth study of accidental local congestion on urban roads, to some extent, it can provide help for the management and control of accidental traffic congestion and the formulation of reasonable evacuation methods.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491

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