本文選題：城市道路 + 偶發(fā)性擁堵��； 參考：《長沙理工大學(xué)》2014年碩士論文

【摘要】：交通因?yàn)槠渚薮蟮淖饔?在城市發(fā)展中占據(jù)著難以忽視的地位,然而城市交通設(shè)施的建設(shè)相對比較緩慢,就造成了城市交通已經(jīng)不能滿足人們強(qiáng)烈的需求,交通擁堵已經(jīng)成為了城市亟待解決的難題。常發(fā)性擁堵具有一定的規(guī)律性,可以通過日常的規(guī)律統(tǒng)計(jì)可以對其進(jìn)行識別。而偶發(fā)性擁堵則是隨機(jī)產(chǎn)生的,具有相當(dāng)大的偶然性,正是因?yàn)殡y以確定其發(fā)生的時間和地點(diǎn),這就給擁堵的疏散帶來了很大的難度,而且發(fā)生交通事件給人們生命和財(cái)產(chǎn)安全帶來了非常大的威脅。許多國內(nèi)外學(xué)者對偶發(fā)性擁堵的識別和控制進(jìn)行了大量的研究,但是效果都不佳,主要因?yàn)樽R別受到了快速性和有效性方面的局限。因此,必須對偶發(fā)性擁堵的識別問題展開研究,以能夠及時控制擁堵。本文在很多前人對交通的研究基礎(chǔ)上,展開了對偶發(fā)性擁堵的研究。首先對城市道路交通擁堵進(jìn)行了概述,提出了交通擁堵的含義以及在一定程度上分析了造成交通擁堵的原因,分析了城市交通擁擠機(jī)理,介紹了交通流的幾種常用參數(shù),全面分析了交通流特性和交通網(wǎng)絡(luò)特性。再重點(diǎn)介紹了對交通擁堵的幾種經(jīng)典判別算法以及算法的判別條件,全面分析比較了這幾種算法的識別性能。最后在人工神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)上提出了BP神經(jīng)網(wǎng)絡(luò)的理念,通過對網(wǎng)絡(luò)模型的改進(jìn)用于對城市道路偶發(fā)性擁堵的識別,運(yùn)用采集得到的數(shù)據(jù)通過VISSIM仿真軟件進(jìn)行交通仿真,將得到的交通流量、平均車速和占有率三個交通流參數(shù)數(shù)據(jù)輸入到網(wǎng)絡(luò)模型,以用于識別偶發(fā)性交通擁堵,通過實(shí)例分析說明了該方法具有較高的準(zhǔn)確率,并且具有很強(qiáng)的實(shí)用性。本文通過對城市道路偶發(fā)性局部擁堵進(jìn)行的深入研究,在一定程度上可以為偶發(fā)性交通擁堵的管理控制和制定合理的疏散方法提供了幫助。
[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.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491

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本文編號：2003160