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

【摘要】：交通因為其巨大的作用,在城市發(fā)展中占據(jù)著難以忽視的地位,然而城市交通設施的建設相對比較緩慢,就造成了城市交通已經不能滿足人們強烈的需求,交通擁堵已經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)計可以對其進行識別。而偶發(fā)性擁堵則是隨機產生的,具有相當大的偶然性,正是因為難以確定其發(fā)生的時間和地點,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產安全帶來了非常大的威脅。許多國內外學者對偶發(fā)性擁堵的識別和控制進行了大量的研究,但是效果都不佳,主要因為識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絡特性。再重點介紹了對交通擁堵的幾種經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。最后在人工神經網絡的基礎上提出了BP神經網絡的理念,通過對網絡模型的改進用于對城市道路偶發(fā)性擁堵的識別,運用采集得到的數(shù)據(jù)通過VISSIM仿真軟件進行交通仿真,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)輸入到網絡模型,以用于識別偶發(fā)性交通擁堵,通過實例分析說明了該方法具有較高的準確率,并且具有很強的實用性。本文通過對城市道路偶發(fā)性局部擁堵進行的深入研究,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.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491

【參考文獻】

相關期刊論文 前2條

1 靳文舟,張杰;最大似然思想和最大熵思想在交通狀態(tài)分析中的一致性[J];公路交通科技;2001年04期

2 張秀媛,達慶東,張國伍;公路自動事件檢測技術[J];系統(tǒng)工程理論與實踐;2001年06期

相關碩士學位論文 前1條

1 孫莉芬;城市交通擁擠疏導決策支持系統(tǒng)的研究[D];華中科技大學;2005年

，

本文編號：2003160