發(fā)布時(shí)間：2019-06-29 18:28

【摘要】：當(dāng)前我國城市現(xiàn)代化進(jìn)程不斷推進(jìn),然而交通擁堵問題日益突顯,交通擁堵已經(jīng)成為困擾各個(gè)大中城市的嚴(yán)重問題之一。城市交通擁堵帶來的危害性主要有兩個(gè)方面:一是交通擁堵發(fā)生時(shí)造成的時(shí)間延誤和能源浪費(fèi),給社會帶來了巨大的經(jīng)濟(jì)損失。據(jù)中科院專家統(tǒng)計(jì)得出的數(shù)據(jù),我國每天因城市交通擁堵造成的經(jīng)濟(jì)損失可達(dá)約10億元。二是當(dāng)車速過低時(shí),汽車尾氣污染程度大大增加,與此同時(shí)會產(chǎn)生大量噪聲,使空氣質(zhì)量以及城市環(huán)境質(zhì)量急劇下降,進(jìn)而對市民的身心健康造成嚴(yán)重危害,降低了市民生活水平。所以,對復(fù)雜的交通狀況進(jìn)行有效的預(yù)測是當(dāng)前亟須解決的重要問題。近年來,越來越多的學(xué)者開始致力于智能交通系統(tǒng)的研究,提出多種交通擁堵預(yù)測方法。常見的交通擁堵預(yù)測方法主要是基于各類數(shù)學(xué)模型,并且大多只對單一道路單一時(shí)刻進(jìn)行預(yù)測。由于交通系統(tǒng)復(fù)雜多變的特性,這類方法往往考慮的參數(shù)并不全面,同時(shí)沒有考慮到交通擁堵事件的時(shí)序性,無法很好地適應(yīng)實(shí)際情況。在交通系統(tǒng)中,各個(gè)路段發(fā)生擁堵往往遵循一定的因果關(guān)系,同時(shí)考慮到交通擁堵事件的時(shí)序性,本文提出一種基于時(shí)序關(guān)聯(lián)規(guī)則挖掘的交通擁堵預(yù)測方法,先利用遺傳算法挖掘出時(shí)序關(guān)聯(lián)規(guī)則,再將這些關(guān)聯(lián)規(guī)則作為數(shù)據(jù)樣本構(gòu)建分類器,以達(dá)到對交通擁堵預(yù)測的目的。本方法采用進(jìn)化算法的思想,有效避免了傳統(tǒng)方法需要確定參數(shù)過多的弊端,算法更為貼近實(shí)際生活情況,能夠有效預(yù)測交通擁堵,為及時(shí)緩解城市交通壓力、降低道路擁堵發(fā)生率、提高道路通暢度、保障高效快捷地出行提供了參考依據(jù)。
[Abstract]:At present, the process of urban modernization in our country is advancing, but the problem of traffic congestion is becoming more and more obvious, and the traffic jam has become one of the serious problems in the large and medium-sized cities. The harmfulness of urban traffic congestion mainly includes two aspects: one is the time delay and energy waste caused by the traffic jam, and brings great economic loss to the society. According to the data from the experts of the Chinese Academy of Sciences, the economic loss caused by the traffic congestion of the city is about 1 billion yuan a day. Secondly, when the vehicle speed is too low, the pollution degree of the automobile exhaust is greatly increased, and meanwhile, a large amount of noise is generated, so that the air quality and the urban environmental quality are greatly reduced, and further serious harm to the physical and mental health of the citizen is caused, and the living standard of the citizen is reduced. Therefore, the effective prediction of complex traffic conditions is an important problem to be solved at present. In recent years, more and more scholars have begun to study the intelligent transportation system, and put forward a variety of traffic jam prediction methods. The common traffic congestion prediction method is mainly based on various mathematical models, and most of the traffic jam prediction methods are only predicted at a single time of a single road. Due to the complex and changeable nature of the traffic system, the parameters often taken into account are not comprehensive, and the timing of the traffic jam events is not taken into account, and the actual situation cannot be well adapted. In the traffic system, the traffic jam of each road section often follows a certain causal relationship, while taking into account the timing of the traffic jam event, this paper proposes a traffic jam prediction method based on time-series association rule mining, which first uses the genetic algorithm to mine the time-series association rules, The correlation rules are used as data samples to construct a classifier so as to achieve the purpose of predicting the traffic jam. The method adopts the idea of an evolutionary algorithm, effectively avoids the defect that the traditional method needs to determine the excessive parameters, the algorithm is more close to the actual living condition, the traffic jam can be effectively predicted, the traffic pressure can be relieved in time, the traffic congestion rate is reduced, the road smoothness is improved, And provides a reference basis for ensuring the high-efficiency and fast travel.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.14;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 胡啟洲;劉英舜;郭唐儀;;城市交通擁堵態(tài)勢監(jiān)控的時(shí)空分布形態(tài)識別模型[J];交通運(yùn)輸系統(tǒng)工程與信息;2012年03期

2 李春英;湯志康;曹元大;;多分類器組合的交通擁堵預(yù)測模型研究[J];計(jì)算機(jī)工程與設(shè)計(jì);2010年23期

3 榮岡;劉進(jìn)鋒;顧海杰;;數(shù)據(jù)庫中動態(tài)關(guān)聯(lián)規(guī)則的挖掘[J];控制理論與應(yīng)用;2007年01期

4 陳濤;陳森發(fā);;道路交通無序擁擠控制模型的研究[J];公路交通科技;2006年11期

5 吳兵;李林波;;交通擁擠的進(jìn)化動態(tài)分析[J];中國公路學(xué)報(bào);2006年03期

6 莊斌;楊曉光;李克平;;道路交通擁擠事件判別準(zhǔn)則與檢測算法[J];中國公路學(xué)報(bào);2006年03期

7 雷勝;;城市交通流量智能組合預(yù)測方法研究[J];西華大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年02期

8 李碩;高速公路常發(fā)性交通擁擠路段實(shí)時(shí)判定與跟蹤研究[J];中南公路工程;2005年01期

9 戴紅;基于模糊模式識別的城市道路交通狀態(tài)檢測算法[J];吉林工程技術(shù)師范學(xué)院學(xué)報(bào);2005年03期

10 楊兆升,楊慶芳,馮金巧;基于模糊綜合推理的道路交通事件識別算法[J];公路交通科技;2003年04期

相關(guān)會議論文 前1條

1 高晗;裴玉龍;;基于小波包變換的道路交通擁擠事件檢測方法[A];2007年中國智能自動化會議論文集[C];2007年

相關(guān)博士學(xué)位論文 前1條

1 馮金巧;城市道路交通擁擠預(yù)測關(guān)鍵技術(shù)研究[D];吉林大學(xué);2008年

相關(guān)碩士學(xué)位論文 前3條

1 黃國浪;城市交通擁堵的識別與預(yù)測[D];長安大學(xué);2014年

2 屈健;城市主干道交通擁堵預(yù)測方法研究[D];西南交通大學(xué);2012年

3 王江鋒;高速公路交通擁擠狀態(tài)自動識別方法研究[D];吉林大學(xué);2004年

，

本文編號：2508018