發(fā)布時間：2019-06-29 18:28

【摘要】：當前我國城市現(xiàn)代化進程不斷推進,然而交通擁堵問題日益突顯,交通擁堵已經(jīng)成為困擾各個大中城市的嚴重問題之一。城市交通擁堵帶來的危害性主要有兩個方面:一是交通擁堵發(fā)生時造成的時間延誤和能源浪費,給社會帶來了巨大的經(jīng)濟損失。據(jù)中科院專家統(tǒng)計得出的數(shù)據(jù),我國每天因城市交通擁堵造成的經(jīng)濟損失可達約10億元。二是當車速過低時,汽車尾氣污染程度大大增加,與此同時會產(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,進而對市民的身心健康造成嚴重危害,降低了市民生活水平。所以,對復雜的交通狀況進行有效的預測是當前亟須解決的重要問題。近年來,越來越多的學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測方法。常見的交通擁堵預測方法主要是基于各類數(shù)學模型,并且大多只對單一道路單一時刻進行預測。由于交通系統(tǒng)復雜多變的特性,這類方法往往考慮的參數(shù)并不全面,同時沒有考慮到交通擁堵事件的時序性,無法很好地適應實際情況。在交通系統(tǒng)中,各個路段發(fā)生擁堵往往遵循一定的因果關系,同時考慮到交通擁堵事件的時序性,本文提出一種基于時序關聯(lián)規(guī)則挖掘的交通擁堵預測方法,先利用遺傳算法挖掘出時序關聯(lián)規(guī)則,再將這些關聯(lián)規(guī)則作為數(shù)據(jù)樣本構建分類器,以達到對交通擁堵預測的目的。本方法采用進化算法的思想,有效避免了傳統(tǒng)方法需要確定參數(shù)過多的弊端,算法更為貼近實際生活情況,能夠有效預測交通擁堵,為及時緩解城市交通壓力、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.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.14;TP311.13

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