交通誘導系統(tǒng)中車流量預測與路徑誘導算法研究
發(fā)布時間：2018-11-20 22:01
【摘要】：近年來,隨著社會經(jīng)濟的發(fā)展,交通擁堵、交通事故等交通問題日益突出。為了應對這些問題,交通誘導系統(tǒng)被引入到城市的交通管理中,并且得到了快速的發(fā)展。其中車流量的短時預測與路徑誘導是交通誘導系統(tǒng)的關(guān)鍵技術(shù)。對未來某時刻的車流量進行合理的預測,并給出合理的誘導路徑,不僅能夠為交通管理部門提供決策依據(jù),而且能方便出行人出行,避免進入擁堵路段,節(jié)約出行時間。由于城市路網(wǎng)交通狀態(tài)的時變性和復雜性,很難精確的描述其變化規(guī)律,因此研究實時準確的車流量預測與路徑誘導算法具有十分重要的意義。本文通過對城市道路交通數(shù)據(jù)的分析,以城市道路網(wǎng)及交叉口為研究對象,對無檢測器交叉口的車流量預測、路網(wǎng)車流量預測以及路徑誘導算法進行了研究,論文主要研究工作包括以下幾個方面:1.介紹了交通數(shù)據(jù)采集方法和交通數(shù)據(jù)的特性,分析了車流量預測的可行性,闡述了異常數(shù)據(jù)的識別與修復方法。采用時間序列分析法和Lyapunov指數(shù)分析并確定了車流量的可預測性,并使用歷史趨勢數(shù)據(jù)與實測數(shù)據(jù)的加權(quán)估計值對異常數(shù)據(jù)進行了修復。2.針對城市路網(wǎng)中某些交叉口沒有檢測器或者檢測器故障的問題,在分析和研究幾種常用無檢測器交叉口車流量預測方法的基礎上,提出了一種基于模糊C均值聚類的無檢測器交叉口車流量預測方法。該方法通過模糊聚類將相關(guān)聯(lián)的交叉口聚為同一簇,然后使用多元線性回歸方法完成了對車流量的預測。實驗結(jié)果驗證了算法的有效性。3.通過對車流量預測模型的研究,給出了基于支持向量機回歸方法的短時車流量預測模型,并針對SVR的參數(shù)學習速度慢的問題,研究了遺傳算法的全局搜索特性,采用遺傳算法優(yōu)化SVR的參數(shù)選擇,最后實驗驗證了GA-SVR模型的合理性。4.研究了幾種傳統(tǒng)的求解最優(yōu)路徑算法的原理,分析了它們的優(yōu)缺點,在此基礎上,引入了一種模擬進化的蟻群算法,對交通最優(yōu)路徑進行選擇。該算法的主要原理是蟻群依靠與路徑長度有關(guān)的信息素來尋找最優(yōu)路徑。同時針對蟻群算法的缺點對其進行改進,并用改進的蟻群算法與遺傳算法進行實驗對比分析,驗證了算法的有效性。5.利用GA-SVR預測模型與蟻群最短路徑誘導算法的研究結(jié)論,設計并完成了基于J2EE框架的交通誘導系統(tǒng)。
[Abstract]:In recent years, with the development of social economy, traffic congestion, traffic accidents and other traffic problems have become increasingly prominent. In order to deal with these problems, traffic guidance system has been introduced into urban traffic management and developed rapidly. Among them, the short-time prediction and route guidance of traffic flow are the key technologies of traffic guidance system. It can not only provide the decision basis for the traffic management department, but also facilitate the travel, avoid entering the congested section and save the travel time by reasonably forecasting the traffic flow at a certain time in the future. Because of the time variation and complexity of the traffic state of urban road network, it is difficult to describe its changing law accurately, so it is very important to study the real-time and accurate vehicle flow prediction and route guidance algorithm. Based on the analysis of urban road traffic data and taking the urban road network and intersection as the research object, this paper studies the vehicle flow prediction, road network traffic flow prediction and path guidance algorithm of the intersection without detector. The main research work includes the following aspects: 1. This paper introduces the methods of traffic data acquisition and the characteristics of traffic data, analyzes the feasibility of traffic flow prediction, and expounds the methods of identifying and repairing abnormal data. Time series analysis and Lyapunov index analysis are used to determine the predictability of traffic flow, and the weighted estimates of historical trend data and measured data are used to repair the abnormal data. 2. In view of the problem that some intersections in urban road network do not have detectors or fault detectors, based on the analysis and study of several commonly used traffic flow prediction methods of intersections without detectors, In this paper, a new method of traffic flow prediction at intersections without detector based on fuzzy C-means clustering is proposed. In this method, the associated intersections are clustered into the same cluster by fuzzy clustering, and the multivariate linear regression method is used to predict the traffic flow. The experimental results show that the algorithm is effective. Based on the research of traffic flow prediction model, a short-term traffic flow prediction model based on support vector machine regression method is presented. The global search characteristic of genetic algorithm is studied in view of the slow learning speed of parameters in SVR. Genetic algorithm is used to optimize the parameter selection of SVR. Finally, the rationality of GA-SVR model is verified by experiments. 4. 4. In this paper, the principles of several traditional optimal path algorithms are studied, and their advantages and disadvantages are analyzed. On this basis, a simulated evolutionary ant colony algorithm is introduced to select the optimal path of traffic. The main principle of the algorithm is that ant colony depends on the information related to path length to find the optimal path. At the same time, aiming at the shortcomings of ant colony algorithm, the improved ant colony algorithm and genetic algorithm are compared and analyzed, and the validity of the algorithm is verified. A traffic guidance system based on J2EE framework is designed and completed by using the GA-SVR prediction model and the conclusion of the ant colony shortest path guidance algorithm.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495
本文編號：2346172
[Abstract]:In recent years, with the development of social economy, traffic congestion, traffic accidents and other traffic problems have become increasingly prominent. In order to deal with these problems, traffic guidance system has been introduced into urban traffic management and developed rapidly. Among them, the short-time prediction and route guidance of traffic flow are the key technologies of traffic guidance system. It can not only provide the decision basis for the traffic management department, but also facilitate the travel, avoid entering the congested section and save the travel time by reasonably forecasting the traffic flow at a certain time in the future. Because of the time variation and complexity of the traffic state of urban road network, it is difficult to describe its changing law accurately, so it is very important to study the real-time and accurate vehicle flow prediction and route guidance algorithm. Based on the analysis of urban road traffic data and taking the urban road network and intersection as the research object, this paper studies the vehicle flow prediction, road network traffic flow prediction and path guidance algorithm of the intersection without detector. The main research work includes the following aspects: 1. This paper introduces the methods of traffic data acquisition and the characteristics of traffic data, analyzes the feasibility of traffic flow prediction, and expounds the methods of identifying and repairing abnormal data. Time series analysis and Lyapunov index analysis are used to determine the predictability of traffic flow, and the weighted estimates of historical trend data and measured data are used to repair the abnormal data. 2. In view of the problem that some intersections in urban road network do not have detectors or fault detectors, based on the analysis and study of several commonly used traffic flow prediction methods of intersections without detectors, In this paper, a new method of traffic flow prediction at intersections without detector based on fuzzy C-means clustering is proposed. In this method, the associated intersections are clustered into the same cluster by fuzzy clustering, and the multivariate linear regression method is used to predict the traffic flow. The experimental results show that the algorithm is effective. Based on the research of traffic flow prediction model, a short-term traffic flow prediction model based on support vector machine regression method is presented. The global search characteristic of genetic algorithm is studied in view of the slow learning speed of parameters in SVR. Genetic algorithm is used to optimize the parameter selection of SVR. Finally, the rationality of GA-SVR model is verified by experiments. 4. 4. In this paper, the principles of several traditional optimal path algorithms are studied, and their advantages and disadvantages are analyzed. On this basis, a simulated evolutionary ant colony algorithm is introduced to select the optimal path of traffic. The main principle of the algorithm is that ant colony depends on the information related to path length to find the optimal path. At the same time, aiming at the shortcomings of ant colony algorithm, the improved ant colony algorithm and genetic algorithm are compared and analyzed, and the validity of the algorithm is verified. A traffic guidance system based on J2EE framework is designed and completed by using the GA-SVR prediction model and the conclusion of the ant colony shortest path guidance algorithm.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495
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