本文選題：城市公共交通 + 預(yù)測��； 參考：《重慶交通大學(xué)》2014年碩士論文

【摘要】：隨著社會的不斷進(jìn)步，交通擁堵已經(jīng)成為21世紀(jì)各大城市必須面臨的重大難題，而解決城市交通擁堵的重要途徑就是大力發(fā)展公共交通。公交車輛作為公共交通的基本組成部分在緩解城市交通擁堵，提高城市道路利用率上發(fā)揮了極大的功效。城市公交信息服務(wù)系統(tǒng)是實(shí)現(xiàn)城市智能公交系統(tǒng)的基礎(chǔ)，它以“為出行者出行服務(wù)”為目的。因此，必須旅客的角度出發(fā)，發(fā)布旅客重點(diǎn)關(guān)心的出行信息。通過對乘客進(jìn)行實(shí)際調(diào)查后得知，其中公交車輛到站信息是出行最為關(guān)心的信息之一。所以，集中優(yōu)勢力量，利用所獲得的交通采集數(shù)據(jù)，分析和發(fā)布出行者最為關(guān)心的公交車到站信息是實(shí)現(xiàn)城市公共交通系統(tǒng)信息化的重要內(nèi)容。 論文首先對公交車輛定位信息的采集系統(tǒng)進(jìn)行了分析，介紹了GPS定位的基本原理，對所采集到的GPS定位信息的原始數(shù)據(jù)所包含的內(nèi)容以及每個(gè)參數(shù)所對應(yīng)的實(shí)際意義進(jìn)行了詳細(xì)的說明。隨后對公交定位信息產(chǎn)生誤差的原因從多個(gè)角度進(jìn)行了研究，并從其誤差產(chǎn)生的根源出發(fā)，提出了相對應(yīng)的補(bǔ)償方案。接著，為了提高公交到站信息預(yù)測的精度，提出公交實(shí)時(shí)數(shù)據(jù)匹配所采用的方法，其中包括對公交線路路段的劃分方法、公交路線的線性化處理方法以及通過公交定位數(shù)據(jù)中的方向角一項(xiàng)來對公交車輛行駛方向進(jìn)行判別的方法。 在此基礎(chǔ)之上，對公交到站時(shí)間的影響因素進(jìn)行了分析，，依據(jù)公交車輛的行駛特點(diǎn)，將其總的行駛時(shí)間分為了三個(gè)部分，分別是路段行駛時(shí)間、交叉口延誤時(shí)間與到站�？繒r(shí)間，并就每個(gè)部分的影響因子的重要程度和產(chǎn)生影響的原因就行了研究。針對各種影響因素的差異，提出利用BP神經(jīng)網(wǎng)絡(luò)模型來對公交到站時(shí)間進(jìn)行預(yù)測，并對原有的神經(jīng)網(wǎng)絡(luò)數(shù)據(jù)訓(xùn)練算法進(jìn)行優(yōu)化，以提高公交到站時(shí)間預(yù)測的精度和降低模型訓(xùn)練所需要的時(shí)間。 最后，利用重慶市601路公交車的實(shí)時(shí)定位數(shù)據(jù)進(jìn)行實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明論文提出的優(yōu)化模型樣本訓(xùn)練速度有了較大的改善，預(yù)測精度也在可接受范圍內(nèi)，在實(shí)際應(yīng)用時(shí)能有較好的發(fā)揮。
[Abstract]:With the continuous progress of society, traffic congestion has become a major problem that every major city must face in the 21st century, and the important way to solve urban traffic congestion is to develop public transportation. As a basic part of public transport, public transport vehicles play a great role in alleviating urban traffic congestion and improving the utilization ratio of urban roads. The urban public transport information service system is the foundation of realizing the urban intelligent public transport system, which aims at serving the travelers. Therefore, it is necessary to release the travel information which the passengers are concerned about from the point of view of the passengers. Through the actual investigation of passengers, we know that bus arrival information is one of the most concerned information. Therefore, it is an important content to realize the informatization of urban public transportation system by concentrating the advantages, using the traffic data collected, analyzing and publishing the bus arrival information that the travelers are most concerned about. Firstly, the paper analyzes the collection system of public transportation vehicle positioning information, and introduces the basic principle of GPS positioning. The contents of the original data of the collected GPS location information and the practical meaning of each parameter are explained in detail. Then, the causes of the errors of public transportation positioning information are studied from several angles, and the corresponding compensation scheme is put forward from the root of the errors. Then, in order to improve the accuracy of bus arrival information prediction, the method of bus real-time data matching is put forward, including the method of dividing the bus route. The linearization method of bus route and the method to distinguish the driving direction of public transport vehicle by the direction angle in the location data of public transport. On this basis, the influence factors of bus arrival time are analyzed. According to the driving characteristics of public transport vehicles, the total travel time is divided into three parts. The intersections delay time and arrival time, and the importance of each part of the impact factors and the causes of the impact are studied. According to the difference of various factors, the BP neural network model is proposed to predict the bus arrival time, and the original neural network data training algorithm is optimized. In order to improve the accuracy of bus arrival time prediction and reduce the time required for model training. Finally, using the real-time positioning data of the 601 bus in Chongqing, the experimental results show that the training speed of the optimized model has been greatly improved, and the prediction accuracy is within the acceptable range. In the actual application can have a better play.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;U491.17

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