本文選題：智能公交 + 信息采集　； 參考：《重慶交通大學(xué)》2014年碩士論文

【摘要】：隨著經(jīng)濟(jì)和社會(huì)的發(fā)展，城市車輛擁有量不斷增加，交通問題也變的日益嚴(yán)重。如何解決城市交通問題已經(jīng)成為城市發(fā)展的重要課題，在加大城市交通建設(shè)和管理的同時(shí)，人們也意識(shí)到發(fā)展城市公共交通是改善城市交通問題的重要手段之一。隨著信息技術(shù)不斷發(fā)展，以及在城市交通運(yùn)輸系統(tǒng)中有效地應(yīng)用，形成以應(yīng)用交通信息為中心的智能交通運(yùn)輸系統(tǒng)（ITS），智能公交作為ITS中最重要的部分，完善智能公交信息技術(shù)，提高采集、處理及應(yīng)用公交信息能力是公交系統(tǒng)高效運(yùn)行的保障，是提升公交服務(wù)水平和增加企業(yè)經(jīng)營(yíng)效益的前提。 本文從智能公交信息采集的實(shí)現(xiàn)技術(shù)出發(fā)，首先對(duì)當(dāng)前使用的智能公交信息采集技術(shù)及特點(diǎn)進(jìn)行了分析，其次智能公交系統(tǒng)的運(yùn)作，不僅需要信息采集子系統(tǒng)提供的信息，還必須實(shí)現(xiàn)從其它信息化子系統(tǒng)獲取相關(guān)的公交信息�，F(xiàn)有的智能公交系統(tǒng)的各信息化子系統(tǒng)是在不同時(shí)間、針對(duì)不同業(yè)務(wù)要求而開發(fā)的，導(dǎo)致業(yè)務(wù)請(qǐng)求在不同平臺(tái)之間難以實(shí)現(xiàn)調(diào)用，形成信息孤島，將大量有用公交信息封閉在一個(gè)子系統(tǒng)當(dāng)中。針對(duì)這一問題，研究了基于Web服務(wù)的SOA異構(gòu)信息系統(tǒng)集成，它將信息化子系統(tǒng)的業(yè)務(wù)功能進(jìn)行封裝，劃分為粒度不同的服務(wù)，對(duì)業(yè)務(wù)功能的調(diào)用轉(zhuǎn)變?yōu)閷?duì)服務(wù)的調(diào)用，實(shí)現(xiàn)各信息化子系統(tǒng)間的信息共享。 要實(shí)現(xiàn)智能公交系統(tǒng)對(duì)公交車輛的智能化管理，還必須獲取大量原始信息后面隱藏著的有價(jià)值信息，需要對(duì)獲得的公交信息采取進(jìn)一步處理。對(duì)實(shí)時(shí)客流量和公交車輛行程時(shí)間的預(yù)測(cè)能滿足出行者需求和有利于車輛的實(shí)時(shí)調(diào)度，當(dāng)前客流量獲取基本都是靠人工調(diào)查，效率低下，采集的信息單一；公交車輛行程時(shí)間采用路程長(zhǎng)度與全程平均速度比值獲得，與實(shí)際值誤差大。針對(duì)這些問題，研究了公交IC卡和RBF神經(jīng)網(wǎng)絡(luò)結(jié)合的客流量預(yù)測(cè)模型，進(jìn)而對(duì)車輛行程時(shí)間預(yù)測(cè)也進(jìn)行了改善。通過對(duì)IC卡信息的獲取和分析可以獲得比較全面的公交客流量信息，采用RBF神經(jīng)網(wǎng)絡(luò)對(duì)客流量的預(yù)測(cè)，得到較高精度的預(yù)測(cè)結(jié)果。再將預(yù)測(cè)的客流量用于車輛行程時(shí)間預(yù)測(cè)模型，完成預(yù)測(cè)。最后針對(duì)當(dāng)前智能公交信息服務(wù)模式下，服務(wù)系統(tǒng)不能提供具有決策性的信息，探索性的建立了智能化公交信息服務(wù)系統(tǒng)，將神經(jīng)網(wǎng)絡(luò)和專家系統(tǒng)結(jié)合使用在信息服務(wù)系統(tǒng)中，，在神經(jīng)系統(tǒng)具有反饋學(xué)習(xí)和專家系統(tǒng)能推理給出最優(yōu)解共同處理模式下，為出行者提供決策信息。
[Abstract]:With the development of economy and society, the quantity of urban vehicles is increasing and traffic problems become increasingly serious. How to solve the problem of urban traffic has become an important issue for urban development. While increasing the construction and management of urban traffic, people also realize that the development of urban public transport is an important means to improve urban traffic problems. With the continuous development of information technology and the effective application of the information technology in the urban transportation system, the intelligent transportation system (ITS) which uses traffic information as the center is formed. As the most important part of the ITS, intelligent public transportation is the most important part of the public transport information technology to improve the acquisition, processing and application of public transportation information ability is high. The guarantee of effective operation is the premise to improve the level of public transport service and increase the business efficiency of enterprises.
Starting from the realization technology of intelligent bus information collection, this paper first analyzes the current information acquisition technology and characteristics of the intelligent public transport. Secondly, the operation of the intelligent bus system needs not only information provided by the information collection subsystem, but also the relevant information of public transportation from other information subsystems. The information subsystem of the public transportation system is developed at different time and for different business requirements. It causes the business requests to be difficult to call between different platforms, form information island and close a large number of useful information in a subsystem. In this question, the SOA heterogeneous information system based on Web service is studied. Integration, it encapsulates the business functions of the information subsystem, divides into different services, calls the call of business functions to service, and realizes information sharing among the information subsystems.
In order to realize intelligent management of public transport system to bus vehicles, it is necessary to obtain valuable information hidden behind a large number of original information, and need to take further processing of the obtained bus information. The prediction of real-time passenger traffic and bus travel time can meet the needs of the travelers and facilitate the real-time scheduling of vehicles. The passenger flow acquisition is basically based on artificial investigation, low efficiency and single collection of information; bus travel time is obtained by the ratio of road length to the average speed, and the error is large with the actual value. In view of these problems, the passenger traffic prediction model combined with bus IC card and RBF neural network is studied, and then the vehicle travel time is predicted. Through the acquisition and analysis of IC card information, more comprehensive traffic information can be obtained, the RBF neural network is used to predict the passenger traffic, and the prediction results of high precision are obtained. Then the predicted passenger flow is used in the vehicle travel time prediction model, and the prediction is finished. Finally, the information service of the current intelligent bus is used. In the mode, the service system can not provide information with decision making, and establish an intelligent bus information service system, which combines the neural network and expert system in the information service system, and provides the decision for the travelers in the neural system with feedback learning and the expert system can reasoning and giving the best solution together. Information.

【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;U491.17

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