本文關(guān)鍵詞： 高速公路交通安全 預(yù)警系統(tǒng) 云計(jì)算 交通安全狀態(tài)評價(jià)模型　出處：《重慶交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：我國高速公路交通安全形勢因其通車?yán)锍碳叭肆魑锪鞯难该驮黾佣絹碓絿?yán)峻,很多交通問題如突發(fā)事件、交通擁堵等出現(xiàn)的頻率都呈較快增長的趨勢。如何減少高速公路交通事故和提高其安全水平的有效途徑就是現(xiàn)實(shí)高效的、準(zhǔn)確的、及時(shí)的判斷和預(yù)知即將可能發(fā)生的交通事故。如何搭建高效、有力的高速公路交通安全預(yù)警系統(tǒng)用以有效預(yù)測即將可能發(fā)生的交通事故,及時(shí)、快速、準(zhǔn)確的將預(yù)警信息發(fā)布給用戶;利用預(yù)警系統(tǒng)對高速公路進(jìn)行實(shí)時(shí)監(jiān)測,獲得實(shí)時(shí)數(shù)據(jù),經(jīng)過處理數(shù)據(jù)快速的排除潛在的危害,是做到從根源上減少交通事故率的關(guān)鍵。所以,本文針對高速公路,目標(biāo)在于構(gòu)建一套完整的、集交通數(shù)據(jù)采集、數(shù)據(jù)處理、安全評價(jià)和預(yù)警信息發(fā)布于一體的交通安全預(yù)警系統(tǒng)。首先,從云計(jì)算的基本概率入手,介紹其概念、特點(diǎn)及模型及其應(yīng)用;同時(shí),分析總結(jié)了現(xiàn)有的高速公路交通安全預(yù)警系統(tǒng)框架理論,并分析云計(jì)算關(guān)鍵技術(shù)及對云計(jì)算在交通領(lǐng)域中的應(yīng)用進(jìn)行闡述。其次,著重分析了物聯(lián)網(wǎng)技術(shù)在交通安全領(lǐng)域中的應(yīng)用,介紹物聯(lián)網(wǎng)相關(guān)理論并研究基于物聯(lián)網(wǎng)的交通安全預(yù)警系統(tǒng)架構(gòu),包括基于物聯(lián)網(wǎng)的采集系統(tǒng)和基于GIS的信息處理系統(tǒng),為構(gòu)建基于云架構(gòu)的高速公路交通安全預(yù)警系統(tǒng)設(shè)計(jì)提供經(jīng)驗(yàn)支撐。再者,分析高速公路預(yù)警管理和預(yù)警系統(tǒng)的需求,構(gòu)建合理的基于云架構(gòu)的高速公路交通安全預(yù)警系統(tǒng),提出了其總體框架和邏輯結(jié)構(gòu),重點(diǎn)介紹了在物聯(lián)網(wǎng)基礎(chǔ)上的信息采集系統(tǒng)和信息發(fā)布系統(tǒng)。然后,本文重點(diǎn)研究基于云計(jì)算的交通數(shù)據(jù)處理系統(tǒng),主要包括數(shù)據(jù)的預(yù)處理、基于Map Reduce的神經(jīng)網(wǎng)絡(luò)算法的交通流預(yù)測、交通安全狀態(tài)評價(jià)模型。最后,針對基于MapReduce的神經(jīng)網(wǎng)絡(luò)算法的交通流預(yù)測和交通安全狀態(tài)評價(jià)模型給出算例,驗(yàn)證云計(jì)算在交通安全預(yù)警系統(tǒng)中的可行性。
[Abstract]:The traffic safety situation of freeway in our country is more and more serious because of its traffic mileage and the rapid increase of people flow logistics. Many traffic problems such as unexpected events are becoming more and more serious. The frequency of traffic congestion is increasing rapidly. How to reduce highway traffic accidents and improve the safety level of the effective way is realistic, efficient and accurate. How to build an efficient and powerful highway traffic safety warning system to effectively predict the upcoming traffic accidents, timely and quickly. Accurately release the warning information to the user; It is the key to reduce the traffic accident rate by using the early warning system to monitor the freeway in real time and obtain the real time data and eliminate the potential harm quickly after processing the data. The goal of this paper is to build a complete traffic safety early warning system which integrates traffic data collection, data processing, safety evaluation and early warning information. Starting with the basic probability of cloud computing, this paper introduces the concept, characteristics, model and application of cloud computing. At the same time, this paper analyzes and summarizes the existing framework of highway traffic safety early warning system theory, and analyzes the key technologies of cloud computing and the application of cloud computing in the field of transportation. Secondly. This paper analyzes the application of Internet of things technology in the field of traffic safety, introduces the theory of Internet of things and studies the architecture of traffic safety early warning system based on Internet of things. It includes the collection system based on the Internet of things and the information processing system based on GIS, which provides empirical support for the design of highway traffic safety early warning system based on cloud architecture. This paper analyzes the requirements of expressway early warning management and early warning system, constructs a reasonable highway traffic safety early warning system based on cloud structure, and puts forward its general framework and logical structure. Focus on the introduction of the Internet of things based on the information collection system and information distribution system. Then, this paper focuses on cloud computing based traffic data processing system, mainly including data preprocessing. Traffic flow prediction and traffic safety evaluation model based on neural network algorithm of Map Reduce. Finally. An example of traffic flow prediction and traffic safety state evaluation model based on MapReduce neural network algorithm is given to verify the feasibility of cloud computing in traffic safety early warning system.
【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】：2015
【分類號】：U495

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