基于車牌流數(shù)據(jù)的伴隨車發(fā)現(xiàn)方法研究
發(fā)布時間：2018-09-14 18:29
【摘要】：隨著社會經(jīng)濟的快速發(fā)展,機動車越來越多,人們出行愈加頻繁。在中國以北京、上海、深圳為代表的大城市,智能交通管理監(jiān)控系統(tǒng)每天都會存儲海量的機動車出行數(shù)據(jù),包括ANPR數(shù)據(jù)、GPS數(shù)據(jù)等。充分利用這些交通數(shù)據(jù)挖掘有效的信息,解決以伴隨車分析為例的分析性型交通問題,一直以來是智能交通領(lǐng)域的研究熱點。伴隨車分析作為分析性類型的交通問題之一,其典型的應(yīng)用場景是"犯罪團伙駕車協(xié)同作案"。此類案件的理想解決方案是在犯罪團伙逃逸過程中就能及時發(fā)現(xiàn)嫌疑車輛,通知公安干警前往攔截,實時性要求較高。近年來基于流數(shù)據(jù)的研究逐漸成為大數(shù)據(jù)研究的一個趨勢。本文基于隨時間變化的車牌識別流數(shù)據(jù),通過實時分析多輛犯罪嫌疑車輛在一段時間內(nèi)的關(guān)系,提出了一種并行PFID算法的伴隨車輛發(fā)現(xiàn)方法。利用成熟的分布式流數(shù)據(jù)處理框架Spark Streaming實現(xiàn)了該算法,在秒級響應(yīng)時間內(nèi)找到伴隨車輛組,達到及時預(yù)警效果,便于公安部門及時處理。本文的主要工作如下:(1)提出了一種并行PFID算法的伴隨車輛發(fā)現(xiàn)方法。根據(jù)伴隨車輛組的定義、車牌識別數(shù)據(jù)的數(shù)據(jù)格式以及現(xiàn)有的數(shù)據(jù)模型技術(shù),經(jīng)過分析后,采用關(guān)聯(lián)規(guī)則挖掘相關(guān)算法來解決本問題。PFID算法采用了關(guān)聯(lián)規(guī)則Eclat算法的思想,基于車牌流數(shù)據(jù)進行頻繁項集的挖掘,從而發(fā)現(xiàn)伴隨車輛組。(2)利用了分布式流數(shù)據(jù)處理框架Spark Streaming實現(xiàn)了該PFID算法。在云環(huán)境下的Spark集群中進行實驗,從內(nèi)存和響應(yīng)時間兩方面進行對比。實驗結(jié)果表明,該方法在內(nèi)存消耗和響應(yīng)時間上都有較好的效果,克服單一機器下程序運行內(nèi)存不足等問題,較快地發(fā)現(xiàn)伴隨車輛組。(3)構(gòu)造了伴隨車輛組發(fā)現(xiàn)的原型系統(tǒng)。為了更好地呈現(xiàn)實驗結(jié)果,構(gòu)造了原型系統(tǒng),將結(jié)果以圖表、地圖等可視化呈現(xiàn),使實驗結(jié)果更形象生動。(4)實現(xiàn)了伴隨車輛組服務(wù)化。為了便于第三方使用伴隨車輛組結(jié)果數(shù)據(jù),本文將通過實驗處理后的伴隨車輛組結(jié)果數(shù)據(jù)以REST風(fēng)格的Web API對外提供,對外提供的數(shù)據(jù)格式包括Text、XML、JSON等。
[Abstract]:With the rapid development of social economy, more and more motor vehicles, people travel more frequently. In China's big cities, such as Beijing, Shanghai and Shenzhen, the intelligent traffic management and monitoring system stores huge amounts of motor vehicle travel data every day, including ANPR data and so on. Using these traffic data to mine effective information to solve the analytical traffic problem, which takes the analysis of accompanying vehicles as an example, has always been the research hotspot in the field of intelligent transportation. As one of the analytical traffic problems, the typical application scene of accompanying vehicle analysis is "gang driving Synergistic Crime". The ideal solution for this kind of case is to find the suspected vehicle in time during the escape of the criminal gang, to notify the police to stop the case, and to have a high real-time requirement. In recent years, the research based on stream data has gradually become a trend of big data research. Based on the time-varying license plate recognition stream data, this paper presents a parallel PFID algorithm based on the real-time analysis of the relationships of several suspected vehicles over a period of time. The algorithm is implemented by using the mature distributed stream data processing framework (Spark Streaming), which finds the accompanying vehicle group in the second response time, and achieves the effect of timely warning, which is convenient for the public security department to deal with the problem in time. The main work of this paper is as follows: (1) A parallel PFID algorithm for adjoint vehicle discovery is proposed. According to the definition of associated vehicle group, the data format of license plate recognition data and the existing data model technology, after analysis, the association rule mining algorithm is adopted to solve the problem. PFID algorithm adopts the idea of association rule Eclat algorithm. The frequent itemsets are mined based on the license plate stream data, and the associated vehicle groups are found. (2) the PFID algorithm is implemented by using the distributed stream data processing framework (Spark Streaming). The experiment is carried out in Spark cluster in cloud environment, and the memory and response time are compared. The experimental results show that the proposed method has good performance in memory consumption and response time. It overcomes the problems of running memory in a single machine and finds the associated vehicle group quickly. (3) A prototype system with vehicle group discovery is constructed. In order to better present the experimental results, a prototype system is constructed, and the results are visualized as graphs and maps. (4) Service-oriented accompanying vehicle groups are implemented. In order to facilitate the third party to use the accompanying vehicle group result data, the result data of the accompanying vehicle group will be provided by REST style Web API through the experiment. The external data format includes Text,XML,JSON and so on.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;TP311.13
本文編號：2243521
[Abstract]:With the rapid development of social economy, more and more motor vehicles, people travel more frequently. In China's big cities, such as Beijing, Shanghai and Shenzhen, the intelligent traffic management and monitoring system stores huge amounts of motor vehicle travel data every day, including ANPR data and so on. Using these traffic data to mine effective information to solve the analytical traffic problem, which takes the analysis of accompanying vehicles as an example, has always been the research hotspot in the field of intelligent transportation. As one of the analytical traffic problems, the typical application scene of accompanying vehicle analysis is "gang driving Synergistic Crime". The ideal solution for this kind of case is to find the suspected vehicle in time during the escape of the criminal gang, to notify the police to stop the case, and to have a high real-time requirement. In recent years, the research based on stream data has gradually become a trend of big data research. Based on the time-varying license plate recognition stream data, this paper presents a parallel PFID algorithm based on the real-time analysis of the relationships of several suspected vehicles over a period of time. The algorithm is implemented by using the mature distributed stream data processing framework (Spark Streaming), which finds the accompanying vehicle group in the second response time, and achieves the effect of timely warning, which is convenient for the public security department to deal with the problem in time. The main work of this paper is as follows: (1) A parallel PFID algorithm for adjoint vehicle discovery is proposed. According to the definition of associated vehicle group, the data format of license plate recognition data and the existing data model technology, after analysis, the association rule mining algorithm is adopted to solve the problem. PFID algorithm adopts the idea of association rule Eclat algorithm. The frequent itemsets are mined based on the license plate stream data, and the associated vehicle groups are found. (2) the PFID algorithm is implemented by using the distributed stream data processing framework (Spark Streaming). The experiment is carried out in Spark cluster in cloud environment, and the memory and response time are compared. The experimental results show that the proposed method has good performance in memory consumption and response time. It overcomes the problems of running memory in a single machine and finds the associated vehicle group quickly. (3) A prototype system with vehicle group discovery is constructed. In order to better present the experimental results, a prototype system is constructed, and the results are visualized as graphs and maps. (4) Service-oriented accompanying vehicle groups are implemented. In order to facilitate the third party to use the accompanying vehicle group result data, the result data of the accompanying vehicle group will be provided by REST style Web API through the experiment. The external data format includes Text,XML,JSON and so on.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;TP311.13
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