本文選題：時(shí)空數(shù)據(jù)存儲(chǔ) + 行程識(shí)別　； 參考：《武漢理工大學(xué)》2015年碩士論文

【摘要】：研究物流車輛出行的熱點(diǎn)區(qū)域、時(shí)空特征和演變規(guī)律,有利于延伸物流車輛監(jiān)管數(shù)據(jù)的獲取生成、集成管理、共享服務(wù)和應(yīng)用的信息服務(wù)鏈,帶動(dòng)基于車聯(lián)網(wǎng)的移動(dòng)車輛監(jiān)管信息增值服務(wù)的高速增長(zhǎng),提升城市的智慧物流監(jiān)管水平和基于車聯(lián)網(wǎng)的物流運(yùn)輸管理服務(wù)能力,提高工業(yè)運(yùn)輸效率和運(yùn)輸商品安全,進(jìn)一步助力城市的和諧和可持續(xù)發(fā)展。本文基于湖北省科技支撐計(jì)劃項(xiàng)目《基于車聯(lián)網(wǎng)的物流車輛協(xié)同監(jiān)管關(guān)鍵技術(shù)及應(yīng)用示范》(2014BAA146)的課題背景,研究了車輛GPS數(shù)據(jù)的采集、預(yù)處理方法,同時(shí)為了提高時(shí)空數(shù)據(jù)檢索效率,設(shè)計(jì)了一種基于MongoDB的時(shí)空數(shù)據(jù)存儲(chǔ)方案,提出了一種基于車輛速度和ACC狀態(tài)的行程識(shí)別方法,并基于行程數(shù)據(jù)對(duì)車輛出行的時(shí)空特征進(jìn)行了研究。本文的主要工作包括:(1)設(shè)計(jì)了基于MongoDB的時(shí)空數(shù)據(jù)存儲(chǔ)方法。在研究過程中,通過對(duì)采集的GPS元數(shù)據(jù)進(jìn)行數(shù)值編碼格式化處理進(jìn)行存儲(chǔ),緩解了磁盤空間的存儲(chǔ)壓力。同時(shí),采用基于sharding的MongoDB集群架構(gòu)進(jìn)行時(shí)空數(shù)據(jù)存儲(chǔ),并建立了復(fù)合索引和地理空間索引,提升了時(shí)空數(shù)據(jù)檢索效率。(2)提出了基于車輛速度和ACC狀態(tài)的行程識(shí)別方法。根據(jù)Stop-Move模型的基本原理,研究了基于車速和ACC狀態(tài)的軌跡點(diǎn)分類方法,并通過不斷地對(duì)GPS數(shù)據(jù)流中的軌跡點(diǎn)進(jìn)行分類和合并,實(shí)現(xiàn)了對(duì)車輛行程活動(dòng)的識(shí)別,為研究車輛出行熱點(diǎn)和時(shí)空特征提供了數(shù)據(jù)基礎(chǔ)。(3)設(shè)計(jì)了基于LBS的出行時(shí)空特征提取方法,并分析了武漢市物流車輛的出行時(shí)空特征。首先根據(jù)車輛出行的行程特征設(shè)計(jì)了基于LBS的車輛出行偏好、熱點(diǎn)區(qū)域等特征的提取方法,以及車輛熱點(diǎn)區(qū)域吸引力、作息活動(dòng)相似度的計(jì)算方法,實(shí)現(xiàn)了對(duì)車輛出行熱點(diǎn)區(qū)域吸引力計(jì)算和作息規(guī)律程度的評(píng)價(jià),并分析了武漢市物流車輛的出行時(shí)空特征,為基于車聯(lián)網(wǎng)的物流運(yùn)營管理提供了決策支持。本文的主要貢獻(xiàn)在于結(jié)合特定的課題背景,對(duì)車輛出行時(shí)空特征分析的關(guān)鍵技術(shù)進(jìn)行了研究和應(yīng)用,能夠?yàn)橹腔畚锪黝I(lǐng)域的車輛軌跡數(shù)據(jù)挖掘研究和應(yīng)用提供一定的理論參考和技術(shù)參考。
[Abstract]:The study of hot spots, space-time characteristics and evolution rules of logistics vehicle travel is conducive to extending the information service chain of logistics vehicle supervision data acquisition, integration management, sharing services and applications. To promote the rapid growth of mobile vehicle regulatory information value-added services based on vehicle networking, to enhance the level of intelligent logistics supervision in cities and the capacity of logistics and transportation management services based on vehicle networking, to improve the efficiency of industrial transport and transport commodity safety, Further contribute to the harmonious and sustainable development of the city. Based on the project of Hubei province science and technology support project < key technology and application demonstration of logistics and vehicle collaborative supervision based on vehicle networking "2014BAA146), this paper studies the acquisition and preprocessing method of vehicle GPS data. In order to improve the efficiency of spatio-temporal data retrieval, a spatio-temporal data storage scheme based on MongoDB is designed, and a travel recognition method based on vehicle speed and ACC state is proposed. The temporal and spatial characteristics of vehicle travel are studied based on travel data. The main work of this paper is to design a spatio-temporal data storage method based on MongoDB. In the research process, the storage pressure of the disk space is alleviated by the numerical coding and formatting of the collected GPS metadata. At the same time, MongoDB cluster architecture based on sharding is used to store spatio-temporal data, and the composite index and geospatial index are established to improve the efficiency of spatio-temporal data retrieval. According to the basic principle of Stop-Move model, the track point classification method based on speed and ACC state is studied, and the recognition of vehicle travel activity is realized by continuously classifying and merging the trajectory points in GPS data stream. This paper provides a data basis for the study of vehicle travel hot spots and space-time features. (3) A method for extracting travel space-time features based on LBS is designed, and the spatio-temporal characteristics of logistics vehicles in Wuhan are analyzed. Firstly, according to the travel characteristics of vehicles, the paper designs a LBS based method to extract the features of vehicle travel preferences, hot spots and so on, as well as the calculation method of the similarity between the attraction of vehicle hot spots and the activity of work and rest. This paper realizes the calculation of the attraction of the hot spot area of vehicle travel and the evaluation of the degree of the regularity of work and rest, and analyzes the travel space-time characteristics of the logistics vehicle in Wuhan, which provides decision support for the logistics operation management based on the vehicle network. The main contribution of this paper lies in the research and application of the key technology of vehicle travel space-time characteristic analysis combined with the specific subject background. It can provide some theoretical and technical reference for the research and application of vehicle trajectory data mining in the field of intelligent logistics.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495

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