本文選題：出租車GPS數(shù)據(jù)　切入點(diǎn)：時(shí)空分析　出處：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)的快速發(fā)展和城市化建設(shè)的日益推進(jìn),人們對出行服務(wù)的需求愈發(fā)增長,出租車作為城市公共交通的重要組成部分,在人們的日常出行中發(fā)揮著重要作用。然而,因客源時(shí)空分布不均衡等因素而導(dǎo)致出租車高空載率、爭搶客源等不良現(xiàn)象頻現(xiàn),大大增加了出租車尋客的難度,降低了出租車的運(yùn)營效率。隨著ITS(Intelligent Transport System)技術(shù)的發(fā)展,越來越多的學(xué)者將出租車尋客模式作為研究熱點(diǎn),但普遍缺乏出租車效益評價(jià)指標(biāo)和顧及出租車供需關(guān)系及交通狀態(tài)的推薦策略方法研究。本文以深圳市13798輛出租車GPS數(shù)據(jù)為研究對象,以提高出租車效益為研究目標(biāo),在分析影響出租車尋客效益指標(biāo)因素的基礎(chǔ)上,充分考慮推薦的高效益載客熱點(diǎn)區(qū)域出租車的供需關(guān)系及交通狀態(tài),提出了基于出租車GPS數(shù)據(jù)的高效益尋客推薦策略方法,為出租車高效益尋客提供輔助決策支持,同時(shí)對于改善出租車資源分布不均衡的現(xiàn)狀,充分發(fā)揮出租車在城市交通中的補(bǔ)充作用有著重大意義。通過分析影響出租車尋客效益的指標(biāo)因素,本文以出租車載客態(tài)及相鄰空載態(tài)相結(jié)合作為研究對象,提出了基于出租車載客態(tài)單位時(shí)間收入及相鄰空載態(tài)尋客時(shí)間的效益指標(biāo)模型。基于該模型,在對出租車GPS數(shù)據(jù)進(jìn)行數(shù)據(jù)預(yù)處理及地圖匹配的基礎(chǔ)上,采用經(jīng)典數(shù)理統(tǒng)計(jì)及地統(tǒng)計(jì)分析方法,對出租車高效益客源的時(shí)空分布特征進(jìn)行了研究。通過對比分析工作日與非工作日及同一天不同時(shí)段的客源量變化特征來間接反映高效益客源隨時(shí)間在數(shù)量上的變化規(guī)律;同時(shí),對相應(yīng)時(shí)段的高效益客源的空間分布特征加以分析,為出租車高效益載客熱點(diǎn)推薦提供支撐�；诟咝б婵驮吹臅r(shí)空分布特征,在對出租車進(jìn)行高效益載客熱點(diǎn)推薦時(shí),首先對出租車GPS數(shù)據(jù)進(jìn)行基于效益指標(biāo)模型的熱點(diǎn)分析及熱點(diǎn)區(qū)域的劃分,同時(shí)在充分顧及出租車行駛到載客熱點(diǎn)區(qū)域之后的出租車飽和度及當(dāng)前的交通擁堵狀態(tài)的基礎(chǔ)上,本文提出了基于出租車效益的推薦度指標(biāo)模型。該模型以出租車到載客熱點(diǎn)的最短行駛時(shí)間為基礎(chǔ),計(jì)算出租車到達(dá)載客熱點(diǎn)區(qū)域后的出租車供需比及交通運(yùn)行指數(shù),以此計(jì)算推薦度指標(biāo)來評估該高效益載客熱點(diǎn)是否適合推薦給空駛出租車前往,進(jìn)而形成一套完整的出租車高效益尋客推薦策略方法。最后,本文以深圳市2011年4月18日早高峰的某輛空駛出租車為研究實(shí)例,對其進(jìn)行基于出租車效益指標(biāo)模型及推薦度指標(biāo)模型的高效益載客熱點(diǎn)推薦,并通過對比分析同一天相同時(shí)段內(nèi)兩個(gè)高效益載客熱點(diǎn)區(qū)域的不同推薦結(jié)果,同時(shí)結(jié)合城市居民出行規(guī)律及城市功能區(qū)結(jié)構(gòu)的實(shí)際情況,驗(yàn)證了該推薦策略的有效性。
[Abstract]:With the rapid development of economy and the development of urbanization, people's demand for travel services is increasing. Taxi, as an important part of urban public transportation, plays an important role in people's daily travel. With the development of ITS(Intelligent Transport system technology, many bad phenomena such as high no-load rate of taxis and scrambling for passengers occur frequently because of the unbalanced distribution of passenger space and time, which greatly increases the difficulty of finding taxi passengers and reduces the efficiency of taxi operation. More and more scholars regard taxi passenger seeking mode as the research hotspot. However, there is a general lack of evaluation index of taxi benefit and the study of recommended strategy method, which takes into account the relationship between supply and demand of taxis and traffic conditions. This paper takes the GPS data of 13798 taxis in Shenzhen as the research object, and takes the improvement of taxi benefit as the research goal. Based on the analysis of the factors affecting the efficiency index of taxi passenger seeking, the relationship between supply and demand and the traffic state of the recommended hot spot region of high-benefit passenger transportation are considered, and the method of high-benefit passenger seeking recommendation strategy based on taxi GPS data is put forward. To provide auxiliary decision support for taxi high-benefit passenger seeking, and to improve the unbalanced distribution of taxi resources, at the same time, It is of great significance to give full play to the supplementary role of taxis in urban traffic. By analyzing the index factors that affect the efficiency of taxi passenger seeking, this paper takes the combination of taxi carriage and adjacent no-load as the research object. This paper presents a benefit index model based on the unit time income of taxi carriage and the adjacent no-load seeking time. Based on this model, the data preprocessing and map matching of taxi GPS data are carried out. Using classical mathematical statistics and geostatistical analysis methods, This paper studies the spatial and temporal distribution characteristics of taxi high-benefit passenger source, and indirectly reflects the variation law of high-benefit passenger source with time by comparing and analyzing the changing characteristics of passenger quantity in different periods of working day, non-working day and the same day. At the same time, the spatial distribution characteristics of the high-benefit passenger source in the corresponding period are analyzed to provide the support for the recommendation of the high-benefit passenger carrying hot spot of the taxi. Based on the space-time distribution characteristics of the high-benefit passenger source, when the taxi is recommended to the high-benefit passenger hot spot, Firstly, the hot spot analysis and hot spot area division based on benefit index model are carried out for taxi GPS data. At the same time, based on the full consideration of taxi saturation and current traffic congestion after taxi driving to the hot spot area, In this paper, a recommendation index model based on taxi benefit is proposed. Based on the shortest driving time from taxi to hot spot, the taxi supply and demand ratio and traffic operation index are calculated after the taxi reaches the hot spot. The index of recommendation degree is used to evaluate whether the hot spot of high benefit passenger carrying is suitable for empty taxi, and then a complete set of recommended strategy method of high benefit passenger search for taxi is formed. Finally, Taking an empty taxi in Shenzhen on April 18, 2011, as an example, the hot spot of high benefit passenger carrying is recommended based on taxi benefit index model and recommendation index model. By comparing and analyzing the different recommended results of two high-benefit hot spots in the same day and the same time period, and combining with the actual situation of urban residents' travel rules and the structure of urban functional areas, the effectiveness of the proposed recommendation strategy is verified.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;P228.4

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