本文選題：網(wǎng)約車 + 供需匹配度�。� 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：隨著“互聯(lián)網(wǎng)+”在各行業(yè)的不斷滲透,為傳統(tǒng)行業(yè)的革新與升級(jí)注入了全新的動(dòng)力。巡游出租車行業(yè)供需時(shí)空信息的不對(duì)稱性導(dǎo)致“打車難”問題持續(xù)存在,使其成為“互聯(lián)網(wǎng)+”必然會(huì)觸及的領(lǐng)域。由此催生了網(wǎng)約車這一新業(yè)態(tài),有效打通了出租車供給與需求之間的信息不對(duì)稱性問題。本文利用網(wǎng)約車平臺(tái)的公開數(shù)據(jù),開展了網(wǎng)約車需求特性分析與短時(shí)預(yù)測(cè),為網(wǎng)約車運(yùn)營(yíng)提供方法性參考,對(duì)提升供需匹配效率具有重要意義。本文主要工作如下:首先,本文通過查閱大量研究文獻(xiàn),從傳統(tǒng)出租車供需、互聯(lián)網(wǎng)時(shí)代的出租車供需研究、以及交通短時(shí)預(yù)測(cè)等方面進(jìn)行了綜述,梳理了本文研究?jī)?nèi)容與技術(shù)路線。并且通過對(duì)比網(wǎng)約車與巡游出租車的行業(yè)特性,進(jìn)一步明確了本文的研究?jī)?nèi)容。第二,網(wǎng)約車出行需求時(shí)空特性分析。根據(jù)網(wǎng)約車出行數(shù)據(jù)的特征,本文將網(wǎng)約車出行需求總數(shù)拆分為供需匹配數(shù)與需求缺口數(shù),并定義了網(wǎng)約車供需匹配度與需求緊缺度。并據(jù)此分析工作日與非工作日網(wǎng)約車出行需求的時(shí)間特性,劃分了不同的時(shí)段類型,并得出工作日與非工作日供需匹配度的差異性。然后在此基礎(chǔ)上進(jìn)行分時(shí)段的網(wǎng)約車出行需求空間特性分析,為網(wǎng)約車出行需求短時(shí)預(yù)測(cè)提供了依據(jù)。第三,網(wǎng)約車出行需求短時(shí)預(yù)測(cè)。論文從現(xiàn)實(shí)意義角度出發(fā),以需求缺口數(shù)作為網(wǎng)約車出行需求短時(shí)預(yù)測(cè)的目標(biāo),并進(jìn)行了時(shí)間相關(guān)性分析,發(fā)現(xiàn)網(wǎng)約車出行需求缺口與歷史前50分鐘,以及同時(shí)刻歷史日期的出行需求缺口相關(guān)程度較大。根據(jù)網(wǎng)約車出行需求缺口的特點(diǎn)構(gòu)建了基于BP神經(jīng)網(wǎng)絡(luò)的網(wǎng)約車出行需求短時(shí)預(yù)測(cè)模型,依據(jù)相關(guān)性分析結(jié)果確定了模型結(jié)構(gòu),并以網(wǎng)約車出行實(shí)際數(shù)據(jù)進(jìn)行了需求短時(shí)預(yù)測(cè)并驗(yàn)證了模型有效性。最后,給出了改善網(wǎng)約車供需匹配的相關(guān)建議,并對(duì)下一步研究進(jìn)行了展望。
[Abstract]:With the continuous penetration of the Internet in various industries, the innovation and upgrading of traditional industries has injected a new impetus. The asymmetry of time and space information between supply and demand of tour taxi industry leads to the persistence of the problem of "taxi hailing difficulty", which makes it an inevitable area to be touched by the "Internet". This leads to the birth of a new form of cable-sharing, which effectively solves the problem of information asymmetry between the supply and demand of taxis. This paper makes use of the open data of the network car-hailing platform to carry out the analysis of the demand characteristics and the short-term forecast of the network-ride-hailing demand, which provides a methodological reference for the network-ride-hailing operation and is of great significance to the improvement of the efficiency of the matching between supply and demand. The main work of this paper is as follows: firstly, this paper reviews the traditional taxi supply and demand, the research of taxi supply and demand in the Internet era, and the short-term traffic forecasting through consulting a large number of research documents. Combing the research content and technical route of this paper. And by contrasting the industry characteristic of the net and tour taxi, the research content of this paper is further clarified. Second, the time-space characteristic analysis of the travel demand. According to the characteristics of the trip data, this paper divides the total demand into the supply and demand matching number and the demand gap number, and defines the supply and demand matching degree and the demand shortage degree. Based on the analysis of the time characteristics of the demand for car-sharing between workday and non-workday, different time periods are divided, and the difference of supply and demand matching degree between workday and non-workday is obtained. On the basis of this, the spatial characteristics of the travel demand are analyzed, which provides the basis for the short-term forecast of the demand for the net-ride-hailing trip. Third, the net car ride demand short-term forecast. From the point of view of practical significance, this paper takes the number of demand gap as the goal of short-term forecast of the demand for car-hailing travel, and analyzes the correlation of time, and finds that the gap of demand for network-ride-sharing travel is 50 minutes before history. And at the same time the historical date of travel demand gap is relatively large. According to the characteristics of the gap in the demand for ride-to-vehicle travel, a short-time forecasting model of the demand for ride-hailing trip is constructed based on BP neural network, and the structure of the model is determined according to the results of correlation analysis. Based on the actual data, the demand is predicted in a short time and the validity of the model is verified. Finally, some suggestions to improve the matching between supply and demand are given, and the future research is prospected.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP183;U491

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相關(guān)期刊論文 前10條

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本文編號(hào)：1899180