本文選題：出租車 + 載客點(diǎn)��； 參考：《吉林大學(xué)》2013年碩士論文

【摘要】：出租車是城市客運(yùn)交通系統(tǒng)的重要組成部分，對出租車出行行為進(jìn)行研究和合理引導(dǎo)是滿足城市客運(yùn)需求解決交通擁堵問題的關(guān)鍵出租車載客點(diǎn)即出租車搭載乘客的點(diǎn)，其分布特征是出租車出行行為的重要體現(xiàn)掌握載客點(diǎn)的空間特征對于出租車司機(jī)合理安排自己的巡航路線以及出租車管理部門合理布局出租車服務(wù)設(shè)施都至關(guān)重要 論文首先分析了國內(nèi)外在空間分析方法——信息熵理論和空間統(tǒng)計(jì)分析理論方面的研究現(xiàn)狀，提出了將兩個(gè)理論結(jié)合起來分析出租車多日載客點(diǎn)空間特征的思路與方法，以尋找各日載客點(diǎn)空間分布特征的異同根據(jù)信息熵理論構(gòu)建出租車載客點(diǎn)的信息熵公式均衡度公式和聚集度公式，從不同角度闡述出租車載客點(diǎn)的空間整體分布特征運(yùn)用空間統(tǒng)計(jì)分析理論體系分析出租車載客點(diǎn)的空間具體分布特征：應(yīng)用集中趨勢的分析方法確定載客點(diǎn)的空間分布中心；應(yīng)用標(biāo)準(zhǔn)差距離確定載客點(diǎn)在均數(shù)中心周邊分布的離散情況；應(yīng)用標(biāo)準(zhǔn)差橢圓確定載客點(diǎn)的主要分布區(qū)域分布最多的方向；應(yīng)用凸殼的確定載客點(diǎn)在深圳市的分布范圍；應(yīng)用熱點(diǎn)分析確定載客點(diǎn)聚集的位置，并結(jié)合在崗職工數(shù)常住人口數(shù)用地性質(zhì)道路交通條件具體介紹載客點(diǎn)在不同區(qū)域的聚集情況和原因；根據(jù)重要設(shè)施周邊出租車載客點(diǎn)的密度分布確定其周邊載客點(diǎn)聚集分布的范圍和強(qiáng)度 通過論文研究，得到各日載客點(diǎn)的空間分布特征主要結(jié)論為：各日載客點(diǎn)的空間分布中心均位于福田區(qū)，但非工作日的有向遠(yuǎn)離CBD方向移動(dòng)的趨勢；各日載客點(diǎn)分布的離散情況較接近，但工作日的離散程度比非工作日的��；通過標(biāo)準(zhǔn)差橢圓的計(jì)算可知各日載客點(diǎn)主要分布于福田區(qū)羅湖區(qū)和南山區(qū)，且工作日標(biāo)準(zhǔn)差橢圓的面積比非工作日的��；各日載客點(diǎn)分布最多的方向都接近東西方向；載客點(diǎn)的分布范圍較廣，不僅局限在深圳市，且工作日偏遠(yuǎn)點(diǎn)的個(gè)數(shù)比非工作日的少各日載客點(diǎn)最聚集的地方都是火車站，其次是羅湖口岸，它們的聚集程度和范圍在工作日和非工作日的差別不大相比之下，CBD地區(qū)工作日和非工作日的差別卻較大：工作日的聚集范圍比非工作日的小，工作日的聚集強(qiáng)度比非工作日的高 論文的特色與創(chuàng)新之處在于： 1綜合應(yīng)用信息熵理論和空間統(tǒng)計(jì)分析方法，，對出租車載客點(diǎn)的空間分布特征進(jìn)行量化計(jì)算 2研究多日載客點(diǎn)的空間分布特征，對比分析工作日和非工作日的異同 3根據(jù)重要設(shè)施周邊出租車載客點(diǎn)的密度分布確定其周邊載客點(diǎn)聚集分布的范圍和強(qiáng)度，并分析其時(shí)變性
[Abstract]:Taxi is an important part of the urban passenger transport system. The study and reasonable guidance of the taxi travel behavior are the key points to meet the urban passenger demand to solve the traffic congestion problem. The distribution characteristics of the taxi travel behavior is the important embodiment of the space special of the passenger point. It is essential for taxi drivers to arrange their cruise routes and taxi management departments to arrange taxi service facilities reasonably.
The paper first analyzes the research status of the spatial analysis method, the theory of information entropy and the theory of spatial statistical analysis, and puts forward the idea and method of combining the two theories to analyze the spatial characteristics of the taxi's multi day passenger point, in order to find the similarities and differences of the spatial distribution characteristics of each day's passenger point. The information entropy formula and the aggregation formula of the information entropy formula of the car renting point are described. From different angles, the spatial distribution characteristics of the taxi passenger point are expounded and the spatial distribution characteristics of the taxi passenger point are analyzed by the spatial statistical analysis theory system. The spatial distribution center of the passenger point is determined by the analytical method of the centralized trend; A standard difference distance is used to determine the discrete-time distribution of the distribution of the passenger points around the center of the average number; the standard difference ellipse is used to determine the most distribution direction of the main distribution areas of the passenger points; the convex hull is used to determine the distribution of the passenger points in Shenzhen, and the location of the gathering of the passenger points is determined by the application of hot spot analysis, and the number of workers at the post is permanent. The road traffic conditions of population and land use specifically introduce the gathering situation and reason of the passenger points in different areas. According to the density distribution of the taxi passenger points around the important facilities, the range and intensity of the gathering distribution of the surrounding passenger points is determined.
The main conclusion is that the spatial distribution center of the daily passenger points is located in Futian District, but the trend of moving away from the CBD direction in the non working day is close, but the discrete degree of the daily passenger point distribution is close, but the degree of dispersion of the day is smaller than that of the non working day. The calculation of the quasi differential ellipse shows that the daily passenger points are mainly distributed in Luohu District and Nanshan District, Futian District and Nanshan District, and the area of standard deviation ellipse of the working day is smaller than that of non working day; the direction of the most distributed passenger points in each day is close to the East and the West; the distribution of the passenger points is wide, not only in Shenzhen, but also in the number ratio of the remote point of the working day. The most gathering places in the non working day are the train stations and the Luohu ports, and the degree and scope of their aggregation are less than that of the working day and non working day. The difference between the working day and the non working day in the CBD area is larger than that of the non working day: the gathering strength of the working day is smaller than the non working day, and the intensity of the working day is the aggregation. Higher than the non working day
The characteristics and innovation of the paper are as follows:
1 integrating the application of information entropy theory and spatial statistical analysis method, we quantify the spatial distribution characteristics of taxi carrying points.
2, we study the spatial distribution characteristics of multi day passenger sites, and compare the similarities and differences between working days and non working days.
3 according to the density distribution of taxi carrying points around the important facilities, determine the scope and intensity of the surrounding passenger destination gathering and distribution, and analyze the time variability.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P228.4;U492.4

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本文編號：1942081