基于GPS数据的出租车载客点空间特征分析

发布时间：2018-05-27 12:28

本文选题：出租车 + 载客点　；参考：《吉林大学》2013年硕士论文

【摘要】：出租车是城市客运交通系统的重要组成部分，对出租车出行行为进行研究和合理引导是满足城市客运需求解决交通拥堵问题的关键出租车载客点即出租车搭载乘客的点，其分布特征是出租车出行行为的重要体现掌握载客点的空间特征对于出租车司机合理安排自己的巡航路线以及出租车管理部门合理布局出租车服务设施都至关重要论文首先分析了国内外在空间分析方法——信息熵理论和空间统计分析理论方面的研究现状，提出了将两个理论结合起来分析出租车多日载客点空间特征的思路与方法，以寻找各日载客点空间分布特征的异同根据信息熵理论构建出租车载客点的信息熵公式均衡度公式和聚集度公式，从不同角度阐述出租车载客点的空间整体分布特征运用空间统计分析理论体系分析出租车载客点的空间具体分布特征：应用集中趋势的分析方法确定载客点的空间分布中心；应用标准差距离确定载客点在均数中心周边分布的离散情况；应用标准差椭圆确定载客点的主要分布区域分布最多的方向；应用凸壳的确定载客点在深圳市的分布范围；应用热点分析确定载客点聚集的位置，并结合在岗职工数常住人口数用地性质道路交通条件具体介绍载客点在不同区域的聚集情况和原因；根据重要设施周边出租车载客点的密度分布确定其周边载客点聚集分布的范围和强度通过论文研究，得到各日载客点的空间分布特征主要结论为：各日载客点的空间分布中心均位于福田区，但非工作日的有向远离CBD方向移动的趋势；各日载客点分布的离散情况较接近，但工作日的离散程度比非工作日的小；通过标准差椭圆的计算可知各日载客点主要分布于福田区罗湖区和南山区，且工作日标准差椭圆的面积比非工作日的小；各日载客点分布最多的方向都接近东西方向；载客点的分布范围较广，不仅局限在深圳市，且工作日偏远点的个数比非工作日的少各日载客点最聚集的地方都是火车站，其次是罗湖口岸，它们的聚集程度和范围在工作日和非工作日的差别不大相比之下，CBD地区工作日和非工作日的差别却较大：工作日的聚集范围比非工作日的小，工作日的聚集强度比非工作日的高论文的特色与创新之处在于： 1综合应用信息熵理论和空间统计分析方法，，对出租车载客点的空间分布特征进行量化计算 2研究多日载客点的空间分布特征，对比分析工作日和非工作日的异同 3根据重要设施周边出租车载客点的密度分布确定其周边载客点聚集分布的范围和强度，并分析其时变性
[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.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：P228.4;U492.4

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