Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing
Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing
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DOI：
10.1080/15481603.2017.1309092
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发表时间：
2017-03
影响因子：
6.7
通讯作者：
Jingwei Shen;Xintao Liu;Min Chen
中科院分区：
文献类型：
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作者：
Jingwei Shen;Xintao Liu;Min Chen
浮动车数据(FCD)指的是配备了全球定位系统设备的车辆的轨迹，这些设备可以在短时间间隔内自动记录与位置相关的数据。由于中国城市的出租车不断在街道上寻找乘客，FCD每天都可以很容易地遍历城市的整个街道网络。利用这种情况，本研究从南京市6,445辆出租车的FCD中提取了两周的乘客上落点，中国说，以发现人类的行为模式及其背后的动态。在本研究中，将道路节点转换为点，在此基础上生成泰森多边形，将研究区域划分为小区域，目的是探索上下车地点的空间分布。使用Moran‘s I指数计算上落点空间分布的空间自相关性，并使用热点分析来识别具有统计意义的热点和冷点的空间集群。研究结果表明：(1)时间格局表现出较强的日节律性；(2)空间格局表明，从市区到远郊，上落点数量逐渐减少；(3)时空格局表现出较大的时间差异性；(4)回归模型探索的驱动力表明，人口密度和交通密度与人口分布基本一致，但人均可支配收入与人口分布不一致。
Floating Car Data (FCD) refers to the trajectories of vehicles equipped with Global Positioning System-enabled devices that automatically record location-related data within a short time interval. As taxies in Chinese cities continually drive along the streets seeking passengers, FCD can easily traverse the entire street network in a city on a daily basis. Taking advantage of this situation, this study extracted passenger pickup and drop-off locations from FCD sourced from 6445 taxis over a 2-week period in Nanjing, China to discover human behavioral patterns and the dynamics behind them. In this study, road nodes are converted to the points, based on which Thiessen polygons are generated to divide the study area into small areas with the goal of exploring the spatial distribution of pickup and drop-off locations. Moran’s I index is used to calculate the spatial autocorrelation of the spatial distribution of pickup and drop-off locations, and hot spot analysis is used to identify statistically significant spatial clusters of hot and cold spots. The spatial and temporal patterns of FCD in the study area are investigated, and the results show that: (1) the temporal patterns show a strong daily rhythm, (2) the spatial patterns show that the number of pickup and drop-off locations gradually diminish from the downtown areas to the outer suburbs, (3) the spatiotemporal patterns exhibit large differences over time, and (4) the driving forces explored by regression models indicate that population density and transportation density are consistent with the population distribution, but per capita disposable income is not consistent with the population distribution.