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《城市交通》杂志
2012年 第1期
上海市出租汽车出行时空分布规律研究
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文章编号:1672-5328(2012)01-0068-07

邓中伟1,季民河1,2
(1.华东师范大学地理信息科学教育部重点实验室,上海 200062;2.华东师范大学中国东西部合作研究中心,上海 200062)

摘要:为测定大样本视角下出租汽车出行时空规律,采用探索性空间数据分析(ESDA)技术进行相关研究。从上海市出租汽车调度系统的GPS数据提取单个运营日9 921辆出租汽车的OD样本,按街道和时段统计出租汽车OD的时空发生量,定义并计算各区域各时段的载客空间密度指标,运用ESDA技术分时段计算、分析全局和局部莫伦值(Moran’s I)。全局分析结果表明,上海市出租汽车出行在全局分布上具有较强的空间正自相关性,这与城市中心商业区高度集中的活动分布有关。局部分析结果表明,上海市出租汽车出行分布呈“句”字形聚集结构。全局结构和局部结构在时间序列上变化较小,表明上海市出租汽车出行分布的时空结构明显且稳定。

关键词:出租汽车OD;出行时空分布;探索性数据分析;莫伦值;GPS

中图分类号:U491

文献标识码:A

Spatiotemporal Distribution of Taxi Services in Shanghai

Deng Zhongwei1, Ji Minhe1,2
(1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China; 2. China East-West Cooperation Research Center, East China Normal University, Shanghai 200062, China)

Abstract:Through a large sample survey, this paper uses Exploratory Spatial Data Analysis (ESPDA) to investigate the spatiotemporal distribution of taxi services in Shanghai. A GPS-based taxi service dataset is first retrieved to derive a total of 9 921 taxies’ OD, which are then tallied by individual street districts. An index known as Traffic Ratio Density is computed to characterize the level of taxi services for each street district and to facilitate the mapping of its spatiotemporal variation. In the end, the method of Exploratory Spatial Data Analysis is used to identify spatial clusters of taxi services over time. Both global and local Moran’s I values are computed for Shanghai as a whole and for individual street districts. The positive values of the global index strongly suggests high and stable concentration pattern across all the time-periods. The local index shows that the taxi OD pattern has a high-density cluster in the CBD area, versus the low-density cluster in the suburban regions, and between them there is a stochastic distribution. No noticeable temporal variation at either global or local level is identified, which indicates a rather stable spatial and temporal distribution of taxi service.

Keywords:taxi OD; spatiotemporal distribution; Exploratory Spatial Data Analysis(ESDA); Moran’s I; GPS