Mining spatiotemporal patterns of urban dwellers from taxi trajectory data

• Published in: Frontiers of earth science Vol. 10; no. 2; pp. 205 - 221
• Main Authors: MAO, Feng, JI, Minhe, LIU, Ting
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.06.2016; Springer Nature B.V
• Subjects: Commuting; Earth and Environmental Science; Earth Sciences; Global positioning systems; GPS; Research Article; Spatial analysis; spatial clustering; Spatial distribution; spatiotemporal pattern mining; taxi trajectory; Taxicabs; Urban areas; Urban structures; 全球定位系统; 出租车; 出行方式; 地理位置; 城市居民; 城市空间结构; 数据挖掘; 时空格局; China; taxi trajectory; spatial clustering; spatiotemporal pattern mining
• ISSN: 2095-0195; 2095-0209
• DOI: 10.1007/s11707-015-0525-4

With the widespread adoption of locationaware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps: spatial clustering of taxi origin-destination (OD) based on urban traffic grids to discover potentially meaningful places, identifying threshold values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.