Uncovering urban mobility patterns with massive floating car data
(English)In: Computers, Environment and Urban SystemsArticle in journal (Other academic) Submitted
Urban mobility patterns are crucial to understanding urban structures, with applications ranging from traffic forecasting to urban planning. This paper develops a bottom-up approach to assess urban mobility patterns in a quantitative manner based on over 14,200,000 GPS points obtained from 11,263 moving taxicabs in Wuhan, Hubei, China. These taxicabs are equipped with GPS devices and are continuously being driven; thus, the corresponding mobile data sets (i.e., floating car data) cover the entire urban open space and bear traffic characteristics. Consequently, such mobile data are unique and more suitable for urban mobility analysis. Instead of employing the commonly used trajectory methods, we divided the GPS points into moves and stops, focusing on the latter. We found that the time intervals for all of the stops demonstrate the scaling property; that is, the stops can be separated into far more short ones than long ones, which we believe to be typical of the traffic system. The long stops showed a cluster pattern in a self-organized way at different timelines. We extracted these spatiotemporal clusters in a natural way and found that their sizes bear a heavy-tailed distribution. We further analyzed their evolution in both time and space and then categorized them into hotspots and traffic jams, of which the distributions objectively and quantitatively suggest the dynamic and multiple nuclei of urban mobility patterns. This study also provides insights into research on mobile data from the perspective of a complex system.
Urban mobility patterns, floating car data, self-organized, heavy-tailed distribution, bottom-up and spatiotemporal cluster
IdentifiersURN: urn:nbn:se:kth:diva-89363OAI: oai:DiVA.org:kth-89363DiVA: diva2:502945
QS 201203282012-02-142012-02-142016-04-19Bibliographically approved