Journal
CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE
Volume 43, Issue 2, Pages 103-114Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15230406.2015.1014424
Keywords
Human mobility; Bayes' theorem; activity inference; travel patterns; taxi trajectory
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Funding
- Natural Science Foundation of China [41271386, 41428102]
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Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched. The probability of points of interest to be visited is modeled by Bayes' rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our results closely approximate the residents' travel survey data in Shanghai. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions. In the era of big data, we encounter the dilemma of trajectory data rich but activity information poor when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals' trajectories.
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