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Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

School of Information Engineering, China University of Geosciences, Wuhan 430074, China
National Engineering Research Center for GIS, Wuhan 430074, China
Department of Urban and Regional Planning, State University of New York, Buffalo, NY 14214, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 373;
Received: 26 September 2017 / Revised: 2 November 2017 / Accepted: 13 November 2017 / Published: 19 November 2017
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
PDF [7864 KB, uploaded 21 November 2017]


Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation. View Full-Text
Keywords: trajectory data mining; taxi planning; spatio-temporal trajectory model; load balance trajectory data mining; taxi planning; spatio-temporal trajectory model; load balance

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Wu, L.; Hu, S.; Yin, L.; Wang, Y.; Chen, Z.; Guo, M.; Chen, H.; Xie, Z. Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model. ISPRS Int. J. Geo-Inf. 2017, 6, 373.

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