Conference Proceeding Article
In this paper, we describe the design, analysis, implementation, and operational deployment of a real-time trip information system that provides passengers with the expected fare and trip duration of the taxi ride they are planning to take. This system was built in cooperation with a taxi operator that operates more than 15,000 taxis in Singapore. We first describe the overall system design and then explain the efficient algorithms used to achieve our predictions based on up to 21 months of historical data consisting of approximately 250 million paid taxi trips. We then describe various optimisations (involving region sizes, amount of history, and data mining techniques) and accuracy analysis (involving routes and weather) we performed to increase both the runtime performance and prediction accuracy. Our large scale evaluation demonstrates that our system is (a) accurate—with the mean fare error under 1 Singapore dollar ( 0.76 US$) and the mean duration error under three minutes, and (b) capable of real-time performance, processing thousands to millions of queries per second. Finally, we describe the lessons learned during the process of deploying this system into a production environment.
Taxi Fleets, Trip Information Service, Partition-based Predictions, Nearest Neighbour Queries, History-based Predictions
Software Engineering | Transportation
Software and Cyber-Physical Systems
MobiSys '11: Proceedings of the 9th International Conference on Mobile Systems, Applications and Services: June 28 - July 1, 2011, Bethesda, MD
City or Country
BALAN, Rajesh Krishna; KHOA, Nguyen Xuan; and JIANG, Lingxiao.
Real-time trip information service for a large taxi fleet. (2011). MobiSys '11: Proceedings of the 9th International Conference on Mobile Systems, Applications and Services: June 28 - July 1, 2011, Bethesda, MD. 99-112. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1678
Creative Commons License
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