U-Air: when urban air quality inference meets big data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling urban traffic dynamics in coexistence with urban data streams
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
An opportunistic client user interface to support centralized ride share planning
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).