A decremental search approach for large scale dynamic ridesharing

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6 Citations (Scopus)


The Web of Things (WoT) paradigm introduces novel applications to improve the quality of human lives. Dynamic ridesharing is one of these applications, which holds the potential to gain significant economical, environmental, and social benefits particularly in metropolitan areas. Despite the recent advances in this area, many challenges still remain. In particular, handling large-scale incomplete data has not been adequately addressed by previous works. Optimizing the taxi/passengers schedules to gain the maximum benefits is another challenging issue. In this paper, we propose a novel system, MARS (Multi-Agent Ridesharing System), which addresses these challenges by formulating travel time estimation and enhancing the efficiency of taxi searching through a decremental search approach. Our proposed approach has been validated using a real-world dataset that consists of the trajectories of 10,357 taxis in Beijing, China.

Original languageEnglish
Pages (from-to)202-217
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2014
Externally publishedYes
Event15th International Conference on Web Information Systems Engineering: WISE 2014 - Thessaloniki, Greece
Duration: 12 Oct 201414 Oct 2014


  • Incomplete Data
  • Spatio-temporal Data
  • Taxi Ridesharing
  • Web of Things


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