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A Decremental Search Approach for Large Scale Dynamic Ridesharing

  • Ali Shemshadi
  • Quan Z. Sheng
  • Wei Emma Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8786)

Abstract

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.

Keywords

Taxi Ridesharing Web of Things Spatio-temporal Data Incomplete Data 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ali Shemshadi
    • 1
  • Quan Z. Sheng
    • 1
  • Wei Emma Zhang
    • 1
  1. 1.School of Computer ScienceThe University of AdelaideAustralia

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