Community-Based Trip Sharing for Urban Commuting

Authors

  • Mohd. Hafiz Hasan University of Michigan
  • Pascal Van Hentenryck University of Michigan
  • Ceren Budak University of Michigan
  • Jiayu Chen University of Michigan
  • Chhavi Chaudhry University of Michigan

DOI:

https://doi.org/10.1609/aaai.v32i1.12207

Abstract

This paper explores Community-Based Trip Sharing which uses the structure of communities and commuting patterns to optimize car or ride sharing for urban communities. It introduces the Commuting Trip Sharing Problem (CTSP) and proposes an optimization approach to maximize trip sharing. The optimization method, which exploits trip clustering, shareability graphs, and mixed-integer programming, is applied to a dataset of 9000 daily commuting trips from a mid-size city. Experimental results show that community-based trip sharing reduces daily car usage by up to 44%, thus producing significant environmental and traffic benefits and reducing parking pressure. The results also indicate that daily flexibility in pairing cars and passengers has significant impact on the benefits of the approach, revealing new insights on commuting patterns and trip sharing.

AAAI 2018 Proceedings Cover

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Published

2018-04-26

How to Cite

Hasan, M. H., Van Hentenryck, P., Budak, C., Chen, J., & Chaudhry, C. (2018). Community-Based Trip Sharing for Urban Commuting. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12207

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Main Track: Search and Constraint Satisfaction