A sampling-based strategy for distributing taxis in a road network for occupancy maximization (GIS Cup)

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Abstract

We present a weighted sampling strategy for distributing a system of taxi agents on a road network. We consider a setting, in which each agent operates independently, following a prescribed strategy based on historical data. Furthermore, customer requests appear dynamically and are assigned to the closest unoccupied taxi agent.

We demonstrate that in this setting a simple sampling strategy based on the spatial distribution of historical data performs well in minimizing the average time that agents are unoccupied. The strategy is evaluated on taxi trip data in Manhattan and compared to various, more complex strategies.
Original languageEnglish
Title of host publication27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
PublisherAssociation for Computing Machinery, Inc
Pages616-619
Number of pages4
ISBN (Electronic)9781450369091
ISBN (Print)978-1-4503-6909-1
DOIs
Publication statusPublished - 5 Nov 2019
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, United States
Duration: 5 Nov 20198 Dec 2019
http://sigspatial2019.sigspatial.org/

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Abbreviated titleACM SIGSPATIAL 2019
CountryUnited States
CityChicago, IL
Period5/11/198/12/19
Internet address

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Keywords

  • Dial-a-ride
  • Dynamic scheduling
  • GIS Cup
  • Multi-agent system
  • Taxi routing
  • Trajectories

Cite this

Buchin, K. A., Kostitsyna, I., Custers, B., & Struijs, M. A. C. (2019). A sampling-based strategy for distributing taxis in a road network for occupancy maximization (GIS Cup). In F. Banaei-Kashani, G. Trajcevski, R. H. Guting, L. Kulik, & S. Newsam (Eds.), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (pp. 616-619). Association for Computing Machinery, Inc. https://doi.org/10.1145/3347146.3363348