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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
Titel27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
RedacteurenFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
UitgeverijAssociation for Computing Machinery, Inc
Pagina's616-619
Aantal pagina's4
ISBN van elektronische versie9781450369091
ISBN van geprinte versie978-1-4503-6909-1
DOI's
StatusGepubliceerd - 5 nov 2019
Evenement27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, Verenigde Staten van Amerika
Duur: 5 nov 20198 dec 2019
http://sigspatial2019.sigspatial.org/

Congres

Congres27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Verkorte titelACM SIGSPATIAL 2019
LandVerenigde Staten van Amerika
StadChicago, IL
Periode5/11/198/12/19
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  • Citeer dit

    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 (editors), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (blz. 616-619). Association for Computing Machinery, Inc. https://doi.org/10.1145/3347146.3363348