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.
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 language | English |
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Title of host publication | 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 |
Editors | Farnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam |
Publisher | Association for Computing Machinery, Inc |
Pages | 616-619 |
Number of pages | 4 |
ISBN (Electronic) | 9781450369091 |
ISBN (Print) | 978-1-4503-6909-1 |
DOIs | |
Publication status | Published - 5 Nov 2019 |
Event | 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, United States Duration: 5 Nov 2019 → 8 Dec 2019 http://sigspatial2019.sigspatial.org/ |
Conference
Conference | 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
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Abbreviated title | ACM SIGSPATIAL 2019 |
Country | United States |
City | Chicago, IL |
Period | 5/11/19 → 8/12/19 |
Internet address |
Keywords
- Dial-a-ride
- Dynamic scheduling
- GIS Cup
- Multi-agent system
- Taxi routing
- Trajectories