Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2016

Abstract

Bike Sharing Systems (BSSs) experience a significant loss in customer demand due to starvation (empty base stations precluding bike pickup) or congestion (full base stations precluding bike return). Therefore, BSSs operators reposition bikes between stations with the help of carrier vehicles. Due to unpredictable and dynamically changing nature of the demand, myopic reasoning typically provides a below par performance. We propose an online and robust repositioning approach to minimise the loss in customer demand while considering the possible uncertainty in future demand. Specifically, we develop a scenario generation approach based on an iterative two player game to compute a strategy of repositioning by assuming that the environment can generate a worse demand scenario (out of the feasible demand scenarios) against the current repositioning solution. Extensive computational results from a simulation built on real world data set of bike sharing company demonstrate that our approach can significantly reduce the expected lost demand over the existing benchmark approaches.

Keywords

Bike Sharing Systems, Robustness, Fictitious Play

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 25th International Joint Conference on Artificial Intelligence IJCAI 2016: New York, July 9-15

First Page

3096

Last Page

3102

ISSN

1045-0823

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

Authors/LARC

Additional URL

https://www.ijcai.org/Proceedings/16/Papers/439.pdf

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