Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2021
Abstract
Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on the strategic decisions in (ii).We first model this complex real-world decision making problem (with thousands of taxi drivers) as a multi-stage stochastic congestion game with a non-dedicated set of agents (i.e., agents start operation at a random stage and exit the game after a fixed time), where there is a dynamic population of agents (constrained by the maximum number of drivers). We provide planning and learning methods for computing the ideal operating hours in such a game, so as to improve efficiency of the overall fleet. In our experimental results, we demonstrate that our planning-based approach provides up to 16% improvement in revenue over existing method on a real-world taxi dataset. The learning-based approach further improves the performance and achieves up to 10% more revenue than the planning approach
Keywords
Equilibrium Solution, Game Theory, Optimization
Discipline
Artificial Intelligence and Robotics | Transportation
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Virtual Conference, May 3-7
First Page
728
Last Page
736
ISBN
9781450383073
Identifier
10.5555/3463952.3464040
Publisher
IFAAMAS
City or Country
Richland, SC
Citation
KUMAR, Rajiv Ranjan; VARAKANTHAM, Pradeep; and CHENG, Shih-Fen.
Adaptive operating hours for improved performance of taxi fleets. (2021). Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Virtual Conference, May 3-7. 728-736.
Available at: https://ink.library.smu.edu.sg/sis_research/6130
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5555/3463952.3464040