Adaptive operating hours for improved performance of taxi fleets
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 t...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/6130 https://ink.library.smu.edu.sg/context/sis_research/article/7133/viewcontent/aamas21.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 |
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