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Machine Learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles

Abstract : In this thesis are investigated methods to manage shared electric autonomous taxi urban systems under online context in which customer demands occur over time, and where vehicles are available for ride-sharing and require electric recharging management. We propose the heuristics based on problem decomposition which include road network repartition and highlighting of subproblems such as charging management, empty vehicle redistribution and dynamic ride-sharing.The set of new methods for empty vehicle redistribution is proposed, such as proactive, meaning to take into account both current demand and anticipated future demand, in contrast to reactive methods, which act based on current demand only.We provide the reinforcement learning in different levels depending on granularity of the system.We propose station-based RL model for small networks and zone-based RL model, where the agents are zones of the city obtained by partitioning, for huge ones. The complete information optimisation is provided in order to analyse the system performance a-posteriori in offline context.The evaluation of the performance of proposed methods is provided in set of road networks of different nature and size. The proposed method provides promising results outperforming the other tested methods and the real data on the taxi system performance in terms of number of satisfied passengers under fixed fleet size.
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Submitted on : Thursday, May 20, 2021 - 11:33:10 AM
Last modification on : Wednesday, October 20, 2021 - 12:24:55 AM
Long-term archiving on: : Saturday, August 21, 2021 - 6:27:30 PM


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  • HAL Id : tel-03230845, version 1


Tatiana Babicheva. Machine Learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles. Networking and Internet Architecture [cs.NI]. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG023⟩. ⟨tel-03230845⟩



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