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Title	A Deep Reinforcement Learning Approach For Online Taxi Dispatching
ID_Doc	1389
Authors	Wang Y.; Zhang T.; Wei Z.
Year	2023
Published	Proceedings - 2023 International Conference on Networking and Network Applications, NaNA 2023
DOI	http://dx.doi.org/10.1109/NaNA60121.2023.00113
Abstract	With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network. © 2023 IEEE.
Author Keywords	component; deep reinforcement learning; order assignment; vehicle dispatching

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