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Smart city article details

Title	A Deep Reinforcement Learning Strategy For Intelligent Transportation Systems
ID_Doc	1391
Authors	Giannini F.; Franzè G.; Fortino G.; Pupo F.
Year	2024
Published	Internet of Things, Part F1719
DOI	http://dx.doi.org/10.1007/978-3-031-42194-5_10
Abstract	This chapter proposes a new routing strategy, based on Deep Reinforcement Learning, to improve traffic flow and decrease congestion in the context of smart cities. The idea is to perform routing decisions in real time while considering the actual traffic situation on the overall road network. These high-level routing actions are then translated into set-points for set-theoretic receding horizon controllers, which are in charge of computing the more adequate control action for each involved vehicle. To adequately analyze the performance of the resulting control architecture, the SUMO and MATLAB environments are used to implement complex operating scenarios where road maps data and vehicle state trajectories can be shared and exchanged. Finally, some simulations, performed considering a real city district, show that the proposed algorithm can successfully realize real-time routing decisions and reduce waiting time despite dynamic changes within the road environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Author Keywords	Artificial intelligence; Autonomous vehicles; Deep Q learning; Intelligent transportation systems; Machine learning; Model predictive control; Reduction of traffic congestion; Reinforcement learning; Routing algorithms; Traffic simulation

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