Reinforcement Learning for Dynamic Optimization of Eco-Driving in Smart Healthcare Transportation Networks

Smart transportation networks face increasing demands for efficiency and sustainability. This study presents a reinforcement learning approach that optimizes eco-driving strategies for connected and automated vehicles (CAVs) in urban environments, with a particular application to healthcare logistic...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems pp. 1 - 12
Main Authors: Cai, Wang, Anwlnkom, Tomley, Zhang, Lingling, Basheer, Shakila, Yang, Jing
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Adaptation models
dynamic optimization
eco-driving
Energy efficiency
Heuristic algorithms
Logistics
Medical services
Optimization
Reinforcement learning
smart healthcare transportation networks
Transportation
twin delayed deep deterministic policy gradient algorithm
Urban areas
Vehicle dynamics
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Smart transportation networks face increasing demands for efficiency and sustainability. This study presents a reinforcement learning approach that optimizes eco-driving strategies for connected and automated vehicles (CAVs) in urban environments, with a particular application to healthcare logistics. Specifically, we propose a novel approach using reinforcement learning, specifically a twin delayed deep deterministic policy gradient (TD3) algorithm, to dynamically optimize CAV trajectories at signalized intersections. The proposed healthcare eco-driving trajectory optimization (TD3-HETO) model incorporates real-time traffic conditions, signal timing information, and healthcare urgency levels to generate optimal acceleration profiles. The reward function is designed to balance energy efficiency, traffic flow, safety, comfort, and healthcare delivery timeliness. Additionally, the model introduces a dynamic exploration strategy that adapts to healthcare task urgency, enabling efficient balancing between energy consumption and delivery timelines. Experimental results show that TD3-HETO reduces energy consumption by up to 28.7% compared to baseline methods while improving average speeds by 3.7% for urgent healthcare deliveries. The model achieves superior safety performance with 98.7% of time steps showing zero conflicts, compared to 95.3% for the best baseline. TD3-HETO also demonstrates remarkable adaptability to varying traffic demands and signal timings, maintaining consistent performance even at high traffic volumes. This research contributes to developing intelligent transportation systems to enhance environmental sustainability and healthcare accessibility in smart cities, potentially improving patient outcomes and operational efficiency in urban healthcare logistics.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2025.3561034