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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| Vydáno v: | IEEE transactions on intelligent transportation systems s. 1 - 12 |
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2025
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| ISSN: | 1524-9050, 1558-0016 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cai, Wang Yang, Jing Basheer, Shakila Anwlnkom, Tomley Zhang, Lingling |
| Author_xml | – sequence: 1 givenname: Wang surname: Cai fullname: Cai, Wang email: caiwang@jzmu.edu.cn organization: Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China – sequence: 2 givenname: Tomley surname: Anwlnkom fullname: Anwlnkom, Tomley email: smart_6565@protonmail.com organization: College of Computer Science, Wichita State University, Wichita, KS, USA – sequence: 3 givenname: Lingling orcidid: 0009-0008-4425-7613 surname: Zhang fullname: Zhang, Lingling email: zhangling_beihua@163.com organization: College of Computer Science and Technology, Beihua University, Jilin, China – sequence: 4 givenname: Shakila orcidid: 0000-0001-9032-9560 surname: Basheer fullname: Basheer, Shakila email: sbbasheer@pnu.edu.sa organization: College of Computer and Information Systems, Princess Nourah bint Abdulrahman University, P.O. Box 844428, Riyadh, Saudi Arabia – sequence: 5 givenname: Jing orcidid: 0000-0002-0438-6006 surname: Yang fullname: Yang, Jing email: yangjing01@jzmu.edu.cn organization: Department of Pathology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China |
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| SubjectTerms | 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 |
| Title | Reinforcement Learning for Dynamic Optimization of Eco-Driving in Smart Healthcare Transportation Networks |
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