Reducing vehicle exhaust pollution and energy consumption is of great significance for improving the sustainability of social development. Currently, most energy-efficient and regenerative energy recovery methods are from a vehicle control perspective, ignoring the impact on the overall traffic environment. An important reason is that the transportation system's large scale, complexity and social nature restrict its energy-efficient development. Hence, this letter proposes energy-efficient and regenerative energy recovery schemes for sustainable intelligent transportation system using the Artificial societies, Computational experiments, Parallel execution (ACP) framework. The framework includes three parts: energy-efficient oriented intelligent road infrastructure design, transportation traffic flow and vehicle velocity profile planning co-design, and cloud-based vehicle engine parameter calibration. This letter is the second part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL) and aims to enhance the sustainability of transportation system from the energy-efficient perspective.


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    Titel :

    ACP-Based Energy-Efficient Schemes for Sustainable Intelligent Transportation Systems


    Beteiligte:
    Chen, Jicheng (Autor:in) / Zhang, Yongkang (Autor:in) / Teng, Siyu (Autor:in) / Chen, Yuanyuan (Autor:in) / Zhang, Hui (Autor:in) / Wang, Fei-Yue (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.05.2023


    Format / Umfang :

    744323 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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