IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance “intelligence” of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management


    Beteiligte:
    Zhu, Fenghua (Autor:in) / Lv, Yisheng (Autor:in) / Chen, Yuanyuan (Autor:in) / Wang, Xiao (Autor:in) / Xiong, Gang (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    2020-10-01


    Format / Umfang :

    2710946 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

    Shao, Yunli / Wang, Chieh (Ross) / Berres, Andy et al. | ASCE | 2022


    Smart Traffic Management System using IoT Enabled Technology

    Bali, Vikram / Mathur, Sonali / Sharma, Vishnu et al. | IEEE | 2020


    Traffic Congestion Detection for Smart and Control Transportation Management

    Khalifa, Othman O. / Marzuki, Azri A. / Abdul Malik, Noreha et al. | British Library Conference Proceedings | 2022


    Traffic Congestion Detection for Smart and Control Transportation Management

    Khalifa, Othman O. / Marzuki, Azri A. / Abdul Malik, Noreha et al. | Springer Verlag | 2021