This paper presents a new pathway towards the public health resilience, through the development of a principled understating on the post-hazard emergency transfer of the injured population across densely-populated urban communities, considering the deployment of connected and autonomous vehicles (CAVs). Given the influence on the system resilience of several parameters such as the number and distribution of CAVs, the initial geographic distribution of the injured and the spatiotemporal evolution of the functionality of the integrated hospital-road networks, a multi agent-based modelling (ABM) framework has been established to identify relevant patterns and bottlenecks in injured transfers across hazard-impacted urban communities. In such an ABM framework, each individual vehicle, transferring an injured inhabitant, is modelled as an independent agent, whose traveling is shaped by pre-defined behavioral attributes, while the interplay among those agents is also considered, throughout the entire transfer campaign. Based on a hypothetically catastrophic earthquake scenario, such an ABM framework is employed to model the city-scale, post-shock transfer across Tangshan city, located in one of the most earthquake-prone regions of China. The simulation outcome reveals that the information sharing with regard to the real-time functionality of the local hospital system plays a strategically crucial role, to the avoidance of uncoordinated and prolonged transfers. Furthermore, owing to their capability of intelligent route planning, the participation of CAVs can substantially bolster the rapidity and effectiveness of post-shock transfer campaigns.


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    Title :

    Modeling post-shock emergency transfers with the participation of connected-and-autonomous vehicles


    Contributors:
    Zhao, T (author) / Wang, J (author) / Sun, L (author) / D'Ayala, D (author)

    Publication date :

    2022-12-01


    Remarks:

    International Journal of Disaster Risk Reduction , 83 , Article 103436. (2022)


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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