Road traffic is the most widely used for transport security, which have the significant disaster vulnerability. It often becomes the weak link when the evacuation and rescue. MapX is an ActiveX control product with powerful map analysis functions offered by MapInfo Company. Because it does not support the topology of spatial data, the shortest path analysis has become a hot potato for users to develop application systems. In this paper, several key technologies to establish topology structure of road network based on MapX are discussed. On the base, an algorithm for finding the shortest path in post-disaster road network is presented, and experimental results show that the proposed algorithm is more practical and effective. At last, a brief conclusion as well as the expectation for the further study is made.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Building Topological Structure of the Post-Disaster Road Network Based MapX


    Contributors:
    Yin, Xu-Ri (author)


    Publication date :

    2014


    Size :

    4 Seiten




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Building Topological Structure of the Post-Disaster Road Network Based MapX

    Yin, X.R. | British Library Conference Proceedings | 2014


    Post-Disaster Reconstruction of Road Infrastructure

    Akvan Gajanayake / Tehmina Khan / Guomin Zhang | DOAJ | 2020

    Free access

    Post-disaster recovery sequencing strategy for road networks

    Gokalp, Can / Patil, Priyadarshan N. / Boyles, Stephen D. | Elsevier | 2021


    Urban road network post-disaster recovery method and system considering traffic toughness

    SUN JIANCHENG / DUAN XIUHUI / SUN XIAODONG et al. | European Patent Office | 2023

    Free access

    Practice-based post-disaster road network connectivity analysis using a data-driven percolation theory-based method

    Chang, Kuo-Hao / Sheu, Jiuh-Biing / Chen, Yenming J. et al. | Elsevier | 2023