Complete vehicle coverage trajectory data is of fundamental importance to traffic signal control strategy and intelligent transport system (ITS). There are mainly two categories in traffic data collection: stationary and mobile sensing. However, neither can offer near-complete spatial and temporal coverage, especially at traffic signals. This study first proposes a full-scale automated and connected UAV (AC-UAV) system, which consists of transformable UAVs with automated landing and take-off capabilities, cooperative charging piles, and a fleet management center. Secondly, based on the developed AC-UAV system, we introduce a 3-step methodological framework: multiple vehicle detection algorithm (MVD) based on deep learning and multiple vehicle tracking (MVT) algorithm based on data feature association and trajectories reconstruction. The field experiments were conducted in Xi’an, China. The results show that the proposed framework based on the AC-UAV system is capable of conducting mobile complete traffic data analysis tasks and also feasible for large-scale automated city applications.


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    Title :

    AC-UAV System for Complete Vehicle Coverage Trajectory Reconstruction: Methodology Framework and Field Experiment


    Contributors:
    Wang, Kaiping (author) / Yang, Rong (author) / Shi, Dahu (author) / Li, Meng (author)

    Conference:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Published in:

    CICTP 2020 ; 1255-1266


    Publication date :

    2020-12-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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