Safety and security of guided public transportation systems, such as subways, light rails, and, to some extent, APMs, is impacted by various factors. Passengers or people in general may be endangered when falling onto the tracks by driving trains or may constitute a threat to the subway system when willfully entering and damaging the system. The detection system presented in this paper is based on intelligent data sensing and video image analysis (camera, laser combination). It puts a virtual supervisory zone on the platform and the platform track of stations. Several detection modules analyze dedicated parts of the platform and the track and generate alarms in case of dangerous situations. Comprehensive experience has been accumulated for the last 10 years from various test/benchmark installations, e.g., Munich or New York City subway or fully-operational systems, such as the Berlin subway. Functional feasibility and technical stability of the proposed system architecture have been demonstrated; that is, the sensor system can reliably detect any intrusion and just causes a very low number of false detections, which can still be handled without serious impact on train operations.


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    Title :

    Enhancing Track Safety and Asset Security of Subways by Video Data Analytics


    Contributors:

    Conference:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Publication date :

    2022-08-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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