Track Circuit Reliability Assessment for Preventing Railway Accidents

The safe operation of a railway signaling system depends on accurate and up-to-date information on the position and movements of trains provided by train detection devices. Track circuits are the most popular systems used over the world to provide information on position and movements of trains and ensure safety of circulations. Track circuits’ failure or malfunction have two consequences: operating unreliability causing train delays, and accidents. In this paper, the authors focus on potential accidents. In close cooperation with experts and practitioners of the French national railways company SNCF, the authors have designed a reliability model of track circuits taking into account local conditions: environment, vehicles, track, traffic, track circuit technology and use. Combined with an assessment of potential accidents’ severity due to TC failure, this model aims at supporting managers’ strategies to reduce the risks and optimize resources allocation. This model is also used to predict potential locations of track circuits that may generate malfunctions, using a measure of similarity with track circuits’ locations where malfunctions occurred. Using the database of past malfunctions, the similarity measure uses the model’s parameters to set up and update a classification of malfunction types. Each type is compared with the database of track circuits to identify those that are similar enough to raise attention and check if appropriate prevention measures are already in place or need to be added. The model is used as a support tool by the local staff to optimize the resources needed to ensure safety by setting priorities among track circuits and determining which ones need prevention and/or protection measures. The model was designed and implemented in a French region where track circuits malfunctions have a higher probability to occur due to climatic conditions in autumn that pollute the wheel-rail contact.

Language

  • English

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Filing Info

  • Accession Number: 01684494
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 19 2018 3:07PM