Bayesian Network Modeling Applied on Railway Level Crossing Safety

Nowadays, railway operation is characterized by increasingly high speed and large transport capacity. Safety is the core issue in railway operation, and as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, the causal reasoning analysis of LX accidents is carried out based on Bayesian risk model. The causal reasoning analysis aims to investigate various influential factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the accidents at LXs. A detailed statistical analysis is firstly carried out based on the accident/incident data. Then, a Bayesian risk model is established according to the causal relationships and statistical results. Based on the Bayesian risk model, the prediction of LX accident can be made through forward inference. Moreover, accident cause identification and influential factor evaluation can be performed through reverse inference. The main outputs of our study allow for providing improvement measures to reduce risk and lessen consequences related to LX accidents.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Pagination: 15p

Subject/Index Terms

Filing Info

  • Accession Number: 01688795
  • Record Type: Publication
  • Source Agency: Institut Francais des Sciences et Technologies des Transports, de l'Amenagement et des Reseaux (IFSTTAR)
  • Files: ITRD
  • Created Date: Dec 18 2018 10:14AM