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Bayesian Network Modeling Applied on Railway Level Crossing Safety

机译：贝叶斯网络建模在铁路平交道口安全中的应用

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摘要

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.
机译：如今，铁路运营的特点是越来越高的速度和更大的运输能力。安全是铁路运营中的核心问题，从事故/事件统计数据可以看出，铁路平交道口（LX）安全是铁路中最关键的问题之一。本文基于贝叶斯风险模型对LX事故的因果关系进行了分析。因果推理分析旨在调查可能导致LX事故的各种影响因素，并对这些因素的影响进行量化，从而确定对LX事故造成最大影响的关键因素。首先根据事故/事故征候数据进行详细的统计分析。然后，根据因果关系和统计结果建立贝叶斯风险模型。基于贝叶斯风险模型，可以通过前向推理来预测LX事故。此外，可以通过反向推理进行事故原因识别和影响因素评估。我们研究的主要输出结果允许提供改进措施，以降低与LX事故有关的风险并减少后果。

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