TY - GEN
T1 - Bayesian Network Modeling Applied on Railway Level Crossing Safety
AU - Liang, Ci
AU - Ghazel, Mohamed
AU - Cazier, Olivier
AU - Bouillaut, Laurent
AU - El-Koursi, El Miloudi
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Bayesian network modeling
KW - Level crossing safety
KW - Risk assessment
KW - Statistical analysis
KW - Train-car collision
UR - https://www.scopus.com/pages/publications/85034229064
U2 - 10.1007/978-3-319-68499-4_8
DO - 10.1007/978-3-319-68499-4_8
M3 - 会议稿件
AN - SCOPUS:85034229064
SN - 9783319684987
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 130
BT - Reliability, Safety, and Security of Railway Systems
A2 - Lecomte, Thierry
A2 - Romanovsky, Alexander
A2 - Fantechi, Alessandro
PB - Springer Verlag
T2 - 2nd International Conference on Reliability, Safety, and Security of Railway Systems, RSSRail 2017
Y2 - 14 November 2017 through 16 November 2017
ER -