Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison
Abstract
:1. Introduction
1.1. Background
1.2. Related Studies
1.3. Contributions and Organization
- (1)
- We propose an improved ACN model (WLDACN) to model the relationship between hazards and accidents. The shortest distances from hazards to accidents can be obtained based on the proposed new length metrics of edges in WLDACN, which is proven to be superior to other previous methods.
- (2)
- Given the proposed length metrics of WLDACN, the network efficiency is used to represent the difficulty of hazards causing accidents. Therefore, the accident prevention problem is transferred to successfully minimize the WLDACN efficiency.
- (3)
- To support the hazard management of railway systems, we propose a high centrality adaptive and integer programming method to identify critical hazards that greatly contribute to railway accidents. A heuristic algorithm is proposed to solve the integer programming model. The comparison results show that the integer programming method can help prevent accidents better than other models.
2. Problem Description and Formulation
2.1. ACN Model
2.1.1. Edge Weight Metrics
2.1.2. Edge Length Metrics
2.2. CHI Method Development
2.2.1. Objective of CHI
2.2.2. High Centrality Adaptive Methods
2.2.3. Integer Programming Method
2.3. Model Performance Comparison
3. Case Studies
3.1. Data Description
3.2. ACN Construction and Analysis
3.3. CHI Model Application and Comparison
3.4. ACN Model Comparison
3.5. Limitation of the Method
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Katsakiori, P.; Sakellaropoulos, G.; Manatakis, E. Towards an evaluation of accident investigation methods in terms of their alignment with accident causation models. Saf. Sci. 2009, 47, 1007–1015. [Google Scholar] [CrossRef]
- Hollnagel, E. Barriers and Accident Prevention; Routledge: London, UK, 2016. [Google Scholar]
- Hosseinian, S.S.; Torghabeh, Z.J. Major theories of construction accident causation models: A literature review. Int. J. Adv. Eng. Technol. 2012, 4, 53. [Google Scholar]
- Heinrich, H.W. Industrial Accident Prevention. A Scientific Approach, 2nd ed.; McGraw-Hill Book Company, Inc.: New York, NY, USA; London, UK, 1941. [Google Scholar]
- Weaver, D. Symptoms of operational error. Prof. Saf. 1971, 104, 39–42. [Google Scholar]
- Bird, F.E.; Cecchi, F.; Tilche, A.; Mata-Alvarez, J. Management Guide to Loss Control; Institute Press: Durham, NC, USA, 1974. [Google Scholar]
- Hughes, P.; Ferrett, E. Introduction to Health and Safety in Construction; Routledge: London, UK, 2012. [Google Scholar]
- Kyriakidis, M.; Majumdar, A.; Grote, G.; Ochieng, W.Y. Development and assessment of taxonomy for performance-shaping factors for railway operations. Transp. Res. Rec. 2012, 2289, 145–153. [Google Scholar] [CrossRef]
- San Kim, D.; Baek, D.H.; Yoon, W.C. Development and evaluation of a computer-aided system for analyzing human error in railway operations. Reliab. Eng. Syst. Saf. 2010, 95, 87–98. [Google Scholar]
- Baysari, M.T.; McIntosh, A.S.; Wilson, J.R. Understanding the human factors contribution to railway accidents and incidents in Australia. Accid. Anal. Prev. 2008, 40, 1750–1757. [Google Scholar] [CrossRef]
- Madigan, R.; Golightly, D.; Madders, R. Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents. Accid. Anal. Prev. 2016, 97, 122–131. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, M.; Wei, W.; Liao, G.; Chu, F. The impact of personality on driving safety among Chinese high-speed railway drivers. Accid. Anal. Prev. 2016, 92, 9–14. [Google Scholar] [CrossRef] [PubMed]
- Hollnagel, E. Understanding accidents-from root causes to performance variability. In Proceedings of the IEEE 7th Conference on Human Factors and Power Plants, Scottsdale, Arizona, 15–19 September 2002; pp. 1–4. [Google Scholar]
- Underwood, P.; Waterson, P. Systemic accident analysis: Examining the gap between research and practice. Accid. Anal. Prev. 2013, 55, 154–164. [Google Scholar] [CrossRef] [Green Version]
- San Kim, D.; Yoon, W.C. An accident causation model for the railway industry: Application of the model to 80 rail accident investigation reports from the UK. Saf. Sci. 2013, 60, 57–68. [Google Scholar]
- Ouyang, M.; Hong, L.; Yu, M.H.; Fei, Q. STAMP-based analysis on the railway accident and accident spreading: Taking the China–Jiaoji railway accident for example. Saf. Sci. 2010, 48, 544–555. [Google Scholar] [CrossRef]
- Li, C.; Tang, T.; Chatzimichailidou, M.M.; Jun, G.T.; Waterson, P. A hybrid human and organisational analysis method for railway accidents based on STAMP-HFACS and human information processing. Appl. Ergon. 2019, 79, 122–142. [Google Scholar] [CrossRef] [Green Version]
- Zhan, Q.; Zheng, W.; Zhao, B. A hybrid human and organizational analysis method for railway accidents based on HFACS-Railway Accidents (HFACS-RAs). Saf. Sci. 2017, 91, 232–250. [Google Scholar] [CrossRef]
- Cao, T.; Mu, W.; Gou, J.; Peng, L. A study of risk relevance reasoning based on a context ontology of railway accidents. Risk Anal. 2020, 40, 1589–1611. [Google Scholar] [CrossRef]
- Lin, C.Y.; Rapik Saat, M.; Barkan, C.P.L. Quantitative causal analysis of mainline passenger train accidents in the United States. Proc. Inst. Mech. Eng. Part. F J. Rail Rapid Transit. 2020, 234, 869–884. [Google Scholar] [CrossRef]
- Chen, D.; Xu, C.; Ni, S. Data mining on Chinese train accidents to derive associated rules. Proc. Inst. Mech. Eng. Part. F J. Rail Rapid Transit. 2017, 231, 239–252. [Google Scholar] [CrossRef]
- Read, G.J.M.; Naweed, A.; Salmon, P.M. Complexity on the rails: A systems-based approach to understanding safety management in rail transport. Reliab. Eng. Syst. Saf. 2019, 188, 352–365. [Google Scholar] [CrossRef]
- An, M.; Huang, S.; Baker, C.J. Railway risk assessment-the fuzzy reasoning approach and fuzzy analytic hierarchy process approaches: A case study of shunting at waterloo depot. Proc. Inst. Mech. Eng. Part. F J. Rail Rapid Transit. 2007, 221, 365–383. [Google Scholar] [CrossRef]
- Guo, S.; Zhou, X.; Tang, B.; Gong, P. Exploring the behavioral risk chains of accidents using complex network theory in the construction industry. Phys. A Stat. Mech. Appl. 2020, 560, 125012. [Google Scholar] [CrossRef]
- Xin, M.; Ke-Ping, L.; Zi-Yan, L.; Jin, Z. Analyzing the causation of a railway accident based on a complex network. Chin. Phys. B 2013, 23, 028904. [Google Scholar]
- Li, Q.; Song, L.; List, G.F.; Deng, Y.; Zhou, Z.; Liu, P. A new approach to understand metro operation safety by exploring metro operation hazard network (MOHN). Saf. Sci. 2017, 93, 50–61. [Google Scholar] [CrossRef]
- Zhou, J.; Xu, W.; Guo, X.; Ding, J. A method for modeling and analysis of directed weighted accident causation network (DWACN). Phys. A Stat. Mech. Appl. 2015, 437, 263–277. [Google Scholar] [CrossRef]
- Liu, J.; Schmid, F.; Zheng, W.; Zhu, J. Understanding railway operational accidents using network theory. Reliab. Eng. Syst. Saf. 2019, 189, 218–231. [Google Scholar] [CrossRef]
- Ai, X. Node importance ranking of complex networks with entropy variation. Entropy 2017, 19, 303. [Google Scholar] [CrossRef] [Green Version]
- Qiao, T.; Shan, W.; Zhou, C. How to identify the most powerful node in complex networks? A novel entropy centrality approach. Entropy 2017, 19, 614. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Cai, W.; Li, Y.; Du, X. Key node ranking in complex networks: A novel entropy and mutual information-based approach. Entropy 2020, 22, 52. [Google Scholar] [CrossRef] [Green Version]
- Nguyen, D.T.; Shen, Y.; Thai, M.T. Detecting critical nodes in interdependent power networks for vulnerability assessment. IEEE Trans. Smart Grid 2013, 4, 151–159. [Google Scholar] [CrossRef]
- Han, C.; Sun, X.; Yang, Y.; Che, Y.; Qin, Y. Brain complex network characteristic analysis of fatigue during simulated driving based on electroencephalogram signals. Entropy 2019, 21, 353. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, K.; Wang, M.; Cao, Y.; Zhu, W.; Wu, J.; Yan, X. A Comprehensive Risk Analysis of Transportation Networks Affected by Rainfall-Induced Multihazards. Risk Anal. 2018, 38, 1618–1633. [Google Scholar] [CrossRef] [PubMed]
- Valério, D.; Lopes, A.M.; Tenreiro Machado, J.A. Entropy analysis of a railway network’s complexity. Entropy 2016, 18, 388. [Google Scholar] [CrossRef] [Green Version]
- Xu, H.; Zhang, Y.; Li, H.; Skitmore, M.; Yang, J. Safety risks in rail stations: An interactive approach. J. Rail Transp. Plan. Manag. 2019, 11, 100148. [Google Scholar] [CrossRef]
- Xuelan, F.; Kennedy, G. Expressing causation in written English. Relc J. 1992, 23, 62–80. [Google Scholar] [CrossRef]
- Fan, C.; Zeng, L.; Sun, Y.; Liu, Y.Y. Finding key players in complex networks through deep reinforcement learning. Nat. Mach. Intell. 2020, 2, 317–324. [Google Scholar] [CrossRef]
- He, Z.; Navneet, K.; van Dam, W.; Van Mieghem, P. Robustness assessment of multimodal freight transport networks. Reliab. Eng. Syst. Saf. 2021, 207, 107315. [Google Scholar] [CrossRef]
Variable | Description |
---|---|
Abbreviations | |
CHI | critical hazard indentification |
ACN | accident causality network |
WDACN | weighted direct accident causality network |
DACN | direct accident causality network |
SCP | shortest causation path |
MPCP | most probable causation path |
WLDACN | directed ACN with weights and length metrics |
HDA | high degree adaptive |
HBA | high betweenness adaptive |
HCA | high closeness adaptive |
HPA | high pagerank adaptive |
ASCP | average SCP |
IPM | Integer Programming method |
Notations | |
hazard node | |
a | accident node |
the weight of the edge from node to | |
The frequencies of the causation route from hazard node to accident node | |
the set of points on causation route | |
the normalized weight of the edge from node to | |
the active probability of causation route | |
the length of the edge from node to | |
the ACN efficiency | |
degree centrality of hazard node | |
betweenness centrality of hazard node | |
closeness centrality of hazard node | |
PageRank of hazard node |
Accident Type | A01 | A02 | A03 | A04 | A05 | A06 | A07 | A08 | A09 |
---|---|---|---|---|---|---|---|---|---|
H-type hazard | 4.19 | 4.35 | -- | 3.42 | 5.73 | 3.50 | 4.89 | 3.71 | 5.98 |
EM-type hazard | 4.54 | 5.16 | 598 | 3.54 | 5.98 | 5.47 | 5.07 | 3.83 | 7.04 |
E-type hazard | 5.80 | 5.98 | 5.98 | 5.62 | 9.03 | 6.90 | 5.98 | 5.91 | 7.37 |
M-type hazard | 5.88 | 6.09 | -- | 5.19 | 8.34 | 4.78 | 5.58 | 5.47 | 8.47 |
All types | 5.10 | 5.39 | 5.98 | 4.44 | 7.27 | 5.16 | 5.38 | 4.73 | 7.22 |
Accident type | A10 | A11 | A12 | A13 | A14 | A15 | A16 | A17 | A18 |
H-type hazard | 3.71 | 3.71 | 5.73 | 5.04 | 5.73 | 5.73 | 5.04 | -- | 5.76 |
EM-type hazard | 3.83 | 3.82 | 7.58 | 6.89 | 7.58 | 5.29 | 5.92 | 5.98 | 5.98 |
E-type hazard | 5.91 | 5.91 | 9.03 | 8.34 | 9.03 | 5.98 | 8.34 | 5.98 | 8.06 |
M-type hazard | 5.47 | 5.47 | 8.34 | 7.64 | 8.34 | 8.29 | 7.37 | -- | 9.21 |
All types | 4.73 | 4.73 | 7.67 | 6.98 | 7.67 | 6.32 | 6.67 | 5.98 | 7.25 |
Models | HBA | HCA | HDA | HPA | IPM |
---|---|---|---|---|---|
DACN | 27.3342 | 72.3560 | 32.4598 | 31.8879 | 27.1856 |
WDACN | 25.8917 | 70.3325 | 31.8904 | 30.0392 | 25.3462 |
WLDACN | 22.9560 | 67.7097 | 31.8904 | 30.0392 | 21.6170 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, Q.; Zhang, Z.; Peng, F. Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison. Entropy 2021, 23, 864. https://doi.org/10.3390/e23070864
Li Q, Zhang Z, Peng F. Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison. Entropy. 2021; 23(7):864. https://doi.org/10.3390/e23070864
Chicago/Turabian StyleLi, Qian, Zhe Zhang, and Fei Peng. 2021. "Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison" Entropy 23, no. 7: 864. https://doi.org/10.3390/e23070864
APA StyleLi, Q., Zhang, Z., & Peng, F. (2021). Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison. Entropy, 23(7), 864. https://doi.org/10.3390/e23070864