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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94501完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴勇成 | zh_TW |
| dc.contributor.advisor | Yung-Cheng Lai | en |
| dc.contributor.author | 陳柏邑 | zh_TW |
| dc.contributor.author | Po-I Chen | en |
| dc.date.accessioned | 2024-08-16T16:23:48Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-09 | - |
| dc.identifier.citation | Baysari, M. T., McIntosh, A. S., & Wilson, J. R. (2008). Understanding the human factors contribution to railway accidents and incidents in Australia. Accident Analysis & Prevention, 40(5), 1750-1757.
Bin, N. (1970). Analysis of train braking accuracy and safe protection distance in automatic train protection (ATP) systems. WIT Transactions on The Built Environment, 20. Boehm, B. (1989, September). Software risk management. In European software engineering conference (pp. 1-19). Berlin, Heidelberg: Springer Berlin Heidelberg. Cheng, C. B., Shyur, H. J., & Kuo, Y. S. (2014). Implementation of a flight operations risk assessment system and identification of critical risk factors. Scientia Iranica, 21(6), 2387-2398. Chen, Y. F., Hsueh, K. C., & Lai, Y. C. (2021). Identification of high-risk driving behavior and sections for rail systems. Transportation research record, 2675(12), 1379-1392. Drivalou, S., & Marmaras, N. (2009). Supporting skill-, rule-, and knowledge-based behaviour through an ecological interface: An industry-scale application. International Journal of Industrial Ergonomics, 39(6), 947-965. Dorrian, J., Baulk, S. D., & Dawson, D. (2011). Work hours, workload, sleep and fatigue in Australian Rail Industry employees. Applied ergonomics, 42(2), 202-209. Dorrian, J., Roach, G. D., Fletcher, A., & Dawson, D. (2006). The effects of fatigue on train handling during speed restrictions. Transportation research part F: traffic psychology and behaviour, 9(4), 243-257. Ebrahimi, H., Sattari, F., Lefsrud, L., & Macciotta, R. (2021). Analysis of train derailments and collisions to identify leading causes of loss incidents in rail transport of dangerous goods in Canada. Journal of Loss Prevention in the Process Industries, 72, 104517. El Rashidy, R. A. H., Hughes, P., Figueres-Esteban, M., & Van Gulijk, C. (2018). Automated train driver competency performance indicators using real train driving data. In Safety and reliability–safe societies in a changing world (pp. 3071-3075). CRC Press. Ergai, A., Cohen, T., Sharp, J., Wiegmann, D., Gramopadhye, A., & Shappell, S. (2016). Assessment of the Human Factors Analysis and Classification System (HFACS): Intra-rater and inter-rater reliability. Safety science, 82, 393-398. Esmaeeli, N., Sattari, F., Lefsrud, L., & Macciotta, R. (2024). Assessing the risks associated with the Canadian Railway System using a safety risk model approach. Transportation research record, 2678(2), 795-808. Federal Railroad Administration (FRA) (2021) Rail Moving America Forward. Proc., TRB Annual Conference. FRA Office of Research, Development, and Technology. https://railroads.dot.gov/sites/fra.dot.gov/files/2020-12/2021_ RDT_CurrentProjects_complete_pdfa.pdf. Ferjencik, M. (2011). An integrated approach to the analysis of incident causes. Safety science, 49(6), 886-905. Figueres-Esteban, M., Hughes, P., El Rashidy, R. A. H., & Van Gulijk, C. (2018). Manifestation of ontologies in graph databases for big data risk analysis. In Safety and Reliability–Safe Societies in a Changing World (pp. 3189-3193). CRC Press. Gordon, R., Flin, R., & Mearns, K. (2005). Designing and evaluating a human factors investigation tool (HFIT) for accident analysis. Safety science, 43(3), 147-171. Gnoni, M. G., & Lettera, G. (2012). Near-miss management systems: A methodological comparison. Journal of Loss Prevention in the Process Industries, 25(3), 609-616. doi:10.1016/j.jlp.2012.01.005. Hadjimichael, M. (2009). A fuzzy expert system for aviation risk assessment. Expert systems with applications, 36(3), 6512-6519. Han, S. H., Byen, Y. S., Baek, J. H., An, T. K., Lee, S. G., & Park, H. J. (1999, September). An optimal automatic train operation (ATO) control using genetic algorithms (GA). In Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99.'Multimedia Technology for Asia-Pacific Information Infrastructure'(Cat. No. 99CH37030) (Vol. 1, pp. 360-362). IEEE. Hani Tabai, B., Bagheri, M., Sadeghi-Firoozabadi, V., Shahidi, V., & Mirasadi, H. (2018). Impact of train drivers’ cognitive responses on rail accidents. Transportation research record, 2672(10), 260-268. Heinrich, H. W. (1941). Industrial Accident Prevention. A Scientific Approach. Hickey, A. R., & Collins, M. D. (2017). Disinhibition and train driver performance. Safety science, 95, 104-115. Hollnagel, E. (1998). Cognitive reliability and error analysis method (CREAM). Elsevier. Huang, W., Kou, X., Zhang, Y., Mi, R., Yin, D., Xiao, W., & Liu, Z. (2021). Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach. Reliability Engineering & System Safety, 205, 107250. Huang, Y., Zhang, Z., Tao, Y., & Hu, H. (2022). Quantitative risk assessment of railway intrusions with text mining and fuzzy Rule-Based Bow-Tie model. Advanced Engineering Informatics, 54, 101726. Jay, S. M., Dawson, D., Ferguson, S. A., & Lamond, N. (2008). Driver fatigue during extended rail operations. Applied ergonomics, 39(5), 623-629. Jong, J. C., Lai, Y. C., Young, C. C., & Chen, Y. F. (2020). Application of fault tree analysis and swiss cheese model to the overspeed derailment of Puyuma Train in Yilan, Taiwan. Transportation research record, 2674(5), 33-46. Kaplan, S. (1990). On the inclusion of precursor and near miss events in quantitative risk assessments: A Bayesian point of view and a space shuttle example. Reliability Engineering & System Safety, 27(1), 103-115. doi:10.1016/0951-8320(90)90034- K Katsakiori, P., Sakellaropoulos, G., & Manatakis, E. (2009). Towards an evaluation of accident investigation methods in terms of their alignment with accident causation models. Safety science, 47(7), 1007-1015. Khakzad, N., Khan, F., & Amyotte, P. (2011). Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliability Engineering & System Safety, 96(8), 925-932. Khakzad, N., Khan, F., & Amyotte, P. (2013). Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Safety and Environmental Protection, 91(1-2), 46-53. Leitner, B. (2017). A General Model for Railway Systems Risk Assessment with the Use of Railway Accident Scenarios Analysis. Procedia Engineering, 187, 150-159. doi:10.1016/j.proeng.2017.04.361 Li, Y., Trinh, H., Haas, N., Otto, C., & Pankanti, S. (2013). Rail component detection, optimization, and assessment for automatic rail track inspection. IEEE Transactions on Intelligent Transportation Systems, 15(2), 760-770. Liang, C., Ghazel, M., & Cazier, O. (2018). Using Bayesian networks for the purpose of risk analysis at railway level crossings. IFAC-PapersOnLine, 51(9), 142-149. Lin, C. Y., Rapik Saat, M., & Barkan, C. P. (2020). Quantitative causal analysis of mainline passenger train accidents in the United States. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 234(8), 869-884. Lin, C. Y., Saat, M. R., & Barkan, C. P. (2016). Fault tree analysis of adjacent track accidents on shared-use rail corridors. Transportation Research Record, 2546(1), 129-136. Lin, C. Y., Saat, M. R., & Barkan, C. P. (2022). Semi-quantitative risk assessment of adjacent track accidents on shared-use rail corridors. Journal of Rail Transport Planning & Management, 24, 100355. Liu, P., Yang, L., Gao, Z., Li, S., & Gao, Y. (2015). Fault tree analysis combined with quantitative analysis for high-speed railway accidents. Safety science, 79, 344-357. Macciotta, R., Martin, C. D., Morgenstern, N. R., & Cruden, D. M. (2016). Quantitative risk assessment of slope hazards along a section of railway in the Canadian Cordillera—a methodology considering the uncertainty in the results. Landslides, 13, 115-127. McLeod, R. W., Walker, G. H., & Moray, N. (2005). Analysing and modelling train driver performance. Applied ergonomics, 36(6), 671-680. Molloy, G. J., & O'Boyle, C. A. (2005). The SHEL model: a useful tool for analyzing and teaching the contribution of Human Factors to medical error. Academic Medicine, 80(2), 152-155. Muttram, R. I. (2002). Railway safety's safety risk model. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 216(2), 71-79. Myrtek, M., Deutschmann-Janicke, E., Strohmaier, H., Zimmermann, W., Lawerenz, S., Brügner, G., & Müller, W. (1994). Physical, mental, emotional, and subjective workload components in train drivers. Ergonomics, 37(7), 1195-1203. Naweed, A. (2014). Investigations into the skills of modern and traditional train driving. Applied ergonomics, 45(3), 462-470. Naweed, A., Bowditch, L., Chapman, J., Dorrian, J., & Balfe, N. (2022). On good form? Analysis of rail Signal Passed at Danger pro formas and the extent to which they capture systems influences following incidents. Safety science, 151, 105726. Nivolianitou, Z. S., Leopoulos, V. N., & Konstantinidou, M. (2004). Comparison of techniques for accident scenario analysis in hazardous systems. Journal of Loss Prevention in the Process Industries, 17(6), 467-475. Qin, Y., Cao, Z., Sun, Y., Kou, L., Zhao, X., Wu, Y., ... & Jia, L. (2022). Research on active safety methodologies for intelligent railway systems. Engineering. Ramos, D., Ramirez-Hereza, P., Toledano, D. T., Gonzalez-Rodriguez, J., Ariza-Velazquez, A., Solis-Tovar, D., & Munoz-Reja, C. (2021). Dynamic Bayesian networks for temporal prediction of chemical radioisotope levels in nuclear power plant reactors. Chemometrics and Intelligent Laboratory Systems, 214, 104327. Rasmussen, J. (1987). The definition of human error and a taxonomy for technical system design. In New technology and human error (pp. 23-30). Wiley. Reinach, S., & Viale, A. (2006). Application of a human error framework to conduct train accident/incident investigations. Accident Analysis & Prevention, 38(2), 396-406. Sammarco, J. J. (2005). Operationalizing normal accident theory for safety-related computer systems. Safety Science, 43(9), 697-714. San Kim, D., Baek, D. H., & Yoon, W. C. (2010). Development and evaluation of a computer-aided system for analyzing human error in railway operations. Reliability Engineering & System Safety, 95(2), 87-98. Shorrock, S. T., & Kirwan, B. (2002). Development and application of a human error identification tool for air traffic control. Applied ergonomics, 33(4), 319-336. Simić, V., Soušek, R., & Jovčić, S. (2020). Picture fuzzy MCDM approach for risk assessment of railway infrastructure. Mathematics, 8(12), 2259. Stone, E. R., Yates, J. F., & Parker, A. M. (1994). Risk communication: Absolute versus relative expressions of low-probability risks. Organizational Behavior and Human Decision Processes, 60(3), 387-408. Svenson, O. (1991). The accident evolution and barrier function (AEB) model applied to incident analysis in the processing industries. Risk Analysis, 11(3), 499-507. Uğurlu, Ö., Yıldız, S., Loughney, S., Wang, J., Kuntchulia, S., & Sharabidze, I. (2020). Analyzing collision, grounding, and sinking accidents occurring in the Black Sea utilizing HFACS and Bayesian networks. Risk analysis, 40(12), 2610-2638. Van Gulijk, C., Hughes, P., Figueres-Esteban, M., El-Rashidy, R., & Bearfield, G. (2018). The case for IT transformation and big data for safety risk management on the GB railways. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(2), 151-163. Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671-682. Wiegmann, D. A., & Shappell, S. A. (2017). A human error approach to aviation accident analysis: The human factors analysis and classification system. Routledge. Zhao, Y., Stow, J., & Harrison, C. (2018). A method for classifying red signal approaches using train operational data. Safety science, 110, 67-74. 孫碩昱(2010)。鐵路司機員駕駛行為分析之研究(碩士論文)。國立成功大學，臺南市。取自 https://hdl.handle.net/11296/tv58nt [Sun, S.Y. (2010). Train Drivers’ Driving Behavior Investigation. (master thesis, National Cheng Kung University, Tainan City). Retrieved from https://hdl.handle.net/11296/tv58nt] 陳昱甫(2019)。鐵路高風險駕駛行為與路段辨識模組開發(碩士論文)。國立台灣大學，台北市。取自https://hdl.handle.net/11296/86b252 [CHEN, Y.F. (2019). Development of High-Risk Driving Behavior and Section Identification Modules for Railway System (master thesis, National Taiwan University, Taipei City). Retrieved from https://hdl.handle.net/11296/86b252] 國立臺灣大學軌道科技研究中心（2012）。捷運萬大線風險評估模式。 [National Taiwan University Railway Technology Research Center (NTURTRC). (2012). Risk Assessment Models of MRT Wanda Line.] | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94501 | - |
| dc.description.abstract | 在普悠瑪事故和太魯閣事故接連發生之後，鐵路行車安全的風險管理成為大眾關注的焦點。面對鐵道行車的不確定性，本研究提出了一個結合人因分析與分類系統和貝葉斯網路的六階段步驟，建構鐵道行車風險評估框架，全面檢視風險並提前辨識潛在風險因子。首先，透過事故分析識別和定義潛在風險事件，並進行因果分析，確定導致風險事件的原因。接著，基於過往行車數據構建貝葉斯網路，量化各風險因子之間的關係和影響，並利用貝葉斯推斷計算風險事件的發生機率，進行事故後果分析，評估每個風險事件的潛在影響。在案例分析中，以實際的路線和行車資訊進行路段分析，結果顯示瑞芳到雙溪的風險相對較高，這表明除了人為失誤外，平交道和施工區域造成的風險也必須重視。此結果也說明了本研究提出的框架，能根據每班列車的行車資訊，提前預測路段風險，若能在行車前通知司機員各路段風險差異，使其提前準備，達到事前預防的效果，更可提升鐵道行車的整體安全性 | zh_TW |
| dc.description.abstract | Following the consecutive occurrences of the Puyuma and Taroko accidents, public attention has been drawn to the importance of risk management in train operations. To address operational uncertainties, this research proposes a six-phase approach that integrates the Human Factors Analysis and Classification System with Bayesian networks to develop a risk assessment framework. Initially, potential risk events are identified through accident data, followed by causal analysis. A Bayesian network, constructed from historical data, quantifies relationships and impacts of various risk factors. Bayesian inference calculates the probabilities of risk events and assesses their potential impacts. A case study applying this framework to real routes reveals higher risks between Ruifang and Shuangxi, emphasizing the risk in level crossings and construction areas. The results also demonstrate that the framework proposed in this research can predict section risks in advance based on the train's operational information and all the latest details about the route it travels through. By informing drivers of the varying risks of different sections before their journeys, they can be better prepared, achieving proactive prevention. This approach can possibly enhances the overall safety of train operations. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T16:23:48Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T16:23:48Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii ABSTRACT iii TABLE OF CONTENT iv LIST OF FIGURES vii LIST OF TABLES ix CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Research Objectives 3 1.3 Contribution Summary 4 1.4 Thesis Organization 5 CHAPTER 2 LITERATURE REVIEW 7 2.1 Development and Applications of Railway Safety 7 2.1.1 Development of Railway Safety Prevention Technologies 7 2.1.2 Applications of Railway Safety Management 10 2.1.3 Practices of Risk Management in Aviation 11 2.2 Risk Management Methodology 12 2.2.1 Risk identification method 12 2.2.2 Risk evaluation method 15 2.3 Summary of Literature Review 18 CHAPTER 3 METHODOLOGY 21 3.1 The Framework of the Research 21 3.2 Application of HFACS in railway accident 23 3.3 Application of Bayesian Network 30 3.4 Development of Train Operation Risk Assessment System 36 Phase 1: Risk Event Definition 36 Phase 2: Accident Causal Analysis under HFACS 39 Phase 3: Bayesian Network Construction with Historical Data 47 Phase 4: Bayesian Inference 62 Phase 5: Consequence Analysis 63 Phase 6: Predominate Factors Search 66 CHAPTER 4 CASE STUDY 68 4.1 Risk Assessment 68 4.1.1 Analysis Network 68 4.1.2 Human Failure 72 4.1.3 External Intrusion 74 4.1.4 Train Operation Risk Assessment 76 4.2 BN Validation 80 4.3 Risk Control 83 CHAPTER 5 CONCLUSION AND FUTURE WORK 88 5.1 Conclusion 88 5.2 Future Work 90 REFERENCE 92 | - |
| dc.language.iso | en | - |
| dc.title | 以數據驅動貝葉斯網絡建立鐵道行車風險評估框架 | zh_TW |
| dc.title | Development of Train Operation Risk Assessment Framework Based on a Data-Driven Bayesian Network | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林陳佑;鍾志成;楊正君 | zh_TW |
| dc.contributor.oralexamcommittee | Chen-Yu Lin;Jyh-Cherng Jong;Cheng-Chung Young | en |
| dc.subject.keyword | 鐵路運輸,行車風險,風險評估,事故分析,風險因子,司機員行為,路段評估, | zh_TW |
| dc.subject.keyword | Rail Transportation,Train operation risk,Risk assessment,Accident analysis,Risk factors,Driving behavior,Section Assessment, | en |
| dc.relation.page | 100 | - |
| dc.identifier.doi | 10.6342/NTU202404075 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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