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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 賴勇成 | |
dc.contributor.author | Kuan-Ting Chen | en |
dc.contributor.author | 陳冠廷 | zh_TW |
dc.date.accessioned | 2021-05-17T09:17:18Z | - |
dc.date.available | 2015-08-01 | |
dc.date.available | 2021-05-17T09:17:18Z | - |
dc.date.copyright | 2012-08-01 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-07-26 | |
dc.identifier.citation | 1. 鍾志成與張仕龍(2012),「鐵道服務水準與營運模式」,發表於臺灣整體鐵道網規劃願景研討會。
2. 李宗晏(2010),「臺鐵時刻表穩定度與效率評估」,台灣大學工學院土木工程學系交通工程組碩士論文。 3. Goverde, R. M. P. (2007), “Railway timetable stability analysis using max-plus system theory”, Transportation Research Part B, Vol. 41, pp. 179-201. 4. Goverde, R. M. P., Heidergott, B., and Merlet G. (2009), “Railway timetable stability analysis using stochastic max-plus linear systems”, Proceedings of 3rd ISROR, pp. 19. Zurich: ISROR. 5. Salido, M. A., Barber, F., and Ingolotti, L. (2008), “Robustness in railway transportation scheduling”, Proceedings of the 7th World Congress on Intelligent Control and Automation, June 25-27, 2008, Chongqing, China. 6. Delorme, X., Gandibleux, X., and Rodriguez, J. (2009), “Stability evaluation of a railway timetable at station level”, European Journal of Operational Research, Vol. 195, pp. 780-790. 7. D’Angelo, G., Di Stefano, G., Navarra, A., and Pinotti, C. M. (2009), “Recoverable robust timetables on trees”, Lecture Notes in Computer Science, v 5573 LNCS, 451-462. 8. Cicerone, S., D’Angelo, G., Di Stefano, G., Frigioni, D., and Navarra, A. (2009), “Recoverable robust timetabling for single delay: Complexity and polynomial algorithms for special cases”, Journal of Combinatorial Optimization, Vol. 18, pp. 229-257. 9. Carey, M., and Carville, S. (2000), “Testing schedule performance and reliability for train stations”, Journal of the Operation Research Society, vol. 51, pp. 666-682. 10. Middelkoop, D., Bouwman, M. (2002), “Testing the stability of the rail network”, Computers in Railways VIII, pp. 995-1002. 11. Demitz, J., Hubschen, C., Albrecht, C. (2004), “Timetable stability – Using simulation to ensure quality in a regular interval timetable”, Advances in Transport, vol. 15, pp. 549-562. 12. Vromans, M. J., Dkker, R., Kroon, L. G. (2006), “Reliability and heterogeneity of railway services”, European Journal of Operational Research, Vol. 172, pp. 647-665. 13. 劉昭榮(2011),「鐵路列車連鎖延滯之模擬模式構建與應用」,交通大學交通運輸研究所博士論文。 14. 交通部運輸研究所(2008),「運輸系統容量分析暨應用研究 – 軌道系統(2/4)」,交通部運輸研究所,研究報告。 15. Lai, Y. C., Shih, M. C., and Jong, J. C. (2010), “Railway capacity model and decision support process for strategic capacity planning”, Transportation Research Record: Journal of the Transportation Research Board, No. 2197, Transportation Research Board of the National Academies, Washington, D. C., pp. 19-28. 16. Goverde, R. M. P. (2005), “Punctuality of railway operations and timetable stability analysis”, PhD Dissertation, Transport & Planning Department, Delft University of Technology. 17. Hermann, T. M. (2006), “Stability of timetables and train routings through station regions”, PhD Dissertation, Institute of Operation Research, ETH Zurich. 18. Stok, R. (2008), “Estimation of railway capacity consumption using stochastic differential equations”, PhD Dissertation, University of Trieste. 19. Hansen, I. A. (2010), “Railway network timetabling and dynamic traffic management”, International Journal of Civil Engineering, Vol. 8, No. 1, pp. 19-32. 20. Purdy, G. (2010), “ISO 31000:2009 – Setting a New Standard for Risk Management”, Risk Analysis, Vol. 30, No. 6, pp. 881-886. 21. 劉牧阡(2010),「臺鐵行車營運風險分析系統之研究」,台灣大學工學院土木工程學系交通工程組碩士論文。 22. James, F. (1980), “Monte carlo theory and practice”, Rep. Prog. Phys., Vol. 43, pp. 1145-1189. 23. 洪華生、鄧漢中(1988),「工程或然率:決策、風險與可靠度第二冊」,台北市:科技圖書公司。 24. Ross, S. M. (2009), “Introduction to Probability Models (10th ed., pp. 312-319) ”, Academic Press. 25. 交通部臺灣鐵路管理局(2001),「行車事故調查報告及救援須知」,交通部臺灣鐵路管理局運轉規章(下)。 26. RSSB (2004), “A statistical review of the RSSB safety risk model (WP1)”, Rail Safety and Standards Board, U.K. 27. Yuan, J., Goverde, R. M. P., Hansen, I. A. (2006), “Evaluating stochastic train process time distribution models on the basis of empirical detection data”, Computers in Railways X, pp. 631-640. 28. Coleman, R. (2002), “Modelling Extremes”, International Statistical Workshop, Seoul University. 29. Lin, X. G. (2003), “Statistical Modelling of Severe Wind Gust”, International Congress on Modelling and Simulation, Townsville, Vol. 2, pp. 620-625. 30. Kellezi, E., and Gilli, M. (2000), “Extreme value theory for tail-related risk measures”, Working paper, Department of Econometrics and FAME, University of Geneva. 31. 交通部運輸研究所(2009),「運輸系統容量分析暨應用研究 – 軌道系統(3/4)」,交通部運輸研究所,研究報告。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6744 | - |
dc.description.abstract | 時刻表是軌道運輸服務最高的營運準則,而如何衡量一個的時刻表績效是營運單位的重要課題,對於軌道運輸所提供的服務來說,績效的衡量可從營運者、旅客亦或是車輛觀點,本研究自營運單位的角度以時刻表的效率與穩定程度來定義時刻表之績效,高使用效率代表有效的利用軌道資源,而穩定的時刻表在系統發生意外時能迅速的回復至正常狀態,然而,密集的班次有時反而造成可靠度的下降,因此效率與穩定度之間存在一權衡關係。本研究結合可靠度分析、容量分析及風險分析的方法概念,建立一套時刻表績效評估系統,可協助營運單位比較不同時刻表之間的績效差異,亦可辨識系統中不穩定的時段與路段。
過去的研究常以延滯時間評估時刻表的穩定度,但這種方法可能會有低估或高估穩定度的可能,因此,李宗晏(2010)自容量的角度提出「回復時間」的概念,即系統發生意外狀況後排除受影響的列車所需的時間,其研究以時刻表在一天營運下的「期望回復時間」作為指標;但由於行車事故的隨機特性,營運時回復時間未必會是平均值,本研究進一步分析回復時間的不確定性,以建立完整的時刻表評估系統,提出四個評估指標:使用效率、期望回復時間、回復時間標準差以及失效機率,研究中承襲回復時間的概念,除修正其模式外,亦建立蒙地卡羅模擬模式,提出新的評估指標衡量回復時間的不確定性,期望以此系統提供營運業者在進行時刻表建構設計時更彈性的資訊作為參考。 在案例分析中,本研究以實際臺鐵北部時刻表透過四個評估指標進行分析,發現穩定度的瓶頸時空點發生在晨峰與昏峰時段及汐止至七堵區間,若進一步比較改點前後,結果顯示上下行的時刻表在改點後都提高了使用率,而改點後上行時刻表的穩定度指標都顯示穩定度下降的情況,但在改點後下行時刻表只有期望回復時間增加,其標準差及失效機率卻下降,顯示回復時間的出現較為集中,改點前在一個標準差的範圍下反而會出現較高的回復時間,而失效機率則直接說明高回復時間的出現機率較低,因此相較於上行時刻表,改點後下行時刻表的穩定度不必然較差。透過這些指標的分析,營運業者可在改點決策上有更精確的資訊,並透過持續的修正與績效分析協助提升時刻表之績效,以確保營運單位提供的服務能滿足運輸需求持續的成長且維持優良的服務品質。 | zh_TW |
dc.description.abstract | Reliable railway operation is a result of a well-designed timetable. A robust and stable timetable should incorporate an appropriate level of slacks in order to recover the system from the unexpected disruption to the normal state. However, due to the high cost of railway infrastructure, a surplus slack can incur an unexpected expense and waste. Consequently, the evaluation of timetable stability and efficiency is important, since there is a trade-off between the railway capacity, capacity utilization and stability.
Most of the previous studies evaluated timetable stability with delay index, while Li (2010) considered this index may either over or underestimate the stability and thus proposed to use recovery time from the aspect of railway capacity. Recovery time is the amount of time to clear out the disrupted scheduled trains and return to the normal state. Li calculated the expected recovery time of timetable as the stability index. However, due to the uncertainty of disturbance, the inherently randomness of recovery time should be further studied in order to provide a flexible evaluation result. In this research, a timetable performance evaluation system is developed with four indices, including efficiency, expected recovery time, standard deviation of recovery time and failure probability. And the Monte Carlo simulation accounted for the uncertainty of recovery time is also developed. A case study of Taiwan Railway Administration (TRA) before and after the timetable revision on September 28th, 2012 was applied. The evaluation results showed that the bottlenecks of the stability are on peak periods and Xizhi to Qidu section. The analysis also showed that after the revision, the efficiency of capacity utilization increased. This led to the decrease of stability of northbound timetable, but not all the stability indices of southbound timetable indicated a worse result. With the evaluation of these four indices, accurate information can be provided to the railway agency in the timetable planning process so as to provide reliable and robust services to their customers, and return on shareholders’ investment. | en |
dc.description.provenance | Made available in DSpace on 2021-05-17T09:17:18Z (GMT). No. of bitstreams: 1 ntu-101-R99521514-1.pdf: 2397851 bytes, checksum: 6296ede350b13ba1d01fa9d97a4e7393 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III Abstract V 圖目錄 IX 表目錄 XI 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 4 1.3 研究範圍與限制 4 1.4 研究方法 5 1.5 研究流程與架構 5 第二章 文獻回顧 7 2.1 時刻表穩定度分析 7 2.1.1 解析模式 7 2.1.2 模擬模式 9 2.2 軌道容量分析 10 2.3 時刻表、容量與營運可靠度之關係 15 2.4 小結 17 第三章 時刻表績效評估系統架構 19 3.1 評估指標意義 19 3.2 指標一:時刻表使用效率 21 3.3 指標二:期望回復時間(容量使用之風險) 22 3.3.1 期望回復時間模式架構 24 3.3.2 事故發生機率分析 25 3.3.3 嚴重度分析 29 3.3.4 期望回復時間運算模式 34 3.4 指標三:回復時間標準差 35 3.4.1 模擬模式架構 37 3.4.2 模擬模式假設 38 3.4.3 模擬機制與分析流程 39 3.4.4 回復時間標準差運算 47 3.5 指標四:時刻表失效機率 47 3.6 時刻表績效評估流程與系統建立 48 第四章 案例分析 53 4.1 資料說明與整理 53 4.1.1 容量與事故容量分析結果 53 4.1.2 事故發生機率分佈試合結果 59 4.2 指標一:使用效率分析 64 4.3 指標二:期望回復時間分析 65 4.4 指標三:回復時間標準差分析 69 4.4.1 模擬次數決定 69 4.4.2 模擬結果 70 4.5 指標四:失效機率分析 78 4.6 時刻表改點前後指標綜合討論 86 4.7 小結 90 第五章 結論與建議 93 5.1 結論 93 5.2 建議 94 參考文獻 97 | |
dc.language.iso | zh-TW | |
dc.title | 軌道運輸系統時刻表績效評估系統之研發與建立 | zh_TW |
dc.title | Development of the Timetable Performance Evaluation System for Rail Transportation | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 范植谷,李治綱,鍾志成 | |
dc.subject.keyword | 時刻表穩定度,軌道容量,回復時間,蒙地卡羅模擬, | zh_TW |
dc.subject.keyword | Timetable stability,Railway capacity,Recovery time,Monte Carlo simulation, | en |
dc.relation.page | 100 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2012-07-26 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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