博碩士論文 109385601 詳細資訊



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姓名 安蒂娜(Andina Mugi Utami)  查詢紙本館藏   畢業系所 土木系營建管理博士班
論文名稱 探索和排名 MRT 維護成本的影響因素 使用粗糙集增強的 K-近鄰方法
(EXPLORING AND RANKING INFLUENTIAL FACTORS IN MRT MAINTENANCE COSTS USING ROUGH SET ENHANCED K- NEAREST NEIGHBOR METHOD)
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摘要(中) 鑑於全球對大眾捷運系統（MRT）之依賴，優化後之維護規畫成為實現高效能運營成本之關鍵；然而，確定影響捷運維護成本等重要因素之複雜性，仍為持續存在之挑戰。本研究運用約略集增強K-近鄰演算法（KNN）調查6項捷運維護成本中之重要影響因素：對捷運之需求、維護團隊執行檢修任務所需之差旅（OV）需求、員工薪資、工地成本、捷運直接費率、以及用於載運維護人員之公務車輛直接費率。
本研究採用之研究方法包括文獻回顧、數據收集、以及用於確定最具影響力因素之約略集增強分析。研究結果顯示，捷運直接費率為核心因素，頻率為78.95%，共有820次獨特出現。其次為OV直接費率，頻率為76.48%，共出現736次。薪資和需求（MRT和OV）等其他因素之頻率約為60%（獨特出現325-349次），而工地成本之頻率為55.66%，共出現193次。本研究提供大眾捷運系統（MRT）之利害關係人一量化方法，使其得以更準確地估計維護成本。此種改良方法可提升決策過程之成效，尤其益於預算規劃及資源分配具有益處。
摘要(英) The global reliance on Mass Rapid Transit (MRT) systems necessitates optimized maintenance planning for efficient operational costs. The complexity of identifying significant factors affecting MRT maintenance costs has presented persistent challenges. This research uses the Rough Set Enhanced K-Nearest Neighbor (KNN) method to investigate the factors in MRT maintenance costs. The study targets six influential factors: demand MRT, demand OV, staff salary, site cost, MRT direct rate and official bus direct rate for transportation of the crew maintenance. The research methodology implemented in this study comprised a structured blend of literature review, data collection, and Rough Set Enhanced analysis used to determine the most influential factor. The MRT direct rate, with a frequency of 78.95% and 820 unique occurrences, is the core factor. The OV direct rate follows closely at 76.48% frequency and 736 occurrences. Other factors like Salary and Demand (for MRT and OV) show frequencies around 60% (unique occurrences 325-349), while site cost registers a frequency of 55.66% with 193 occurrences. The study provides a quantifiable method for MRT stakeholders to estimate maintenance costs more accurately. This improved approach enhances decision-making processes, which is particularly beneficial for budget planning and resource allocation.
關鍵字(中) ★ 大眾捷運系統、維護成本、約略集增強K-近鄰演算法、影響因素、營運成本
★ 大眾捷運系統
★ 維護成本
★ 約略集增強K-近鄰演算法
★ 影響因素
★ 營運成本		 關鍵字(英) ★ Mass Rapid Transit (MRT), Maintenance Costs, Rough Set Enhanced KNN Method, Influential Factors, Operational Costs.
★ Mass Rapid Transit (MRT)
★ Maintenance Costs
★ Rough Set Enhanced KNN Method
★ Influential Factors
★ Operational Costs
論文目次 ABSTRACT i
摘 要 ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENT v
LIST OF TABLES viii
LIST OF FIGURES ix
CHAPTER I INTRODUCTION 1
1.1 Background of the Study 1
1.2 Research Problem 10
1.3 Research Objectives 12
1.4 Research Scope and Limitation 12
1.5 Methodology Overview 14
1.6 Research Outline 16
CHAPTER II LITERATURE REVIEW 20
2.1 Overview of Mass Rapid Transit 21
2.1.1 Importance and Role of MRT in Urban Transportation 22
2.1.2 Key Components of MRT Systems 23
2.2 MRT Operational Cost 23
2.2.1 Capital Costs 24
2.2.2 Operation Costs 26
2.2.3 Maintenance Costs 27
2.3 MRT Maintenance System 28
2.3.1 Types of Maintenance in MRT Systems 29
2.3.2 Maintenance Procedures and Practices in MRT Systems 32
2.4 Breakdown of MRT Maintenance Cost 34
2.4.1. Overview of MRT Maintenance Costs 34
2.4.2. Detailed Breakdown of MRT Maintenance Costs 35
2.5 Factors Influencing MRT Maintenance Costs 37
2.5.1 Demand 38
2.5.2 Salary 39
2.5.3 MRT Direct Rate for Transportation of the Crew Maintenance 39
2.5.4 Official Bus Direct Rate for Transportation of the Crew Maintenance 40
2.5.5 Site Cost 41
2.6 Problems Regarding Maintenance Cost 43
2.5.2. Previous Work of Maintenance Cost Problems in MRT Systems 47
2.5.3. Previous Work of Optimizing MRT Maintenance Cost 48
2.7 Review of Proposed Method: Rough Set Enhanced KNN 50
2.7.1 Rough Set Theory 50
2.7.2 KNN Method 51
2.7.3 Rough Set Enhanced KNN Method 52
2.8 Summary and Research Gap 55
CHAPTER III DEVELOPING AND IMPLEMENTING A ROUGH SET ENHANCED KNN 59
3.1 Data Collection 59
3.2 Assumptions 62
3.3 Determining Influenced Factor 65
3.4 Proposed Rough Set Enhanced KNN Method 67
CHAPTER IV EXPLORING AND RANKING INFLUENTIAL FACTORS IN MRT MAINTENANCE COSTS 74
4.1 Data Selection and Pre-Processing 75
4.2 Identify the Influenced Factor 76
4.2.1 Core Factor 80
4.2.2 Medium Impact Factor 82
4.2.3 Insignificant Factor 84
4.3 Evaluating Method Performance 85
4.4 Total Maintenance Cost Calculation 88
4.5 Discussion 93
CHAPTER V CONCLUSION 99
5.1 Summary of Findings 99
5.2 Recommendations for Future Research 101
REFERENCE: 105
參考文獻 1. Yeh, H., Preventive interface management for complex capital projects a case study of mass rapid transit project construction. 2019: researchgate.net.
2. Diao, M., Y. Fan, and T. Sing, A new mass rapid transit (MRT) line construction and housing wealth: Evidence from the circle line. Journal of Infrastructure, Policy and …, 2017.
3. Kawamura, S., et al., An effective use of Tokyo metro passengers flow by visualization of smart card ticket ′PASMO′origin-destination data for public transport network to be …. Proc …. 2015: tkl.iis.u-tokyo.ac.jp.
4. M. Pour, S., et al., A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem. European Journal of Operational Research, 2018. 269(1): p. 341-352.
5. Liu, L. and R.C. Chen. A MRT Daily Passenger Flow Prediction Model with Different Combinations of Influential Factors. in 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). 2017.
6. Martinod, R., et al., Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions. Computers &Industrial …, 2018.
7. Jie, H., H. Zou, and Q. Xu. Forecasting Daily MRT Passenger Flow in Taipei Based on Google Search Queries. in 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC). 2021.
8. Lee, A., Subways as a space of cultural intimacy: The mass rapid transit system in Taipei, Taiwan. The China Journal, 2007(58): p. 31-55.
9. Chang, H.H. and T.Y. Lai, THE TAIPEI MRT (MASS RAPID TRANSIT) TOURISM ATTRACTION ANALYSIS FROM THE INBOUND TOURISTS′ PERSPECTIVES. Journal of Travel & Tourism Marketing, 2009. 26(5-6): p. 445-461.
10. Jih-Wen, S. and L. Wei-Song, Technical Considerations in Electrical and Mechanical System Design of Taipei Mass Rapid Transit System Extension Projects. 2007.
11. Lo, W., et al., Contractor selection process for Taipei mass rapid transit system. 1998. 14(3): p. 57-65.
12. Chen, Y.-S., et al., Two Advanced Models of the Function of MRT Public Transportation in Taipei. 2021. 10(9): p. 1048.
13. Schmaranzer, D., R. Braune, and K.F. Doerner, Multi-objective simulation optimization for complex urban mass rapid transit systems. Annals of Operations Research, 2021. 305(1): p. 449-486.
14. Li, H., et al., Preventive Maintenance Decision Model of Urban Transportation System Equipment Based on Multi-Control Units. IEEE Access, 2020. 8: p. 15851-15869.
15. Liu, Y., Y. Wu, and Z. Kalbarczyk. Smart Maintenance via Dynamic Fault Tree Analysis: A Case Study on Singapore MRT System. in 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). 2017.
16. Fouracre, P., C. Dunkerley, and G.J.T.R. Gardner, Mass rapid transit systems for cities in the developing world. 2003. 23(3): p. 299-310.
17. Zhu, Y. and M. Diao, The impacts of urban mass rapid transit lines on the density and mobility of high-income households: A case study of Singapore. Transport Policy, 2016. 51: p. 70-80.
18. Reddy, V., et al., Modelling and analysis of rail maintenance cost. International Journal of …. 2007: Elsevier.
19. Wong, K. and T. Ho, Coast control for mass rapid transit railways with searching methods. IEE Proceedings-Electric Power Applications, 2004.
20. Nurcahyo, R., et al., Mass Rapid Transit Operation and Maintenance Cost Calculation Model. Journal of Advanced Transportation, 2020. 2020: p. 7645142.
21. Aloe, M.D., et al., Applying cost–benefit analysis to the economic evaluation of a tram-train system: Evidence from Brescia (Italy). Research in …, 2023.
22. Singh, V., G. Subramanian, and ... Risk-Based Maintenance Analysis of Metro Train Doors and Proposed Blockchain Technology Approach for Railway Operation. … and Knowledge Economy …, 2023.
23. Chen, C.H., S. Yan, and M. Chen, Short-term manpower planning for MRT carriage maintenance under mixed deterministic and stochastic demands. Annals of Operations Research, 2010. 181(1): p. 67-88.
24. Su, Z., et al., Integrated condition-based track maintenance planning and crew scheduling of railway networks. Transportation Research Part C: Emerging Technologies, 2019. 105: p. 359-384.
25. Nurcahyo, R., et al., Mass Rapid Transit Operation and Maintenance Cost Calculation Model. Journal of Advanced …. 2020: hindawi.com.
26. Gattuso, D. and A. Restuccia, A tool for railway transport cost evaluation. Procedia-Social and Behavioral Sciences, 2014.
27. Li, G. and C. Toda, Discussions on the Local Rail Transit System in the Urbanization. Procedia - Social and Behavioral Sciences, 2014. 138: p. 193-198.
28. Liang, J., Z.J.I.J.o.B. Liu, and S. Science, Analysis of urban rail transit operation cost control. 2014. 5(5).
29. Shang, B. and X. Zhang, Study of Urban Rail Transit Operation Costs. Procedia - Social and Behavioral Sciences, 2013. 96: p. 565-573.
30. Wang, R., et al., A cost-benefit analysis of commuter train improvement in the Dhaka Metropolitan Area, Bangladesh. 2014. 138: p. 819-829.
31. Yuan, J., W. Cui, and L. Rong, Construction and Market Application Analysis of the Rail Transit Predictive Maintenance Management System. CICTP 2014, 2014: p. 1733-1741.
32. Shiau, T. and Q. Peng, Mode-based transport sustainability: a comparative study of Taipei and Kaohsiung Cities. Journal of Sustainable Development. 2012: Citeseer.
33. Durazo-Cardenas, I., et al., An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost. Transportation Research Part C: Emerging Technologies, 2018. 89: p. 234-253.
34. Odolinski, K. and J. Nilsson, Estimating the marginal maintenance cost of rail infrastructure usage in Sweden; does more data make a difference? Economics of transportation, 2017.
35. Cheng, Y. and H. Tsao, Rolling stock maintenance strategy selection, spares parts′ estimation, and replacements′ interval calculation. International Journal of Production Economics. 2010: Elsevier.
36. Sahin, B., et al., An approach for analysing transportation costs and a case study. European Journal of …, 2009.
37. Roess Roger, P., F. Huss Martin, and S. Kwicklis Claire, Operating and Maintenance Costs for Rail Rapid Transit. Transportation Engineering Journal of ASCE, 1977. 103(3): p. 421-439.
38. Yip, H., H. Fan, and Y. Chiang, Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models. Automation in Construction. 2014: Elsevier.
39. Budai, G., D. Huisman, and R. Dekker, Scheduling preventive railway maintenance activities. Journal of the Operational Research Society, 2006. 57(9): p. 1035-1044.
40. Peralta Cámara, D., et al., Multiobjective optimization for railway maintenance plans. 2018. 32(3).
41. Arenas, D., et al., Timetable rearrangement to cope with railway maintenance activities. Computers & Operations Research, 2018. 95: p. 123-138.
42. Li, H., X. Huang, and Q. Feng. Optimizing expressway maintenance planning by coupling ant algorithm and geography information system transportation in Hubei province, China. in 2011 IEEE International Geoscience and Remote Sensing Symposium. 2011.
43. Chuang, H., et al., Optimal expansion planning of traction substations for an electrified mass rapid transit system. … Conference on Power …, 2006.
44. Budai-Balke, G., R. Dekker, and U. Kaymak, Genetic and memetic algorithms for scheduling railway maintenance activities. 2009.
45. Yeh, W., Multistate network reliability evaluation under the maintenance cost constraint. International Journal of Production Economics. 2004: Elsevier.
46. Idris, M., et al., Cost of Rolling Stock Maintenance in Urban Railway Operation: Literature Review and Direction. Pertanika Journal of …. 2022: researchgate.net.
47. Kirkwood, L., et al., Uncertainty of Net Present Value Calculations and the Impact on Applying Integrated Maintenance Approaches to the UK Rail Industry. Procedia CIRP, 2015. 38: p. 245-249.
48. Idris, M., et al., Factor and Influential Variables Affecting Maintenance Cost of Urban Rail Rolling Stock. International …, 2023.
49. Sinaga, M., M.A. Musadieq, and Z. Arifin, The Effect Of Maintenance &Operation And Transit Oriented Development Toward Sustainability Mediated By Time Utility And Occupancy. International Journal of Economics …, 2022.
50. Shi, Y., et al., Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems. European Journal of Operational Research, 2021.
51. Prabhakaran, P. and S. Anandakumar, Maintenance Methodologies Embraced by O&M Department for Track Geometry at Kochi Metro Rail Limited, India: A Case Study. Smart Cities, 2022.
52. Kiefer, A., M. Schilde, and K.F. Doerner, Scheduling of maintenance work of a large-scale tramway network. European Journal of Operational Research, 2018. 270(3): p. 1158-1170.
53. Wu, L., et al., A System Dynamic Model for Coordinated Planning of Bus Rapid Transit and Transit Oriented Development. ICCTP 2011: Towards …, 2011.
54. Shang-Yao Yan, J.-H.C., Andina Mugi Utami, Ting-Yo Young, Hsi-Hsien Wei, Scheming Preventive Maintenance Assignments for MRT - Taoyuan Metro MRT System. Engineering Optimization, 2023.
55. Sreedharan, E., Rail-Based Urban Transport. Indian Journal of Public Administration, 2001.
56. Chuang, H. and W. Liao, Optimal expansion planning of MRT traction substations by using immune algorithm. … on Advanced Materials for Science and …, 2016.
57. Lidén, T. and M. Joborn, Dimensioning windows for railway infrastructure maintenance: Cost efficiency versus traffic impact. Journal of Rail Transport Planning &Management, 2016.
58. Nurcahyo, R., et al., Research Article Mass Rapid Transit Operation and Maintenance Cost Calculation Model. 2020: academia.edu.
59. Rahman, H., P. Miraj, and A. Andreas, Exploring public–private partnership scheme in operation and maintenance stage of railway project. Sustainability, 2019.
60. Kumari, M. and A. Banerjee, Evaluation of mass rapid transit system (MRTS): a case study of Delhi. Urban Health Risk and Resilience in Asian Cities, 2020.
61. Varabuntoonvit, V., K. Boonyarith, and ... Sustainable efficiency indicator for urban transportation: Case study of public bus and rapid transit systems in Bangkok. Environmental …, 2023.
62. Sittipong, W. and V. Varabuntoonvit, Life Cycle Sustainability and Eco-Efficiency for Mass Transportation in Bangkok: A Case Study for Bus and Rapid Transit. 2021: Kasetsart University.
63. Pawlak, Z.J.I.j.o.c. and i. sciences, Rough sets. 1982. 11: p. 341-356.
64. Inuiguchi, M. and T.J.E.j.o.o.r. Miyajima, Rough set based rule induction from two decision tables. 2007. 181(3): p. 1540-1553.
65. Fan, T.-F., D.-R. Liu, and G.-H.J.E.J.o.O.R. Tzeng, Rough set-based logics for multicriteria decision analysis. 2007. 182(1): p. 340-355.
66. Chen, J.-H., J.-Z. Lin, and S.-C.J.J.o.M.i.E. Hsu, Determining and classifying factors of employees’ expatriation willingness using rough set theory. 2014. 30(5): p. 04014021.
67. Chen, J.-H., et al., Automatic manpower allocation for public construction projects using a rough set enhanced neural network. 2021. 48(8): p. 1020-1025.
68. Bazan, J. and M. Szczuka, The rough set exploration system. Transactions on Rough Sets III, 2005.
69. Suguna, N. and K. Thanushkodi, An improved k-nearest neighbor classification using genetic algorithm. International Journal of Computer …. 2010: researchgate.net.
70. Xueli, S. and Q. Xinyu, KNN Algorithm of Enhanced Clustering Based on Density Canopy and Deep Feature. Journal of Frontiers of Computer Science & …, 2021.
71. Wang, Z., S. Ji, and B. Yu, Short-term traffic volume forecasting with asymmetric loss based on enhanced KNN method. Mathematical Problems in Engineering. 2019: hindawi.com.
72. Kong, S., et al., KNN-enhanced Deep Learning Against Noisy Labels. arXiv preprint arXiv:2012.04224, 2020.
73. Chen, J.-H., C.-L. Chen, and H.-H. Wei, Manpower Allocation of Work Activities for Producing Precast Components: Empirical Study in Taiwan. Journal of Sustainability, 2023. 15(9): p. 7436.
74. Li, S., et al., Online streaming feature selection based on neighborhood rough set. Applied Soft Computing, 2021. 113: p. 108025.
75. Chen, J.-H. and S.-C.J.J.o.M.i.E. Hsu, Quantifying impact factors of corporate financing: engineering consulting firms. 2008. 24(2): p. 96-104.
76. Shahraki, N., et al., Optimal locations of electric public charging stations using real world vehicle travel patterns. Transportation Research Part D: Transport and Environment, 2015. 41: p. 165-176.
77. Ahabchane, C., A. Langevin, and M. Trépanier, Robust optimization for the hierarchical mixed capacitated general routing problem applied to winter road maintenance. Computers & Industrial Engineering, 2021. 158: p. 107396.
78. Zhang, C., et al., Integrated optimization of train scheduling and maintenance planning on high-speed railway corridors. Omega, 2019. 87: p. 86-104.
79. Peng, S., C. Huang, and H. Liu, A Novel CSAHP Approach to Assess the Priority of Maintenance Work Outsourced by a Metro Company. Processes, 2022.
80. Liu, B., et al., A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation. IEEE Access, 2019. 7: p. 94931-94943.
指導教授 陳介豪 顏上堯 蘇木春(Jieh-Haur Chen Yan-Shang Yao Mu-Chun Su) 審核日期 2023-8-8
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