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研究生:劉昭堂
研究生(外文):Chao-TangLiu
論文名稱:捷運系統班距最佳化之研究:以高雄捷運系統為例
論文名稱(外文):Determination of Optimal Headways for Mass Rapid Transit Scheduling: A Case Study for the Kaohsiung Mass Rapid Transit System
指導教授:胡守任胡守任引用關係
指導教授(外文):Shou-Ren Hu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:交通管理學系碩博士班
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:71
中文關鍵詞:服務指標列車運行計畫班距高雄捷運
外文關鍵詞:Mass Rapid TransitTrain Service PlanHeadwayMathematical Programming
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運輸系統之經營,是需要考量營運成本與旅客服務品質間的均衡。而在大眾捷運系統當中,為維持民眾「行」的品質,政府在捷運系統服務指標法規,規定整體系統服務最基本的要求,以提供民眾基本運輸的權利。列車運行計畫,即是在政府服務指標標準的框架下,進而研訂。

本研究以高雄捷運為例,該系統自97年通車營運迄今,運量不如當初預期,目前平均日運量約為13.5萬人次,而現行使用之列車運行計畫,在尖峰時段提供4~6分鐘班距,離峰時段7~8分鐘班距,全日提供之運能約為45萬人次,由此得知,提供之運能供過於求,尤其在離峰時間,空車率相當地高。可見,目前所實施之時刻表,具有相當大的調整空間。

本研究之方向,希望藉由歷史運量資料的統計分析,觀察運量分佈趨勢,建置一個能依運量分佈,並以班距為導向,進而動態調整的時刻班表模組。規劃自時間及空間的兩種不同面向,觀察旅客的旅運行為,使用該模組,進行時刻表班距之調整,期能有效的降低整體營運成本。

The operation of a mass rapid transit (MRT) should consider the balance between total system cost and service level. In a MRT system, the main service is to provide people with the cost-affordable mobility, in the regulation of MRT service indicators, the government sets the minimum standards to ensure a certain level of MTR services. Thereby, how to schedule an optimal operating model is one of the important operational issues for a MRT system.

In the past research of MRT operations, mostly focused on issues of train delay, energy saving, route design or overall system operating regards, and placed less focus on the optimal headway problem. In the Kaohsiung Mass Rapid Transit (KMRT) system, the total ridership has not reached a predicted level, but the service in terms of the Train Service Plan (TSP, a plan concerning train operation under the government regulation) provided is more than needed. Therefore, a headway-oriented model for the KMRT is crucial to minimize total system cost while maintaining the certain level of train service.

In this research, we aim to establish a headway-oriented model which can be dynamically adjusting train schedule depending on the passenger spatio-temporal distribution data during daily operation. The developed model will be solved by both the operating cost and passenger waiting cost. Finally, numerical case study and sensitivity analysis will be conducted to demonstrate the feasibility and effectiveness of the development models and solution algorithms.

LIST OF TABLES III
LIST OF FIGURES IV
Chapter1 INTRODUCTION 1
1.1 Motivation 1
1.2 Objective 1
1.3 Research Scope 2
1.3.1 Purpose 2
1.3.2 Spatial Domain 2
1.3.3 Temporal Domain 6
1.3.4 Passenger Demand 6
1.4 Research Methodology 7
1.5 Research Content With Research Flow Chart 7

Chapter2 LITERATURE REVIEW 9
2.1 Background Information 9
2.2 Problem Statement 13
2.3 Review of the Relevant Literature 13
2.3.1 MRT System Performance 13
2.3.2 MRT Headway Design 14
2.3.3 Other Factors 15
2.4 Comments on the Reviewed Literature 16

Chapter3 MODEL CONSTRUCTION 18
3.1 Justification 18
3.2 Model Structure 20
3.3 Model Formulation 22
3.3.1 Basic Assumption 22
3.3.2 Objective Function and Model Constraints 22
3.4 Model Variant: Net Income 27

Chapter4 EMPIRICAL STUDY 29
4.1 O-D Data Analysis 29
4.2 Model Parameters Assumption 30
4.2.1 Assume the Headway of Each Hour for New Timetable 30
4.2.2 Passenger Waiting Time 34
4.2.3 The Relative Weight of Model 34
4.2.4 Parameters of Model 34
4.3 Case Scenarios 36
4.3.1 Scenario 1:Headway Setting is Regulated by Service Indicators 36
4.3.2 Scenario 2:Headway Setting is not Regulated by Service Indicators 36

Chapter5 MODEL RESULT 38
5.1 Scenario 1: Headway Setting is Regulated by Service Indicators 38
5.1.1 Minimal Cost (M1) and Maximal Net Income (M2) 38
5.1.2 Adopting the Extra Trains Model 41
5.1.3 Adopting the Shuttle Trains Model 44
5.2 Scenario 2: Headway Setting is not Regulated by Service Indicator 47
5.2.1 Minimal cost (M1) and Maximal Net Income (M2) 47
5.2.2 Adopting the Extra Trains Model 49
5.2.3 Adopting the Shuttle Trains Model 51
5.2.4 Adopting the Fixed Timetable Model 53
5.3 Summary of Research 56
5.3.1 Cost and Net Income Aspect 56
5.3.2 Headway Aspect 57

Chapter6 CONCLUSIONS AND FUTURE WORK 65
6.1 Conclusion 65
6.2 Future Work 67

Reference 69


1.Albrecht, A.R., Panton, D.M. and Lee, D.H. (2010), “ Rescheduling Rail Networks With Maintenance Disruption Using Problem Space Search, Computer & Operation Research, doi:10.1016/j.cor.2010.09.001.
2.Caprara, A., Fischetti, M. and Toth P. (2002), “Modeling and Solving the Train Timetabling Problem, Operation Research, Vol. 50, No. 5, pp. 851-861.
3.Chen, C.H. (2002), “Stochastic Optimization for Computing Bus Multiple Headways, Master Thesis, Chung Yuan Christian University.
4.Chang, E.F. (2002), “Simulation Analysis of on the Operations Adjustments of Urban Mass Rapid Transit, Master Thesis, National Cheng Kung University.
5.Chen, C.H. (2008), “Application of Object Oriented Modeling Technique to Simulating the Rescheduling Operation of a MRT System, Doctor of Philosophy Thesis, National Cheng Kung University.
6.Chou, Y.D. (2009), “Optimal Energy Saving in Mass Rapid Transit System Using Timetable Simulation System, Master Thesis, National Kaohsiung University of Applied Sciences.
7.Christian, L. (2008), “The First Optimized Railway Timetable in Practice, Transportation Science Vol. 42, No. 4, pp. 420-435.
8.Feng, C.M. and Chiou, Y.C. (2004), “Research Methods, Du-Jian Cultural Business Corporation.
9.Guihaire, V. and Hao, J.K. (2008), “Transit Network Design and Scheduling: A Global Review, Transportation Research Part A.
10.Jean-Francois, C., Paolo, T. and Daniele, V. (1998), “A Survey of Optimization Models for Train Routing and Scheduling , Transportation Science Vol. 32, No. 4, pp.380-404.
11.KRTC (2010), “Train Service Plan of Red and Orange Line, Kaohsiung, Taiwan.
12.Kim, K. and Chien, I.J. (2011), “Optimal Train Operation for Minimum Energy Consumption Considering Track Alignment, Speed Limit, and Schedule Adherence Journal of Transportation Engineering Vol.137, No.9, pp.665-674.
13.KRTC (2011), ( http://www.krtco.com.tw/en/service/service-1.aspx).
14.Liu, C.M. (1995), “Mathematical Programming-Theory and Practice, Hung Ming Library Corporation.
15.Lin, C.M. (2009), “Mass Transit Operational Route Design Using Genetic Algorithm, Doctor of Philosophy Thesis, National Chiao Tung University.
16.Lo, M.C. (2009), “Construction of the Simulation for Transit System's Operation Plans Under Random Demand-Taking Taipei Muh-Cha Line as an Example, Master Thesis, National Cheng Kung University.
17.Malachy, C. and Ivan, C. (2007), “Scheduling trains on a network of busy complex stations, Transportation Part B 41, pp.159-178.
18.Shie, S.S. (2003), “The Simulation Study of Train Controls in Incident Delay for Taipei Rapid Transit Systems, Doctor of Philosophy Thesis, National Cheng Kung University.
19.Shiu, Y.W. (2005), “The Strategy of the MRT Dispatch Depended on Passenger Demand, Master Thesis, National Taiwan University.
20.Wong, C.W., Yuen, W. Y., Fung, W. and Leung, M.Y. (2008), “Optimizing Timetable Synchronization for Rail Mass Transit, Transportation Science Vol.42, No.1, pp.57-69.
21.Xuesong, Z. and Ming, Z. (2007), “Single train timetable with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds, Transportation Part B 41, pp.320-341.

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