Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8354
標題: 高運量捷運列車行駛策略之線上最佳化
Online Optimization of Train Driving Strategy in Mass Rapid Transit Systems
作者: 楊致傑
Yang, Chih-Chieh
關鍵字: Mass Rapid Transit System;高運量捷運系統;Moving Block Signaling System;Online optimization;Max-Min Ant System;移動式閉塞區間號誌系統;線上最佳化;最大最小螞蟻系統
出版社: 電機工程學系所
引用: [1] Hill, R. J. and L. J. Bond, “Modeling Moving-Block Railway Signaling Systems Using Discrete-Event Simulation,” Proceedings of IEEE/ASME JRC Conference, 4-6, Baltimore, MD, USA, pp. 105-111, Apr. 1995. [2] B.R. Ke and N. Chen, “Signalling Block-Layout and Strategy of Train Operation for Saving Energy in Mass Rapid Transit Systems,” IEE Proceedings- Electric Power Applications, vol. 152, no. 2, pp. 129-140, April 2005. [3] B.R. Ke, “Signaling System for Saving Energy on Mass Rapid Transit Systems,” Ph.D. Dissertation, National Taiwan University of Science and Technology, Taiwan, ROC., 2006. [4] Dorigo, M. and Stuzle, T., “Ant Colony Optimization,” MIT press, Massachusetts, 2004. [5] 陳孟傑,「使用最大-最小螞蟻系統於高運量捷運省能之閉塞區間設計」,國立中興大學電機工程學系研究所碩士論文,民國九十六年七月。 [6] Rose, J.; Klebsch, W.; Wolf, J.; “Temperature measurement and equilibrium dynamics of simulated annealing placements,” IEEE Transactions on Computer-Aided Design. , vol. 9, no. 3, pp. 253-259, Mar 1990. [7] Finni, J., A., Manunza, A., Marchesi, M., Pilo, F., “Tabu Search metaheuristics for global optimization of electromagnetic problems,” IEEE Transactions on Magnetic, vol.34, no 5, pp. 2960-2963, Sept 1998. [8] Eiben, A.E., Hinterding, R., Michalewicz, Z., “Parameter control in evolutionary algorithms, “IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp.124-141, Jul 1999. [9] Bullnheimer, B.,Htral, R.F. and Strauss, C., ‘Applying the ant system to the vehicle routing problem,’ Department of Management Science, University of Vienna, 1997. [10] Gambardella, L.M. and Dorigo, M., “An ant colony system Hybridized with a new local search for the sequential ordering problem,” Informs Journal on Computing, vol. 12, no. 3, pp. 237-255, 2000. [11] Sim, K. M. and Sun., W. H., “Ant colony optimization for routing and load-balancing: survey and new directions,” IEEE Transactions on System, Man and Cybernetics, Part A, vol. 33, no. 5, pp. 560-572, Sept 2003. [12] Dorigo, M. and L.M. Gambardella, “Ant Colony System: A Cooperative Leaning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, April 1997. [13] D. Merkle, M. Middendorf and H. Schmeck, “Ant Colony Optimization for Resource-Constrained Project Scheduling,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 333-346, August 2002. [14] Groselj, B. and Maluhi, Q.M., “Combinational optimization of distributed queries,” IEEE Transactions on Knowledge and Data Engineering, vol. 7, no.6, pp. 915-927, December 1995. [15] Dorigo, M., V. Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, February 1996. [16] Dorigo, M., Stutzle, T. and Birattari, M., “Ant Colony Optimization” IEEE Computational Intelligence Magazine, vol. 1, no. 4 pp. 28-39, Nov 2006. [17] Bauer, A. Bullnheimer, B. Hartl, R.F. and Strauss, C., “An ant colony optimization approach for the single machine total tardiness problem” IEEE Proceedings of Evolutionary Computation, vol. 2, pp. 1445-1450, Jul 1999. [18] Stuzle, T and H. Hoos, “Max-Min Ant System and Local Search for the Traveling Salesman Problem,” Proceedings of IEEE International Conference on Evolutionary Computation, pp. 309-314, 1997. [19] Stuzle, T. and Hoos, Holger H., “MAX-MIN Ant System”, Future Generation Computer Systems Journal, vol.16, no. 8, pp. 889-914, 2000. [20] 翁聿復,「高運量捷運系統電聯車號誌設備功能與操作概論」,捷運技術,第十三期,民國八十四年三月二十三日。 [21] M. J. Lockyear, “Changing track moving-block railway signaling,” IEEE Docklands Light Railway, FANUARY, pp.21-25, Jun 1996. [22] H. M. Glickenstein, T. K. Dyer and G. A. Kelly, “Amtrak’s Northeast Corridor gets proposal for high speed signal system to increasing capacity”, IEEE Vehicular Technology Society News. vol. 41, no. 2, pp.36-42, May 1994. [24] Uher, R. A., Program Manual for the railway System Train Operations Model (TOM Version 2.0), USA, 1997. [25] Davis, W. J., “The Tractive Resistance of Electric Locomotive and Cars,” General Electric Review, pp. 685-707, Oct 1926. [26] 曾乙申,「台北捷運工程淡水¬-新店線供電系統之負載潮流與諧波分析」,台灣工業技術學院電機研究所博士論文,民國八十四年七月。 [27] 陳智淵,「台北捷運系統列車運行動態模擬」,台灣科技大學碩士論文,民國九十一年五月二十三日。 [28] 許臨國,「列車行駛之阻力探討」,捷運技術,第五期,民國八十年八月二十三日。 [29] D.C. Gill and C.J. Goodman, “Computer-based Optimisation Techniques for Mass Transit Railway Signalling Design,” IEE Proceedings-B, vol. 139, no. 3, pp. 261-275, May 1992. [30] B.R. Ke and N. Chen, “Strategy of train Operation under Maximum Train Capacity in Mass Rapid Transit System,” ASME Proceeding of JRC, Pueblo, Colorado, pp. 16-18, Mar 2005. [31] 賴啟文,「高運量捷運列車省能之最佳速度軌跡與控制」,國立中興大學電機工程學系研究所碩士論文,民國九十六年七月。 [32] Mandal, S., Saha, D., and Mahanti, A. “A Heuristic Search for Generalized Cellular Network Planning,” Proceedings of 2002 IEEE International Conference on Personal Wireless Communications, pp. 105-109, 2002. [33 ] Beerliova, Z., Eberhard, F., Erlebach, T., Hall, A., Hoffmann, M., Mihal''ak, M., and Ram, L. S., “Network Discovery and Verification” IEEE Journal on Selected Areas in Communications, vol. 24, no. 12, pp. 2168-2181, February 2006. [34] Stutzle, T., and Dorigo, M., “A Short Convergence Proof for a Class of Ant Colony Optimization Algorithms,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 358-365, August 2002. [35] Wong, K. K. and Ho, T. K., “Coast control for mass rapid transit railways with searching methods,” IEE Proceedings-Electric Power Applications, vol. 151, no. 3, pp. 365-376, May 2004.
摘要: 
本論文提出一個基於移動式閉塞區間號誌系統,以站間省能為目的,設計高運量捷運列車行駛策略的最佳化方法。研究中利用組合最佳化技巧解決線上最佳化的問題。為了滿足列車運行策略的最佳化問題,利用最大最小螞蟻系統決定各區段列車的運行模式與速度碼,其中涵蓋加速、等速與滑行模式,並同時滿足相關限制條件。
研究中使用MATLAB撰寫高運量捷運系統線上最佳化的分析程式。模擬結果呈現在不同線形速限下,實際列車速度、平均坡度、加速度、計算時間、功率和能量的消耗。由案例顯示,計算時間與消耗能量比較其他研究方法的結果有大幅的改善。由於計算時間小於40秒,本研究所提出的方法足以達到線上最佳化的要求。最後,本研究的結果期待作為評估與設計高運量捷運列車省能行駛策略的線上最佳化參考。

This thesis presents a method to optimize the train driving strategy in mass rapid transit systems (MRTS) based on the moving-block signaling system for saving energy between successive stations. The combinational optimization techniques are used to solve the online optimization problem. In order to fulfill the optimal train driving strategy, train operation modes, including acceleration, constant-speed and coasting modes, and speed codes of sections are determined by using the MMAS. Simultaneously, several constraint conditions, for example, track speed limit and train average speed, are considered in this problem.
The MATLAB-based software is used to design the online optimization program of MRTS in this thesis. The simulation results include different speed limits, practical train speed, practical and equivalent gradient, acceleration, consumed time and power and energy consumption. The results of case studies show the performances of computation time and energy consumption compared with other researches are substantially improved. Because the computation time is shortened to less than forty seconds, the online optimization can be addressed. Finally, this research is expected to provide a useful reference for designing the online optimization of train driving strategy for saving energy in MRTSs.
URI: http://hdl.handle.net/11455/8354
其他識別: U0005-2508200815153200
Appears in Collections:電機工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.