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Abstract: line planning is the first important strategic element in the railway operation planning process, which will directly affect the successive planning to determine the efficiency of the whole railway system. A two-layer optimization model is proposed within a simulation framework to deal with the high-speed railway (HSR) line planning problem. In the model, the top layer aims at achieving an optimal stop-schedule set with the service frequencies, and is formulated as a nonlinear program, solved by genetic algorithm. The objective of top layer is to minimize the total operation cost and unserved passenger volume. Given a specific stop-schedule, the bottom layer focuses on weighted passenger flow assignment, formulated as a mixed integer program with the objective of maximizing the served passenger volume and minimizing the total travel time for all passengers. The case study on Taiwan HSR shows that the proposed two-layer model is better than the existing techniques. In addition, this model is also illustrated with the Beijing-Shanghai HSR in China. The result shows that the two-layer optimization model can reduce computation complexity and that an optimal set of stop-schedules can always be generated with less calculation time.

[1]Baaj, M.H., Mahmassani, H., 1991. An AI-based approach for transit route system planning and design. Journal of Advance Transportation, 25(2):187-209.

[2]Borndǒrfer, R., Grǒtschel, M., Pfetsch, M.E., 2007. A Column-Generation Approach to Line Planning in Public Transport. Technical Report No. ZIB-Report 05-18, Konrad-Zuse-Zentrum für Informationstechnik, Berlin.

[3]Bussieck, M.R., 1998. Optimal Lines in Public Rail Transport. PhD Thesis, Technical University Braunschweig, Germany.

[4]Cepeda, M., Cominetti, R., Florian, M., 2006. A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria. Transportation Research Part B, 40(6):437-459.

[5]Chakroborty, P., Wivedi, T., 2002. Optimal route network design for transit systems using genetic algorithms. Engineering Optimization, 34(1):83-100.

[6]Chang, Y.H., Yeh, C.H., Shen, C.C., 2000. A multiobjective model for passenger train services planning: application to Taiwan’s high-speed rail line. Transportation Research Part B, 34(2):91-106.

[7]Deng, L.B., 2007. Study on the Optimal Problems of Passenger Train Plan for Dedicated Passenger Traffic Line. PhD Thesis, Central South University, China.

[8]Deng, L.B., Shi, F., Zhou, W.L., 2009. Stop schedule plan optimization for passenger train. China Railway Science, 30(4):102-106 (in Chinese).

[9]Eiben, A.E., Smith, J.E., 2003. Introduction to Evolutionary Computing. Springer-Verlag Berlin Heidelberg, New York, USA, p.57-104.

[10]Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, p.23-47.

[11]Goossens, J.W., van Hoesel, S., Kroon, L., 2004a. A branch-and-cut approach for solving railway line-planning problems. Transportation Science, 38(3):379-393.

[12]Goossens, J.W., van Hoesel, S., Kroon, L., 2004b. Optimising Halting Station of Passenger Railway Lines. Available from http://arno.unimaas.nl/show.cgi?fid=803 [Accessed on Sept. 1, 2011].

[13]Goossens, J.W., van Hoesel, S., Kroon, L., 2006. On solving multi-type railway line planning problems. European Journal of Operational Research, 168(2):403-424.

[14]Guan, J.F., Yang, H., Wirasinghe, S.C., 2006. Simultaneous optimization of transit line configuration and passenger line assignment. Transportation Research Part B, 40(10):885-902.

[15]Hamdouch, Y., Lawphongpanich, S., 2008. Schedule-based transit assignment model with travel strategies and capacity constraints. Transportation Research Part B, 42(7-8):663-684.

[16]Laporte, G., Mesa, J.A., Perea, F., 2010. A game theoretic framework for the robust railway transit network design problem. Transportation Research Part B, 44(4):447-459.

[17]Mo, W.T., Wang, L., Wang, B.H., Sun, W., Fei, X., Wang, F.J., Xu, J., Qin, Y., 2011. Two-Layer Optimization Based Timetable Rescheduling in Speed Restriction for High Speed Railway. Transportation Research Board 90th Annual Meeting, Report No. 11-2367, Washington DC, USA.

[18]Nielsen, O.A., 2000. A stochastic transit assignment model considering differences in passengers utility functions. Transportation Research Part B, 34(5):377-402.

[19]Pfetsch, M.E., Borndorfer, R., 2005. Routing in Line Planning for Public Transport. Technical Report No. ZIB-Report 05-36, Konrad-Zuse-Zentrum für Informationstechnik, Berlin.

[20]Poon, M.H., Wong, S.C., Tong, C.O., 2004. A dynamic schedule-based model for congested transit networks. Transportation Research Part B, 38(4):343-368.

[21]Schmǒcker, J.D., Bell, M.G.H., Kurauchi, F., 2008. A quasi-dynamic capacity constrained frequency-based transit assignment model. Transportation Research Part B, 42(10):925-945.

[22]Schmǒcker, J.D., Fonzone, A., Shimamoto, H., Kurauchi, F., Bell, M.G.H., 2011. Frequency-based transit assignment considering seat capacities. Transportation Research Part B, 45(2):392-408.

[23]Schǒebel, A., Schwarze, S., 2006. A Game-Theoretic Approach to Line Planning. Available from http://drops. dagstuhl.de/opus/volltexte/2006/688/pdf/06002.Schoebel Anita.Paper.688.pdf [Accessed on Sept. 1, 2011]

[24]Shi, F., Deng, L.B., Huo, L., 2007. Bi-level programming model and algorithm of passenger train operation plan. China Railway Science, 28(3):110-116 (in Chinese).

[25]Wang, H.Z., 2006. Study on the Train Scheme of Passenger Transport Special Line. MS Thesis, China Academy of Railway Sciences, Beijing, China.