A Method for Optimizing the Phased Development of Rail Transit Lines

Abstract

This paper develops a method for optimizing the construction phases for rail transit line extension projects with the objective of maximizing the net present worth and examines the economic feasibility of such extension projects under various financial constraints (i.e., unconstrained, revenue-constrained, and budget-constrained cases). A Simulated Annealing algorithm is used for solving this problem. Rail transit projects may be divided into several phases due to budget limits or demand growth that justifies different sections at different times. A mathematical model is developed to optimize these phases for a simple, one-route rail transit system, running from a Central Business District (CBD) to a suburban area. Some interesting results indicate that the economic feasibility of links with low demand is affected by the completion time of those links and their demand growth rate after their implementation. Sensitivity analysis explores the effects of interest rates on optimized results (i.e., construction phases and objective value). With further development, such a method should be useful to transportation planners and decision-makers in optimizing construction phases for rail transit line extension projects.

Keywords

Rail transit / Phased development / Optimization / Simulated annealing / Net present worth

References

[1] Chien S, Schonfeld P. Joint optimization of a rail transit line and its feeder bus system. J Adv Transp, 1998, 32(3): 253-284

[2] DiJoseph P, Chien S. Optimizing sustainable feeder bus operation considering realistic networks and heterogeneous demand. J Adv Transp, 2013, 47(5): 483-497

[3] Fan W, Machemehl RB. Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J Transp Eng, 2006, 132(1): 40-51

[4] Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H. A review of urban transportation network design problems. Eur J Oper Res, 2013, 229(2): 281-302

[5] Guan JF, Yang H, Wirasinghe S. Simultaneous optimization of transit line configuration and passenger line assignment. Transp Res Part B, 2006, 40(10): 885-902

[6] Guihaire V, Hao JK. Transit network design and scheduling: a global review. Transp Res Part A, 2008, 42(10): 1251-1273.

[7] Kim M, Schonfeld P, Kim E. Comparison of vertical alignments for rail transit. J Transp Eng ASCE, 2013, 139(2): 230-238

[8] Kim M, Schonfeld P. Integration of conventional and flexible bus services with timed transfers. Transp Res Part B, 2014, 68B–2: 76-97

[9] Kolisch R, Padman R. An integrated survey of deterministic project scheduling. OMEGA Int J Manag Sci, 2001, 29: 249-272

[10] Lai X, Schonfeld P. Optimization of rail transit alignments considering vehicle dynamics. Transp Res Record, 2012, 2275: 77-87

[11] Li ZC, Lam WHK, Wong SC, Sumalee A. Design of a rail transit line for profit maximization in a linear transportation corridor. Transp Res Part E, 2012, 48(1): 50-70

[12] Lo HK, Szeto WY. Time-dependent transport network design under cost-recovery. Transp Res Part B, 2009, 43(1): 142-158

[13] Markovic N, Milinkovic S, Tikhonov KS, Schonfeld P. Analyzing passenger train arrival delays with support vector regression. Transp Res Part C, 2015, 58: 251-262

[14] Matisziw TC, Murray AT, Kim C. Strategic route extension in transit networks. Eur J Oper Res, 2006, 171: 661-673

[15] Metropolis NA, Rosenbluth A, Rosenbluth M, Teller E. Equation of state calculations by fast computing machines. J Chem Phys, 1953, 21: 1087-1092

[16] Shayanfar E, Abianeh AS, Schonfeld P, Zhang L (2015) Prioritizing interrelated road projects using meta-heuristics. J Infrastruct Syst ASCE

[17] Spasovic LN, Schonfeld P. Method for optimizing transit service coverage. Transp Res Rec, 1993, 1402: 28-39.

[18] Sun Y, Schonfeld P. Stochastic capacity expansion models for airport facilities. Transp Res Part B, 2015, 80: 1-18

[19] Szeto WY, Jaber X, O’Mahony M. Time-dependent discrete network design frameworks considering land use. Computer-Aided Civil Infrastruct Eng, 2010, 25(6): 411-426

[20] Tsai FM, Chien S, Wei CH. Joint optimization of temporal headway and differential fare for transit systems considering heterogeneous demand elasticity. J Transp Eng ASCE, 2013, 139(1): 30-39

[21] Valadares Tavares L. Optimal resource profiles for program scheduling. Eur J Oper Res, 1987, 29: 83-90

[22] Vuchic V. Urban transit: operations, planning and economics, 2005, Hoboken, NJ: John Wiley & Sons Inc

[23] Wang S, Schonfeld P (2007) Demand elasticity and benefit measurement in a waterway simulation model. TRB 86th annual meeting compendium of papers

[24] Zhou Y, Kim HS, Schonfeld P, Kim E. Subsidies and welfare maximization tradeoffs in bus transit systems. Ann Reg Sci, 2008, 42–3: 643-660