Abstract
Transit network design is a very important problem. In particular, it has a great influence on passenger satisfaction with the whole transit network system. The present research proposes a simulated annealing (SA) method for optimizing a transit network design. In the algorithm, the strategy to search for neighborhood solutions provides the chance to find the best hybrid of line-type and circular-type routes. The proposed SA method is also compared with other methods. The results show that the proposed SA model is a good alternative for transit network design, particularly as it provides the scope to design hybrids of line-type and circular-type routes.
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References
Agrawal J, Mathew TV (2004) Transit route network design using parallel genetic algorithm. J Comput Civ Eng 18(3):248–256
Amorim P, Parragh SN, Sperandio F, Almada-Lobo B (2012) A rich vehicle routing problem dealing with perishable food: a case study. Top. doi:10.1007/s11750-012-0266-4
Baaj MH, Mahmassani HS (1991) An AI-based approach for transit route system planning and design. J Adv Transp 25(2):187–210
Borndörfer R, Grötschel M, Pfetsch ME (2007) A column-generation approach to line planning in public transport. Transp Sci 41(1):123–132
Ceder A, Wilson NHM (1986) Bus network design. Transp Res, Part B, Methodol 20(4):331–344
Chakroborty P, Dwivedi T (2002) Optimal route network design for transit systems using genetic algorithms. Eng Optim 34(1):83–100
Fan W, Machemehl RB (2006a) Using a simulated annealing algorithm to solve the transit route network design problem. J Transp Eng 132(2):122–132
Fan W, Machemehl RB (2006b) Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J Transp Eng 132(1):40–51
Fan L, Mumford CL (2010) A metaheuristic approach to the urban transit routing problem. J Heuristics 16:353–372
Floyd RD (1962) Algorithm 97: shortest path. Commun ACM 5(6):345
Guihaire V, Hao JK (2010) Transit network timetabling and vehicle assignment for regulating authorities. Comput Ind Eng 59:16–23
Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13:311–329
Johnson DS, Aragon CR, McGeoch LA, Schevon C (1989) Optimization by simulated annealing: an experimental evaluation: Part I, graph partitioning. Oper Res 37:865–892
Kidwai FA (1998) Optimal design of bus transit network: A genetic algorithm based approach. PhD Dissertation, Indian Institute of Technology Kanpur, India
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Lam SW, Tang LC, Goh TN, Halim T (2009) Multiresponse optimization of dispatch rules for public bus service. Comput Ind Eng 56:77–86
Lee DH, Cao Z, Meng Q (2007) Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm. Int J Prod Econ 107:115–124
Lundy M, Mees A (1986) Convergence of an annealing algorithm. Math Program 34:111–124
Mandl CE (1979) Evaluation and optimization of urban public transportation networks. In: The third European congress on operational research, Amsterdam, Netherlands
Marín Á (2007) An extension to rapid transit network design problem. Top 15(2):231–241
Newel CE (1979) Some issues related to the optimal design of bus routes. Transp Sci 13(1):20–35
Pattnaik SB, Mohan S, Tom VM (1998) Urban bus transit route network design using genetic algorithm. J Transp Eng 124(4):368–375
Yang T, Kuo Y, Chang I (2004) Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors—a case study. Int J Prod Res 42:4015–4030
Yang T, Peters BA, Tu M (2005) Layout design for flexible manufacturing systems considering single-loop directional flow patterns. Eur J Oper Res 164:440–455
Zhao F, Gan A (2003) Optimization of transit network to minimize transfers. Final Report, Lehman Center for Transportation Research, Florida International University
Zhao F, Zeng X (2006a) Simulated annealing—genetic algorithm for transit network optimization. J Comput Civ Eng 20(1):57–68
Zhao F, Zeng X (2006b) Optimization for transit network layout and headway with a combined genetic algorithm and simulated annealing method. Eng Optim 38(6):701–722
Zhao F, Zeng X (2008) Optimization of transit route network, vehicle headways and timetables for large-scale transit networks. Eur J Oper Res 186:841–855
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This work was supported, in part, by the National Science Council of Taiwan, Republic of China, under grant NSC-101-2221-E-131-043.
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Kuo, Y. Design method using hybrid of line-type and circular-type routes for transit network system optimization. TOP 22, 600–613 (2014). https://doi.org/10.1007/s11750-013-0273-0
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DOI: https://doi.org/10.1007/s11750-013-0273-0