Abstract
A critical step in the design of urban transport networks is the determination of the routes and the frequencies of buses. This situation entails a highly combinatorial optimization problem with a complex computational solution, even for small instances. Several studies have addressed such a situation, minimizing travel times as the main objective. However, the growing trend toward the development of sustainable transport operations requires that the design of the network also considers the emissions of toxic gases that result from combustion, which leads to a new variant of this type of problem, called the pollution transit network design problem. In this paper, the problem is formulated as a biobjective mathematical programming model. Complex problem instances are proposed for this problem, and by using a multi-objective genetic algorithm, we approach the unimodal and bimodal version of the problem by taking into account the elastic demand between buses and cars. By using the proposed mathematical programming model and the genetic algorithm for small and large problem instances, respectively, we show that the generated pollutant emissions are drastically reduced without increasing travel times or costs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Arbex RO, da Cunha CB (2015) Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transp Res Part B Methodol 81:355–376. https://doi.org/10.1016/j.trb.2015.06.014
Bagloee S, Ceder A (2011) Transit-network design methodology for actual-size road networks. Transp Res Part B Methodol 45(10):1787–1804. https://doi.org/10.1016/j.trb.2011.07.005
Beltran B, Carrese S, Cipriani E, Petrelli M (2009) Transit network design with allocation of green vehicles: a genetic algorithm approach. Transp Res Part C Emerg Technol Artif Intell Transp Anal Approach Methods Appl 17(5):475–483. https://doi.org/10.1016/j.trc.2009.04.008
Borndörfer R, Grötschel M, Pfetsch ME (2007) A column-generation approach to line planning in public transport. Transp Sci 41(1):123–132. https://doi.org/10.1287/trsc.1060.0161
Bravo M, Pradenas L, Parada V (2019) An evolutionary algorithm for the multi-objective pick-up and delivery pollution-routing problem. Int Trans Oper Res 26(1):302–317. https://doi.org/10.1111/itor.12376
Cadarso L, Marín Á (2017) Improved rapid transit network design model: considering transfer effects. Ann Oper Res 258(2):547–567. https://doi.org/10.1007/s10479-015-1999-x
Cahon S, Melab N, Talbi E-G (2004) ParadisEO: a framework for the reusable design of parallel and distributed metaheuristics. J Heuristics 10(3):357–380. https://doi.org/10.1023/B:HEUR.0000026900.92269.ec
Canca D, De-Los-Santos A, Laporte G, Mesa JA (2016) A general rapid network design, line planning and fleet investment integrated model. Ann Oper Res 246(1):127–144. https://doi.org/10.1007/s10479-014-1725-0
Cancela H, Mauttone A, Urquhart ME (2015) Mathematical programming formulations for transit network design. Transp Res Part B Methodol 77:17–37. https://doi.org/10.1016/j.trb.2015.03.006
Ceder A (2015) Public transit planning and operation: modeling, practice and behavior, 2nd edn. CRC Press, Boca Raton
Chakroborty P (2003) Genetic algorithms for optimal urban transit network design. Comput Aided Civ Infrastruct Eng 18:184–200. https://doi.org/10.1111/1467-8667.00309
Demir E, Bektaş T, Laporte G (2014) A review of recent research on green road freight transportation. Eur J Oper Res 237(3):775–793. https://doi.org/10.1016/j.ejor.2013.12.033
Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013) A review of urban transportation network design problems. Eur J Oper Res 229(2):281–302. https://doi.org/10.1016/j.ejor.2013.01.001
Gallo M, Montella B, D’Acierno L (2011) The transit network design problem with elastic demand and internalisation of external costs: an application to rail frequency optimisation. Transp Res Part C Emerg Technol 19(2):1276–1305. https://doi.org/10.1016/j.trc.2011.02.008
GAMS Development Corporation (2014) General Algebraic Modeling System (GAMS) Release 24.3.3. Washington, DC
Guihaire V, Hao J-K (2008) Transit network design and scheduling: a global review. Transp Res Part A Policy Pract 42(10):1251–1273. https://doi.org/10.1016/j.tra.2008.03.011
Hutter F, Stuetzle T, Leyton-Brown K, Hoos HH (2009) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res 36:267–306. https://doi.org/10.1613/jair.2861
Jiménez F, Román A (2016) Urban bus fleet-to-route assignment for pollutant emissions minimization. Transp Res Part E Logist Transp Rev 85:120–131. https://doi.org/10.1016/j.tre.2015.11.003
John MP, Mumford CL, Lewis R (2014) An improved multi-objective algorithm for the urban transit routing problem. In: Blum C, Ochoa G (eds) Evolutionary computation in combinatorial optimisation. EvoCOP 2014. Lecture Notes in Computer Science, vol 8600. Springer, Berlin, pp 49–60. https://doi.org/10.1007/978-3-662-44320-0_5
Kepaptsoglou K, Karlaftis M (2009) Transit route network design problem: review. J Transp Eng 135(8):491–505. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:8(491)
Khan OA (2007). Modelling passenger mode choice behaviour using computer aided stated preference data (Thesis). Queensland University of Technology
Lin C, Choy KL, Ho GTS, Chung SH, Lam HY (2014) Survey of green vehicle routing problem: past and future trends. Expert Syst Appl 41(4):1118–1138. https://doi.org/10.1016/j.eswa.2013.07.107
Magnanti TL, Wong RT (1984) Network design and transportation planning: models and algorithms. Transp Sci 18(1):1–55. https://doi.org/10.1287/trsc.18.1.1
Mandl CE (1980) Evaluation and optimization of urban public transportation networks. Eur J Oper Res 5(6):396–404. https://doi.org/10.1016/0377-2217(80)90126-5
Mauttone A, Urquhart ME (2010) A multi-objective metaheuristic approach for the transit network design problem. Public Transp 1(4):253–273. https://doi.org/10.1007/s12469-010-0016-7
Miandoabchi E, Farahani RZ, Dullaert W, Szeto WY (2012) Hybrid evolutionary metaheuristics for concurrent multi-objective design of urban road and public transit networks. Netw Spat Econ 12(3):441–480. https://doi.org/10.1007/s11067-011-9163-x
Mumford CL (2013) New heuristic and evolutionary operators for the multi-objective urban transit routing problem. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 939–946. https://doi.org/10.1109/cec.2013.6557668
Newell G (1979) Some issues relating to the optimal design of bus routes. Transp Sci 13(1):20–35. https://doi.org/10.1287/trsc.13.1.20
Nikolić M, Teodorović D (2014) A simultaneous transit network design and frequency setting: computing with bees. Expert Syst Appl 41(16):7200–7209. https://doi.org/10.1016/j.eswa.2014.05.034
Patriksson M (2015) The traffic assignment problem: models and methods. VSP, Utrecht
Pérez JC, Carrillo MH, Montoya-Torres JR (2015) Multi-criteria approaches for urban passenger transport systems: a literature review. Ann Oper Res 226(1):69–87. https://doi.org/10.1007/s10479-014-1681-8
Pradenas L, Oportus B, Parada V (2013) Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Syst Appl 40(8):2985–2991. https://doi.org/10.1016/j.eswa.2012.12.014
Pternea M, Kepaptsoglou K, Karlaftis MG (2015) Sustainable urban transit network design. Transp Res Part A Policy Pract 77:276–291. https://doi.org/10.1016/j.tra.2015.04.024
Sanchez M, Pradenas L, Deschamps J-C, Parada V (2016) Reducing the carbon footprint in a vehicle routing problem by pooling resources from different companies. NETNOMICS Econ Res Electron 17(1):29–45. https://doi.org/10.1007/s11066-015-9099-2
Sbihi A, Eglese RW (2010) Combinatorial optimization and Green Logistics. Ann Oper Res 175(1):159–175. https://doi.org/10.1007/s10479-009-0651-z
Schöbel A, Scholl S (2006) Line planning with minimal traveling time. http://goedoc.uni-goettingen.de/goescholar/handle/1/5719. Accessed 14 Aug 15
Sheffi Y (1985) Urban transportation networks: equilibrium analysis with mathematical programming methods. Prentice Hall, Englewood Cliffs
Spiess H, Florian M (1989) Optimal strategies: a new assignment model for transit networks. Transp Res Part B Methodol 23(2):83–102. https://doi.org/10.1016/0191-2615(89)90034-9
Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, New York
Toth P, Vigo D (2014) Vehicle routing, MOS-SIAM series on optimization. Society for Industrial and Applied Mathematics, Philadelphia
US EPA, C.C.D. (2014). U.S. Greenhouse Gas Inventory Report. http://www3.epa.gov/climatechange/ghgemissions/usinventoryreport.html. Accessed 18 Sep 15
Vuchic VR (2005) Urban transit: operations, planning and economics, 1st edn. Wiley, Hoboken
Wan QK, Lo HK (2003) A mixed integer formulation for multiple-route transit network design. J Math Model Algorithms 2(4):299–308. https://doi.org/10.1023/B:JMMA.0000020425.99217.cd
Wan QK, Lo HK (2009) Congested multimodal transit network design. Public Transp 1(3):233–251. https://doi.org/10.1007/s12469-009-0015-8
Wang J-Y, Lin C-M (2010) Mass transit route network design using genetic algorithm. J Chin Inst Eng 33(2):301–315. https://doi.org/10.1080/02533839.2010.9671619
Yen JY (1971) Finding the K shortest loopless paths in a network. Manag Sci 17(11):712–716
Zhang L, Yang H, Wu D, Wang D (2014) Solving a discrete multimodal transportation network design problem. Transp Res Part C Emerg Technol 49:73–86. https://doi.org/10.1016/j.trc.2014.10.008
Zhao H, Xu W, Jiang R (2015) The Memetic algorithm for the optimization of urban transit network. Expert Syst Appl 42(7):3760–3773. https://doi.org/10.1016/j.eswa.2014.11.056
Funding
This study was partially supported by the Grants: BASALCONICYT-FB0816 and ECOS/CONICYT, No. C13E04.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Duran, J., Pradenas, L. & Parada, V. Transit network design with pollution minimization. Public Transp 11, 189–210 (2019). https://doi.org/10.1007/s12469-019-00200-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-019-00200-5