Abstract
Public transport systems play a critical role in improving mobility and access to opportunities, which is crucial for the socio-economic growth and well-being of any society. A key component of the system is the actual transit network, which usually consists of interconnected nodes and links that enable people to access the system and to travel to their chosen destinations in a smooth and efficient manner. This chapter focuses on the transit network design problem (TNDP), which deals with finding efficient network routes among a set of alternatives that best satisfies the conflicting objectives of different network stakeholders including passengers and operators. The goal of solving this problem is to improve the operational efficiency of a network, thereby reducing costs incurred by the service operator and minimising commuting costs for the commuter. A general description of the problem investigated in this chapter is given, exploring key aspects of the problem and trends in the discipline over time. This is followed by a discussion of the evolution of TNDP solution techniques, namely older mathematical solutions, a more recent meta-heuristics solution framework as well as simulation-based solutions that seem to be gaining traction currently. The chapter rounds off with a look at the future of the problem against technological advancements in transportation and significant structural changes that are likely to occur going forward.
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References
Ceder, A.: Public Transit Planning and Operation: Theory, Modelling and Practice, 1st edn, p. 626. Elsevier, Burlington, MA (2007)
Kepaptsoglou, K., Karlaftis, M.: Transit route network design problem: review. J. Transp. Eng. 135(8), 491–505 (2009)
Rodrigue, J., Comtois, C., Slack, B.: The Geography of Transport Systems, 3rd edn, p. 432. United Kindom, Routledge (2013)
Ferro, P.S., Golub. R.B.A.: Planned and Paratransit Service Integration Through Trunk and Feeder Arrangements: An International Review, pp. 604–618 (2012)
Buba, A.T., Lee, L.S.: A differential evolution for simultaneous transit network design and frequency setting problem. Expert. Syst. Appl. 106, 277–289 (2018)
Ceder, A.: Public Transit Planning and Operation: Modeling, Practice and Behavior, 2nd edn, p. 716. CRC Press (2015)
Ibarra-Rojas, O.J., Delgado, F., Giesen, R., Muñoz, J.C.: Planning, operation, and control of bus transport systems: a literature review. Transp. Res. Part B Methodol. 77(3), 38–75 (2015)
Zuidgeest, M.H.P.: Sustainable Urban Transport Development: A Dynamic Optimisation Approach. University of Twente, Enschede (2005)
Heyken Soares, P., Mumford, C.L., Amponsah, K., Mao, Y.: An adaptive scaled network for public transport route optimisation. Public Transp. 11(2), 379–412 (2019)
Yang, J., Jiang, Y.: Application of modified NSGA-II to the transit network design problem. J. Adv. Transp. 2020(i), (2020)
Cadarso, L., Marin, Á.: Rapid transit network design considering risk aversion. Electron. Notes Discret. Math. 52, 29–36 (2016)
Suman, H.K., Bolia, N.B.: Improvement in direct bus services through route planning. Transp. Policy 81, 263–274 (2019)
Curtin, K.M.: Operations research. In: Kempf-Leonard, K. (ed.) Encyclopedia of Social Measurement, pp. 925–931. Elsevier, Texas (2004)
Ngamchai, S., Lovell, D.J.: Optimal time transfer in bus transit route network design using a genetic algorithm. J. Transp. Eng. 129(October), 510–521 (2003)
Shih, M., Mahmassani, H.: A Design Methodology for Bus Transit Networks with Coordinated Operations. Texas (1994)
Pattnaik, S.B., Mohan, S., Tom, V.M.: Urban bus transit route network design using genetic algorithm. J. Transp. Eng. 124(4), 368–375 (1998)
Cipriani, E., Gori, S., Petrelli, M.: Transit network design: a procedure and an application to a large urban area. Transp. Res. Part C Emerg. Technol. 20(1), 3–14 (2012)
Horni, A., Nagel, K., Axhausen, K.W.: The multi-agent transport simulation MATSim. Multi-Agent Transp. Simul. MATSim 662 (2016)
Nagel, K., Flötteröd, G.: Agent-based traffic assignment: going from trips to behavioral travelers. Int. Conf. Travel. Behav. Res. 1–26 (2009)
Possel, B., Wismans, L.J.J., Van Berkum, E.C., Bliemer, M.C.J.: The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework. Transp. (Amst) 45(2), 545–572 (2018)
Daganzo, C.F., Ouyang, Y.: A general model of demand-responsive transportation services: from taxi to ridesharing to dial-a-ride. Transp. Res. Part B Methodol. 126, 213–224 (2019)
Chen, J., Wang, S., Liu, Z., Wang, W.: Design of suburban bus route for airport access. Transp. A Transp. Sci. 13(6), 568–589 (2017)
Ouyang, Y., Nourbakhsh, S.M., Cassidy, M.J.: Continuum approximation approach to bus network design under spatially heterogeneous demand. Transp. Res. Part B Methodol. 68(18), 333–344 (2014)
Szeto, W.Y., Wu, Y.: A simultaneous bus route design and frequency setting problem for Tin Shui Wai Hong Kong. Eur. J. Oper. Res. 209(2), 141–155 (2011)
Nikolić, M., Teodorović, D.: A simultaneous transit network design and frequency setting: computing with bees. Expert. Syst. Appl. 41(16), 7200–7209 (2014)
Nnene, O.A., Joubert, J.W., Zuidgeest, M.H.P.: Transit network design with meta-heuristic algorithms and agent based simulation. IFAC-PapersOnLine 52(3), 13–18 (2019)
Newell, G.F.: Some issues related to the optimal design of bus routes. Transp. Sci. 13(1), 20–35 (1979)
Fusco, G., Gori, S., Petrelli, M.: A heuristic transit network design algorithm for medium size towns. In: Proceedings of 9th Euro Working Group on Transportation, Bari, pp. 652–656. http://www.iasi.cnr.it/ewgt/13conference/116_fusco.pdf (2002)
Fan, W., Machemehl, R.B.: Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J. Transp. Eng. 132(1), 40–51 (2006)
Nnene, O.A., Zuidgeest, M.H.P., Beukes, E.A.: Application of metaheuristic algorithms to the improvement of the MyCiTi BRT network in Cape Town. J. S. Afr. Inst. Civ. Eng. 59(4), 56–63 (2017)
Talbi, E.: From Design to Implementation [Internet], vol. 2009. Main (2009). http://onlinelibrary.wiley.com. https://doi.org/10.1002/9780470496916.fmatter/summary
Fan, W., Machemehl, R.B.: Optimal Transit Route Network Design Problem: Algorithms, Implementations, vol. 7. Texas (2004)
Fan, W., Machemehl, R.B.: Optimal Transit Route Network Design Problem: Algorithms, Implementations, and Numerical Results, vol. 7. Southwest Region University Transportation Center Texas Transportation Institute Texas A&M University System College, Texas (2004)
Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization: Methods and Case Studies, p. 369. Springer (2006)
Durán-Micco, J., Vansteenwegen, P.: A survey on the transit network design and frequency setting problem. Public Transp. 14, 155–190 (2022)
Johar, A., Jain, S.S., Garg, P.K.: Transit network design and scheduling using genetic algorithm–a review. Int. J. Optim. Control Theor. Appl. 6(1), 9–22 (2016)
Lampkin, W., Saalmans, P.D.: The design of routes, service frequencies, and schedules for a municipal bus undertaking: a case study. J. Oper. Res. Soc. 18(4), 375–397 (1967)
Dubois, D., Bel, G., Llibre, M.: A set of methods in transportation network synthesis and analysis. J. Oper. Res. Soc. [Internet] 30(9), 797–808. http://link.springer.com (1979). https://doi.org/10.1057/jors.1979.190. [cited 2017 Aug 15]
LeBlanc, L.J.: Transit system network design. Transp. Res. Part B Methodol. 22B(No 5), 383–90 (1988)
Constantin, I., Florian, M.: Optimizing frequencies in a transit network: a nonlinear bi-level programming approach. Int. Trans. Oper. Res. 2(2), 149–164 (1995)
Huang, D., Liu, Z., Fu, X., Blythe, P.T.: Multimodal transit network design in a hub-and-spoke network framework. Transp. A Transp. Sci. 14(8), 706–735 (2018)
An, K., Lo, H.K.: Robust transit network design with stochastic demand considering development density. Transp. Res. Procedia 7(852), 300–319 (2015)
Nnene, O.A., Joubert, J.W., Zuidgeest, M.H.P.: An agent-based evaluation of transit network design. Procedia Comput. Sci. 151, 757–762 (2019)
Arbex, R.O., da Cunha, C.B.: Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transp. Res. Part B Methodol. 81, 355–376 (2015)
Madadi, B., Nes, R., Snelder, M., Arem, B.: A bi‐level model to optimize road networks for a mixture of manual and automated driving: an evolutionary local search algorithm. Comput. Civ. Infrastruct. Eng. 1–17 (2019)
Lee, Y.J., Vuchic, V.R.: Transit network design with variable demand. J. Transp. Eng. 131(1), 1–10 (2005)
Beltran, B., Carrese, S., Cipriani, E., Petrelli, M.: Transit network design with allocation of green vehicles: a genetic algorithm approach. Transp. Res. Part C Emerg. Technol. 17(5), 475–483 (2009)
Gallo, M., D’Acierno, L., Montella, B.: A meta-heuristic approach for solving the urban network design problem. Eur. J. Oper. Res. 201(1), 144–157 (2010)
Gallo, M., Montella, B., D’Acierno, L.: 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(6), 1276–1305 (2011)
Brands, T., van Berkum, E.: Performance of a genetic algorithm for solving the multi-objective, multimodal transportation network design problem. Int. J. Transp. 2(1), 1–20 (2014)
Sharma, S., Ukkusuri, S.V., Mathew, T.V.: Pareto optimal multiobjective optimization for robust transportation network design problem. Transp. Res. Rec. 2090, 95–104 (2009)
Gosavi, A.: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, 2nd edn, p. 554. Springer, New York (2015)
Bhatnagar, S., Prasad, H.L., Prashanth, L.A.: Stochastic Recursive Algorithms for Optimization, 1st edn. In: Lecture Notes in Control and Information Sciences, vol. 434, p. 280. Springer, London (2013)
Gosavi, A.: Background, 2nd edn. In: Simulation Based Optimization: Parametric Optimization and Reinforcement Learning, pp. 1–12. Springer, New York (2015)
Castiglione, J., Bradley, M., Gliebe, J.: Activity-Based Travel Demand Models: A Primer. SHRP 2 Report. Transportation Research Board of the National Academies, Vermont (2015)
Bates, J.: History of demand modelling. In: Hensher, D.A., Button, K.J. (eds.) Handbook of Transport Modelling, p. 11–33. Emerald Group (2000)
Duell, M., Amini, N., Chand, S., Saxena, N., Grzybowska, H., Waller, T.: Deploying a dynamic traffic assignment model for the Sydney region. Australas. Transp. Res. Forum 2015 Proc. (September), 1–14 (2015)
White, K.P., Ingalls, R.G.: The basics of simulation. In: Proceedings-Winter Simulation Conference, pp. 38–52. Washington, DC (2016)
Bohari, Z.A., Bachok, S., Osman, M.M.: Simulating the pedestrian movement in the public transport infrastructure. Procedia Soc. Behav. Sci. [Internet] 222, 791–799 (2016). https://doi.org/10.1016/j.sbspro.2016.05.167
Papageorgiou, G., Damianou, P., Pitsillides, A., Aphamis, T., Ioannou, P.: Modelling and simulation of transportation systems: planning for a bus priority system. 6th EUROSIM Congr. Model Simul. (EUROSIM 2007) 2, 325–335 (2007)
Cortés, C.E., Burgos, V., Rodrigo, F.: Modelling passengers, buses and stops in traffic microsimulation: review and extensions. J. Adv. Transp. [Internet] 47(June 2010), 512–25 (2011). http://onlinelibrary.wiley.com. https://doi.org/10.1002/atr.144/full
Yin, H., Han, B., Li, D., Wu, J., Sun, H.: Modeling and simulating passenger behavior for a station closure in a rail transit network. PLoS One 11(12), 1–28 (2016)
Schruben, L.: Model is a verb. In: Tolk, A., Fowler, J., Shao, G., Yücesan, E. (eds.) Advances in Modeling and Simulation [Internet], pp. 17–25. Springer, Cham (2017) [cited 2019 Feb 9]. http://link.springer.com.https://doi.org/10.1007/978-3-319-64182-9_2
Uhrmacher, A.M., Weyns, D. (eds.): Multi-Agent Systems: Simulation and Application, 541 p. CRC Press (2009)
Heath, B.L., Hill, R.R.: Some insights into the emergence of agent-based modelling. J. Simul. 4(3), 163–169 (2010)
Hebb, D.O.: The organisation of behaviour: a neuropsychological theory. Wiley (1952)
Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. [Internet] 99(suppl. 3), 7280–7287. http://www.ncbi.nlm.nih.gov/pubmed/12011407 (2002)
Siebers, P.O., Macal, C.M., Garnett, J., Buxton, D., Pidd, M.: Discrete-event simulation is dead, long live agent-based simulation! J. Simul. [Internet] 4(3), 204–210 (2010). http://link.springer.com. https://doi.org/10.1057/jos.2010.14
Weimer, C.W., Miller, J.O., Hill, R.R.: Agent-based modeling: an introduction and primer. In: Roeder, T.M.K., Frazier, P.I., Szechtman, R., Zhou, E., Huschka, T., Chick, S.E. (eds.) Proceedings of the 2016 Winter Simulation Conference, p. 65–79. Washington DC (2016)
Zheng, H., Son, Y.J., Chiu, Y.C., Head, L., Feng, Y., Xo, H., et al.: A Primer for Agent-Based Simulation and Modeling in Transportation Applications. Development Springfield (2013)
Balmer, M., Axhausen, K.W., Nagel, K.: An agent based demand modeling framework for large scale micro-simulations. In: 85th Annual Meeting of the Transportation Research Board, p. 43. Washington, DC (2006)
Vuk, G.: Activity Based Travel Demand Modelling-A Literature Study. Danmarks Transport Forskning (2001)
Wardrop, J.G.: Some theoretical aspects of road traffic research. In: Proceedings of the Institution of Civil Engineers Part II, pp. 325–62 (1952)
de Dios, O.J., Willumsen, L.G.: Modelling Transport, 4th edn., p. 580. Wiley & Sons, Sussex, UK (2011)
Sheffi, Y.: Urban transportation networks: equilibrium analysis with mathematical programming methods. Transp. Res. Part A Gen. [Internet] 20(1), 76–7 1986. https://doi.org/10.1016/0191-2607(86)90023-3
Kaufman, D.E., Smith, R.L., Wunderlich, K.E.: An iterative routing/assignment method for anticipatory real-time route guidance. In: Vehicle Navigation and Information Systems Conference, 1991, pp. 693–700. IEEE, Ann Arbor, MI (1991)
Friedrich, M., Hofsaß, I.: A Dynamic traffic assignment method for planning and telematic applications. In: Proceedings of Seminar K, European Transport Conference, pp. 29–40. University of Stuttgart, Cambridge (2000).
Chiu, Y.C., Bottom, J., Mahut, M., Paz, A., Balakrishna, R., Waller, T., et al.: Dynamic Traffic Assignment: A Primer [Internet]. Transportation Research E-Circular. Washington, DC. https://onlinepubs.trb.org/onlinepubs/circulars/ec153.pdf (2011)
Elarbi, M., Bechikh, S., Ben Said, L., Datta, R.: Multi-objective optimization: classical and evolutionary approaches. In: Bechikh, S., Datta, R., Gupta, A. (eds.) Recent Advances in Evolutionary Multi-objective Optimization, pp. 1–30. Springer, Cham (2017)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, 1st edn, p. 518. Wiley & Sons (2001)
Branke, J., Deb, K., Miettinen, K., Slowiński, R.: Multiobjective Optimization-Interactive and Evolutionary Approaches, 1st edn, p. 470. Springer-Verlag, Berlin (2008)
Rangaiah, G.P., Bonilla-Petriciolet, A. (eds.): Multi-Objective Optimization in Chemical Engineering, 512 p. Wiley & Sons Ltd., Oxford, UK (2013)
Elarbi, M., Bechikh, S., Ben Said, L., Datta, R.: Multi-objective optimization: classical and evolutionary approaches. In: Bechikh, S., Datta, R., Gupta, A. (eds.) Recent Advances in Evolutionary Multi-objective Optimization, pp. 1–30. Cham, Springer (2017)
Nnene OA. Simulation-based optimisation of public transport networks [Internet]. Faculty of Engineering and the Built Environment; 2020. Available from: https://open.uct.ac.za/handle/11427/32308
Nicolai, T.W.: MATSim for UrbanSim: integrating an urban simulation model with a travel model. Ph.D. (2013)
Rieser, M.: Adding transit to an agent-based transportation simulation. Ph.D. (2010)
Bray, T., Jean, P., Sperberg-McQueen, C.M., Maler, E., Yergeau, F., Cowan, J.: Extensible Markup Language (XML) 1.1, 2nd edn. In: W3C Recommendation. W3C (2006)
McHugh, B.: Pioneering open data standards: the GTFS story. In: Beyond Transparency-Open Data and Future of Civic Innovation, pp. 125–136 (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Julian, J., et al. (eds.) Parallel Problem Solving from Nature PPSN VI PPSN 2000 Lecture Notes in Computer Science, vol. 1917, pp. 849–858. Springer-Verlag, Berlin (2000)
Talbi, E.: Common concepts for metaheuritics, 2nd edn. In: Talbi, E., Zomaya, A.Y. (eds.) Metaheuristics: From Design to Implementation. Wiley and Sons, USA (2009)
TDA. Integrated Public Transport Network 2032: Network Plan (2014)
Arnold, K., Gosling, J.: The Java Programming Language, p. 595. Addison-Wesley (2000)
Michail, D., Kinable, J., Naveh, B., Sichi, J.V.: JGraphT—a java library for graph data structures and algorithms, p. 27 (2019)
Brandes, U., Eiglsperger, M., Herman, I., Himsolt, M., Marshall, M.S.: GraphML progress report structural layer proposal. In: Mutzel, P., Jünger, M., Leipert, S. (eds.) Graph Drawing GD 2001 Lecture Notes in Computer Science, vol. 2265, pp. 501–512. Springer, Berlin (2002)
Crockford, D.: Introducing JSON. https://www.json.org/ (2011)
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Nnene, O.A., Zuidgeest, M.H.P., Joubert, J.W. (2023). Optimising Transit Networks Using Simulation-Based Techniques. In: Upadhyay, R.K., Sharma, S.K., Kumar, V., Valera, H. (eds) Transportation Systems Technology and Integrated Management. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-99-1517-0_15
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