Advertisement

A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population

  • Enrique Gabriel BaquelaEmail author
  • Ana Carolina Olivera
Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

Traffic network design is a complex problem due to its nonlinear and stochastic nature. The Origin-Destiny Traffic Assignment Problem is particular case of this problem. In it, we are faced with an increase in the system vehicle population; and, we want to determine where to set the generating nodes and the traffic consumers, minimizing the current system and trying to reduce necessary investment. Performing optimizations in an analytical way in this kind of problems tends to be really complicated and a bit impractical, since it is difficult to estimate vehicle flows. In this chapter, we propose the use of a Multi-Objective Particle Swamp Optimization together with Traffic Simulations in order to generate restructuring alternatives that optimize both, traffic flow and cost associated to this restructure. This approach allows to obtain a very good approximation of the Pareto Frontier of the problem, with a fast convergence to the low infrastructure cost solutions and a total coverage of the frontier when the number of iterations is high.

Keywords

Traffic net design Particle swarm optimization Metaheuristics Traffic simulation Simulated optimization 

Notes

Acknowledgements

The work of Baquela E. G. was supported by the Universidad Tecnologica Nacional, under PID TVUTNSN0003605. Olivera A. C. thanks to ANPCyT for grant PICT 2014-0430, CONICET (PCB-I), and Universidad Nacional de la Patagonia Austral for PI 29/B168.

References

  1. 1.
    S.K. Azad, O. Hasancebi, S.K. Azad, Upper bound strategy for metaheuristic based design optimization of steel frames. Adv. Eng. Soft. 57, 19–32 (2013)CrossRefGoogle Scholar
  2. 2.
    A. Banks, J. Vincent, C. Anyakoha, A review of particle swarm optimization. Part I: background and development. Nat. Comput. 6(4), 467–484 (2007)Google Scholar
  3. 3.
    A. Banks, J. Vincent, C. Anyakoha, A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat. Comput. 7(1), 109–124 (2008)Google Scholar
  4. 4.
    E.G. Baquela, A.C. Olivera, Combining genetic algorithms with traffic simulations for restructuring traffic networks subject to increases in population, in International Conference on Metaheuristics and Nature Inspired Computing 2014 (2014)Google Scholar
  5. 5.
    M. Behrisch, L. Bieker, J. Erdmann, D. Krajzewicz, Sumo – simulation of urban mobility: an overview, in SIMUL 2011, The Third International Conference on Advances in System Simulation, Barcelona, 2011, pp. 63–68Google Scholar
  6. 6.
    J. Brownlee, Clever Algorithms: Nature-Inspired Programming Recipes (2011). https://cs.gmu.edu/~sean/book/metaheuristics/ Google Scholar
  7. 7.
    L. Caggiani, M. Ottomanelli, Traffic equilibrium network design problem under uncertain constraints. Procedia Soc. Behav. Sci. 20, 372–380 (2011)CrossRefGoogle Scholar
  8. 8.
    P. Carrasqueira, M.J. Alves, C.H. Antunes, A bi-level multiobjective pso algorithm, in Evolutionary Multi-Criterion Optimization – 8th International Conference, Apr 2015Google Scholar
  9. 9.
    H.W. Casey, Simulation optimization of traffic light signal timings via perturbation analysis, Ph.D. thesis, Faculty of the Graduate School of the University of Maryland, College Park, 2006Google Scholar
  10. 10.
    H. Ceylan, M. Bell, Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion. Transp. Res. B 39, 169–185 (2005)CrossRefGoogle Scholar
  11. 11.
    D. Chowdhury, L. Santen, A. Schadschneider, Statistical physics of vehicular traffic and some related systems. Physics Report 329 (2000), pp. 199–329CrossRefGoogle Scholar
  12. 12.
    K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in Parallel Problem Solving from Nature PPSN VI. Lecture Notes in Computer Science, vol. 1917 (Springer, Berlin, 2000), pp. 849–858Google Scholar
  13. 13.
    S. Dinu, G. Bordea, A new genetic approach for transport network design and optimization. Bull. Pol. Acad. Sci. Tech. Sci. 59(3), 263–272 (2011)Google Scholar
  14. 14.
    J.J. Durillo, J. Garcia-Nieto, A.J. Nebro, C.A.C. Coello, F. Luna, E. Alba, Multi-objective particle swarm optimizers: an experimental comparison, in Evolutionary Multi-Criterion Optimization 2009 (2009)Google Scholar
  15. 15.
    R.C. Eberhart, Y. Shi, Particle swarm optimization: developments, applications and resources, in Proceedings of the 2001 Congress on Evolutionary Computation, 2001, vol. 1 (2001), pp. 81–86Google Scholar
  16. 16.
    R.Z. Farahani, E. Miandoabchi, W. Szeto, H. Rashidi, A review of urban transportation network design problems. Eur. J. Oper. Res. 229, 281–302 (2013)CrossRefGoogle Scholar
  17. 17.
    J.A. Ferreira, B. Condessa, Defining expansion areas in small urban settlements an application to the municipality of Tomar (Portugal). Landsc. Urban Plan. 107, 281–302 (2012)CrossRefGoogle Scholar
  18. 18.
    J.A. Ferreira, B. Condessa, J.C. e Almeida, P. Pinto, Urban settlements delimitation in low-density areas an application to the municipality of Tomar (Portugal). Landsc. Urban Plan. 97(3), 156–167 (2010)Google Scholar
  19. 19.
    H. Fredik, Towards the solution of large-scale and stochastic traffic network design problems. Master’s thesis, Uppsala Universitet, 2010Google Scholar
  20. 20.
    T.L. Friesz, H.-J. Cho, N.J. Mehta, R.L. Tobin, G. Anandalingam, A simulated annealing approach to the network design problem with variational inequality constraints. Transp. Sci. 26(1), 18–26 (1992)CrossRefGoogle Scholar
  21. 21.
    M. Frutos, A.C. Olivera, F. Tohmé, A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem. Ann. Oper. Res. 181, 745–765 (2010)CrossRefGoogle Scholar
  22. 22.
    M.C. Fu, Optimization via simulation: a review. Ann. Oper. Res. 53, 199–247 (1994)CrossRefGoogle Scholar
  23. 23.
    M.C. Fu, Optimization for simulation: theory vs. practice. INFORMS J. Comput. 14(3), 192–215 (2002)Google Scholar
  24. 24.
    M. Gallo, L. DAcierno, B. Montella, A meta-heuristic algorithm for solving the road network design problem in regional contexts. Procedia Soc. Behav. Sci. 54, 84–95 (2012). Proceedings of EWGT2012 – 15th Meeting of the EURO Working Group on Transportation, September 2012, ParisGoogle Scholar
  25. 25.
    A.H. Gandomi, X.-S. Yang, S. Talatahari, A.H. Alavi, 1 – metaheuristic algorithms in modeling and optimization, in Metaheuristic Applications in Structures and Infrastructures, ed. by A.H. Gandomi, X.-S. Yang, S. Talatahari, A.H. Alavi (Elsevier, Oxford, 2013), pp. 1–24Google Scholar
  26. 26.
    J. Garcia-Nieto, A. Olivera, E. Alba, Optimal cycle program of traffic lights with particle swarm optimization. IEEE Trans. Evol. Comput. 17, 823–839 (2013)CrossRefGoogle Scholar
  27. 27.
    H.C. Gomes, F. de Assis das Neves, M.J.F. Souza, Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations. Comput. Oper. Res. 44, 92–104 (2014)Google Scholar
  28. 28.
    N. Haregeweyn, G. Fikadu, A. Tsunekawa, M. Tsubo, D.T. Meshesha, The dynamics of urban expansion and its impacts on land use land cover change and small-scale farmers living near the urban fringe: a case study of Bahir Dar, Ethiopia. Landsc. Urban Plan. 106(2), 149–157 (2012)CrossRefGoogle Scholar
  29. 29.
    C. He, N. Okada, Q. Zhang, P. Shi, J. Li, Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landsc. Urban Plan. 86(1), 79–91 (2008)CrossRefGoogle Scholar
  30. 30.
    C. He, J. Tian, P. Shi, D. Hu, Simulation of the spatial stress due to urban expansion on the wetlands in Beijing, China using a GIS-based assessment model. Landsc. Urban Plan. 101(3), 269–277 (2011)Google Scholar
  31. 31.
    A. Horvat, A. Tosic, Optimization of traffic networks by using genetic algorithms. Elektrotehniski Vestnik 79, 197–200 (2012)Google Scholar
  32. 32.
    M.K. Jha, M. Head, S.P. Gar, 23 – metaheuristic applications in bridge infrastructure maintenance scheduling considering stochastic aspects of deterioration, in Metaheuristic Applications in Structures and Infrastructures, ed. by A.H. Gandomi, X.-S. Yang, S. Talatahari, A.H. Alavi (Elsevier, Oxford, 2013), pp. 539–556CrossRefGoogle Scholar
  33. 33.
    J. Jin, T.G. Crainic, A. Lokketangen, A cooperative parallel metaheuristic for the capacitated vehicle routing problem. Comput. Oper. Res. 44, 33–41 (2014)CrossRefGoogle Scholar
  34. 34.
    T. Kalganova, G. Russell, A. Cumming, Multiple traffic signal control using a genetic algorithm, in Artificial Neural Nets and Genetic Algorithms (Springer, Vienna, 1999), pp. 220–228Google Scholar
  35. 35.
    J. Kennedy, R. Eberhart, Particle swarm optimization, in International Conference on Neural Networks (IEEE Service Center, Piscataway, NJ, 1995), pp. 1942–1948Google Scholar
  36. 36.
    J. Kratica, An electromagnetism-like metaheuristic for the uncapacitated multiple allocation p-hub median problem. Comput. Ind. Eng. 66(4), 1015–1024 (2013)CrossRefGoogle Scholar
  37. 37.
    S. Krauss, Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Hauptabteilung Mobilität und Systemtechnik des DLR Köln, 1998Google Scholar
  38. 38.
    V. Kumar, S. Minz, Multi-objective particle swarm optimization: an introduction. Smart Comput. Rev. 4, 335–353 (2014)Google Scholar
  39. 39.
    D.T. Lang, XML: Tools for parsing and generating XML within R and S-Plus, R package version 3.95-0.1 (2012)Google Scholar
  40. 40.
    Y. Li, D. Sun, Microscopic car-following model for the traffic flow: the state of the art. J. Control Theory Appl. 10(2), 133–143 (2012)CrossRefGoogle Scholar
  41. 41.
    Y. Li, X. Zhu, X. Sun, F. Wang, Landscape effects of environmental impact on bay-area wetlands under rapid urban expansion and development policy: a case study of Lianyungang, China. Landsc. Urban Plan. 94(3,4), 218–227 (2010)Google Scholar
  42. 42.
    A. Liefooghe, S. Verel, J.-K. Hao, A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Appl. Soft Comput. 16, 10–19 (2014)CrossRefGoogle Scholar
  43. 43.
    S. Luke, Essentials of Metaheuristics, 2nd edn. (Lulu, 2013). https://cs.gmu.edu/~sean/book/metaheuristics/
  44. 44.
    E. Miandoabchi, R.Z. Farahani, Optimizing reserve capacity of urban road networks in a discrete network design problem. Adv. Eng. Softw. 42(12), 1041–1050 (2011)CrossRefGoogle Scholar
  45. 45.
    K. Nagel, M. Schreckenberg, A cellular automaton model for freeway traffic. J. Phys. I 2, 2221–2229 (1992)Google Scholar
  46. 46.
    N. Nedjah, L. de Macedo Mourelle, Evolutionary multi objective optimisation: a survey. Int. J. Bio-Inspired Comput. 7(1), 1–25 (2015)CrossRefGoogle Scholar
  47. 47.
    E. Olivares-Benitez, R.Z. Rios-Mercado, J.L. Gonzalez-Velarde, A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. Int. J. Prod. Econ. 145(1), 161–172 (2013)CrossRefGoogle Scholar
  48. 48.
    M.J. Reddy, D.N. Kumar, Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol. Process. 21, 2897–2909 (2007)CrossRefGoogle Scholar
  49. 49.
    M. Reyes-Sierra, C.A.C. Coello, Improving pso-based multi-objective optimization using crowding, mutation and e-dominance, in Evolutionary Multi-Criterion Optimization – Third International Conference (2005)Google Scholar
  50. 50.
    M. Reyes-Sierra, C.A.C. Coello, Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2, 287–308 (2006)Google Scholar
  51. 51.
    T.M. Sands, D. Tayal, M.E. Morris, S.T. Monteiro, Robust stock value prediction using support vector machines with particle swarm optimization, in 2015 IEEE Congress on Evolutionary Computation (CEC), May 2015, pp. 3327–3331Google Scholar
  52. 52.
    M.-P. Song, G.C. Gu. Research on particle swarm optimization: a review, in Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, vol. 4, Aug 2004, pp. 2236–2241Google Scholar
  53. 53.
    K. Stanilov, M. Batty, Exploring the historical determinants of urban growth patterns through cellular automata. Trans. GIS 15(3), 253–271 (2011)CrossRefGoogle Scholar
  54. 54.
    S. Talatahari, 17 – optimum performance-based seismic design of frames using metaheuristic optimization algorithms, in Metaheuristic Applications in Structures and Infrastructures, ed. by A.H. Gandomi, X.-S. Yang, S. Talatahari, A.H. Alavi (Elsevier, Oxford, 2013), pp. 419–437CrossRefGoogle Scholar
  55. 55.
    R.C. Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2012). ISBN 3-900051-07-0Google Scholar
  56. 56.
    S.T. Waller, A.K. Ziliaskopoulos, A chance-constrained based stochastic dynamic traffic assignment model: analysis, formulation and solution algorithms. Transp. Res. 14, 418–427 (2006)Google Scholar
  57. 57.
    S.T. Waller, K.C. Mouskos, D. Kamaryiannis, A.K. Ziliaskopoulos, A linear model for the continuous network design problem. Comput. Aided Civ. Inf. Eng. 21, 334–345 (2006)CrossRefGoogle Scholar
  58. 58.
    J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, Z. Huang, Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc. Urban Plan. 75(1,2), 69–80 (2006)Google Scholar
  59. 59.
    X.-S. Yang, 1 – optimization and metaheuristic algorithms in engineering, in Metaheuristics in Water, Geotechnical and Transport Engineering, ed. by X.-S. Yang, A.H. Gandomi, S. Talatahari, A.H. Alavi (Elsevier, Oxford, 2013), pp. 1–23Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Enrique Gabriel Baquela
    • 1
    Email author
  • Ana Carolina Olivera
    • 2
    • 3
  1. 1.Facultad Regional San NicolásUniversidad Tecnológica NacionalBuenos AiresArgentina
  2. 2.Departamento de Ciencias Exactas y Naturales - Unidad Académica Caleta OliviaSanta CruzArgentina
  3. 3.Universidad Nacional de la Patagonia Austral. CONICETSanta CruzArgentina

Personalised recommendations