Skip to main content

Transit Network Frequencies-Setting Problem Solved Using a New Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation

  • Conference paper
  • First Online:
Advances in Soft Computing (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10062))

Included in the following conference series:

Abstract

The rise of Bus Rapid Transit Systems (BRTS) in urban centers involves complex problems of design and scheduling including the scheduling of route intervals across the bus network. The difficulty stems from the fact that transport systems keep to established routes and must set frequencies for each route to minimize costs (measured in terms of transport capacity wasted) and maximize the quality of service (minimizing the total time of users in the system). All this depends on the maximum number of buses available in the system. In an effort to find an alternative solution to the Transit Network Frequencies Setting Problem (TNFSP) on BRTS, this paper proposes using Multi-Objective Global Best Harmony Search (MOGBHS), a multi-objective heuristic algorithm based on three main components: (1) Global-Best Harmony Search, as a heuristic optimization strategy, (2) Non-Dominated Sorting, as a multi-objective optimization strategy, and (3) Discrete Event Simulation, for obtaining quality measures in the solutions found. To test the proposed approach, a simulation model was implemented for Megabus, a BRTS located in Pereira (Colombia), for which the frequency of the buses assigned to routes previously defined in the system was optimized so that operating costs were reduced to a minimum, while user satisfaction was maximized. The MOGBHS algorithm was compared with NSGA-II. It was concluded that MOGBHS outperformed NSGA-II in the number of optimal solutions found (Pareto front points), from 175% in 3,000 fitness function evaluations to 488% in 27,000 evaluations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cervero, R., Bus Rapid Transit (BRT): An efficient and competitive mode of public transport. IURD Working Paper 2013–01 (2013)

    Google Scholar 

  2. Farahani, R.Z., et al.: A review of Urban transportation network design problems. Eur. J. Oper. Res. 229(2), 281–302 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  3. Luke, S.: Essentials of Metahuristics. Lulu, Raleigh (2010)

    Google Scholar 

  4. Mazloumi, E., et al.: Efficient transit schedule design of timing points: a comparison of ant colony and genetic algorithms. Transp. Res. Part B: Methodol. 46(1), 217–234 (2012)

    Article  Google Scholar 

  5. Sivasubramani, S., Swarup, K.: Environmental/economic dispatch using multi-objective harmony search algorithm. Electr. Power Syst. Res. 81(9), 1778–1785 (2011)

    Article  Google Scholar 

  6. Omran, M.G., Mahdavi, M.: Global-best harmony search. Appl. Math. Comput. 198(2), 643–656 (2008)

    MathSciNet  MATH  Google Scholar 

  7. Automation, R.: Arena simulation software, vol. 24, Accessed Nov 2013

    Google Scholar 

  8. Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Trans. 6(2), 17 (2002)

    Article  Google Scholar 

  9. Ibarra-Rojas, O.J., et al.: Planning, operation, and control of bus transport systems: a literature review. Transp. Rese. Part B: Methodol. 77, 38–75 (2015)

    Article  Google Scholar 

  10. Kechagiopoulos, P.N., Beligiannis, G.N.: Solving the Urban transit routing problem using a particle swarm optimization based algorithm. Appl. Soft Comput. 21, 654–676 (2014)

    Article  Google Scholar 

  11. Nikolić, M., Teodorović, D.: Transit network design by bee colony optimization. Expert Syst. Appl. 40(15), 5945–5955 (2013)

    Article  Google Scholar 

  12. Yu, B., et al.: Transit route network design-maximizing direct and transfer demand density. Transp. Res. Part C: Emerg. Technol. 22, 58–75 (2012)

    Article  Google Scholar 

  13. Mauttone, A., Urquhart, M.E.: A route set construction algorithm for the transit network design problem. Comput. Oper. Res. 36(8), 2440–2449 (2009)

    Article  MATH  Google Scholar 

  14. Beltran, B., et al.: Transit network design with allocation of green vehicles: a genetic algorithm approach. Transp. Res. Part C: Emerg. Technol. 17(5), 475–483 (2009)

    Article  Google Scholar 

  15. Nayeem, M.A., Rahman, M.K., Rahman, M.S.: Transit network design by genetic algorithm with elitism. Transp. Res. Part C: Emerg. Technol. 46, 30–45 (2014)

    Article  Google Scholar 

  16. Szeto, W.Y., Jiang, Y.: Transit route and frequency design: bi-level modeling and hybrid artificial bee colony algorithm approach. Transp. Res. Part B: Methodol. 67, 235–263 (2014)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Mauttone, A., Urquhart, M.: A multi-objective metaheuristic approach for the transit network design problem. Publ. Transport 1(4), 253–273 (2009)

    Article  MATH  Google Scholar 

  20. Wang, J., Sun, G., Hu, X.: Optimization of transit operation strategies: a case study of Guangzhou, China Annual Meeting of the Transportation Research Board (2013)

    Google Scholar 

  21. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36–38), 3902–3933 (2005)

    Article  MATH  Google Scholar 

  22. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

  23. Cobos, C., Estupiñán, D., Pérez, J.: GHS + LEM: Global-best Harmony Search using learnable evolution models. Appl. Math. Comput. 218(6), 2558–2578 (2011)

    MathSciNet  MATH  Google Scholar 

  24. El-Abd, M.: An improved global-best harmony search algorithm. Appl. Math. Comput. 222, 94–106 (2013)

    MATH  Google Scholar 

  25. Kumar, V., Chhabra, J.K., Kumar, D.: Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. J. Comput. Sci. 5(2), 144–155 (2013)

    Article  MathSciNet  Google Scholar 

  26. Khalili, M., et al.: Global dynamic harmony search algorithm: GDHS. Appl. Math. Comput. 228, 195–219 (2014)

    MathSciNet  MATH  Google Scholar 

  27. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. TIK-report, vol. 103 (2001)

    Google Scholar 

  28. Glover, F.: Tabu search—Part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MATH  Google Scholar 

  29. Glover, F.: Tabu search—Part II. ORSA J. Comput. 2(1), 4–32 (1990)

    Article  MATH  Google Scholar 

  30. Torres-Jimenez, J., Izquierdo-Marquez, I.: Survey of covering arrays. In: 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Cobos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ruano, E., Cobos, C., Torres-Jimenez, J. (2017). Transit Network Frequencies-Setting Problem Solved Using a New Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62428-0_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62427-3

  • Online ISBN: 978-3-319-62428-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics