An Improved Multi-objective Algorithm for the Urban Transit Routing Problem

  • Matthew P. John
  • Christine L. Mumford
  • Rhyd Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8600)

Abstract

The determination of efficient routes and schedules in public transport systems is complex due to the vast search space and multiple constraints involved. In this paper we focus on the Urban Transit Routing Problem concerned with the physical network design of public transport systems. Historically, route planners have used their local knowledge coupled with simple guidelines to produce network designs. Several major studies have identified the need for automated tools to aid in the design and evaluation of public transport networks. We propose a new construction heuristic used to seed a multi-objective evolutionary algorithm. Several problem specific mutation operators are then combined with an NSGAII framework leading to improvements upon previously published results.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ceder, A., Wilson, N.H.M.: Bus network design. Transportation Research Part B 20(4), 331–344 (1986)CrossRefGoogle Scholar
  2. 2.
    Magnanti, T.L., Wong, R.T.: Network design and transportation planning: Models and algorithms. Transportation Science 18(1), 1–55 (1984)CrossRefGoogle Scholar
  3. 3.
    Mumford, C.L.: New heuristic and evolutionary operators for the multi-objective urban transit routing problem. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 939–946 (2013)Google Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Nielsen, G., Nelson, J.D., Mulley, C., Tegner, G., Lind, G., Lange, T.: Public transport–planning the networks. HiTrans Best Practice Guide (2005)Google Scholar
  6. 6.
    Zhao, F., Gan, A.: Optimization of transit network to minimize transfers (2003)Google Scholar
  7. 7.
    Bagloee, S.A., Ceder, A.A.: Transit-network design methodology for actual-size road networks. Transportation Research Part B 45(10), 1787–1804 (2011)CrossRefGoogle Scholar
  8. 8.
    Lampkin, W., Saalmans, P.D.: The design of routes, service frequencies, and schedules for a municipal bus undertaking: A case study. In: OR, pp. 375–397 (1967)Google Scholar
  9. 9.
    Silman, L.A., Barzily, Z., Passy, U.: Planning the route system for urban buses. Computers & Operations Research 1(2), 201–211 (1974)CrossRefGoogle Scholar
  10. 10.
    Mandl, C.E.: Applied network optimization. Academic Pr. (1979)Google Scholar
  11. 11.
    Mandl, C.E.: Evaluation and optimization of urban public transportation networks. European Journal of Operational Research 5(6), 396–404 (1980)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Baaj, M.H., Mahmassani, H.S.: Hybrid route generation heuristic algorithm for the design of transit networks. Transportation Research Part C 3(1), 31–50 (1995)CrossRefGoogle Scholar
  13. 13.
    Agrawal, J., Mathew, T.V.: Transit route network design using parallel genetic algorithm. Journal of Computing in Civil Engineering 18(3), 248–256 (2004)CrossRefGoogle Scholar
  14. 14.
    Chakroborty, P., Dwivedi, T.: Optimal route network design for transit systems using genetic algorithms. Engineering Optimization 34(1), 83–100 (2002)CrossRefGoogle Scholar
  15. 15.
    Pattnaik, S.B., Mohan, S., Tom, V.M.: Urban bus transit route network design using genetic algorithm. Journal of Transportation Engineering 124(4), 368–375 (1998)CrossRefGoogle Scholar
  16. 16.
    Tom, V.M., Mohan, S.: Transit route network design using frequency coded genetic algorithm. Journal of Transportation Engineering 129(2), 186–195 (2003)CrossRefGoogle Scholar
  17. 17.
    Fan, W., Machemehl, R.B.: A tabu search based heuristic method for the transit route network design problem. Computer-aided Systems in Public Transport, 387–408 (2008)Google Scholar
  18. 18.
    Fan, W., Machemehl, R.B.: Using a simulated annealing algorithm to solve the transit route network design problem. Journal of Transportation Engineering 132(2), 122–132 (2006)CrossRefGoogle Scholar
  19. 19.
    Fan, W., Machemehl, R.B.: Optimal transit route network design problem with variable transit demand: genetic algorithm approach. Journal of Transportation Engineering 132(1), 40–51 (2006)CrossRefGoogle Scholar
  20. 20.
    Fan, L., Mumford, C.L., Evans, D.: A simple multi-objective optimization algorithm for the urban transit routing problem. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1–7 (2009)Google Scholar
  21. 21.
    Shih, M.C., Mahmassani, H.S.: A design methodology for bus transit networks with coordinated operations. Technical Report SWUTC/94/60016-1 (1994)Google Scholar
  22. 22.
    Yen, J.Y.: Finding the k shortest loopless paths in a network. Management Science 17(11), 712–716 (1971)CrossRefMATHGoogle Scholar
  23. 23.
    Croes, G.A.: A method for solving traveling-salesman problems. Operations Research 6(6), 791–812 (1958)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Matthew P. John
    • 1
    • 2
  • Christine L. Mumford
    • 1
  • Rhyd Lewis
    • 2
  1. 1.Cardiff School of Computer Science & InformaticsUK
  2. 2.Cardiff School of MathematicsUK

Personalised recommendations