Journal of Heuristics

, Volume 16, Issue 3, pp 353–372 | Cite as

A metaheuristic approach to the urban transit routing problem

  • Lang FanEmail author
  • Christine L. Mumford


The urban transit routing problem (UTRP) is NP-Hard and involves devising routes for public transport systems. It is a highly complex multi-constrained problem and the evaluation of candidate route sets can prove both time consuming and challenging, with many potential solutions rejected on the grounds of infeasibility. Due to the problem difficulty, metaheuristic algorithms are highly suitable, yet the success of such methods depend heavily on: (1) the quality of the chosen representation, (2) the effectiveness of the initialization procedures and (3) the suitability of the chosen neighbourhood moves. Our paper focuses on these three issues, and presents a framework which can be used as a starting point for solving this problem. We devise a simple model of the UTRP to evaluate candidate route sets. Finally, our approach is validated using simple hill-climbing and simulated annealing algorithms. Our simple method improves upon published results for Mandl’s benchmark problems. In addition, the potential for solving larger problem instances has been explored.


Urban transit routing Hill-climbing Simulated annealing 


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  1. Agrawal, J., Mathew, T.V.: Transit route network design using parallel genetic algorithm. J. Comput. Civil Eng., 248–256 (2004) Google Scholar
  2. Baaj, M.H., Mahmassani, H.: An AI-based approach for transit route system planning and design. J. Adv. Transp. 25(2), 187–210 (1991) CrossRefGoogle Scholar
  3. Baaj, M.H., Mahmassani, H.S.: Hybrid route generation heuristic algorithm for the design of transit networks. Transp. Res. 3(1), 31–50 (1995) CrossRefGoogle Scholar
  4. Balcombe, R.: The demand for public transport: a practical guide. TRL Report, TRL Limited, UK (2004) Google Scholar
  5. Ceder, A., Wilson, H.M.: Bus network design. Transp. Res. B 20B(4), 331–344 (1986) CrossRefGoogle Scholar
  6. Chakroborty, P., Dwivedi, T.: Optimal route network design for transit systems using genetic algorithms. Eng. Optim. 34(1), 83–100 (2002) CrossRefGoogle Scholar
  7. Chakroborty, P.: Genetic algorithms for optimal urban transit network design. Comput. Aided Civil Infrastruct. Eng. 18, 184–200 (2003) CrossRefGoogle Scholar
  8. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959) zbMATHCrossRefMathSciNetGoogle Scholar
  9. Emerson, B.: Design and planning guidelines for public transport infrastructure-bus route planning and transit streets. Public Transp. Auth. (2003) Google Scholar
  10. Fan, L., Mumford, C.: A simplified model of the urban transit routing problem. In: The 7th Metaheuristics International Conference, Montreal, Canada (2007) Google Scholar
  11. Fan, W., Machemehl, R.B.: A tabu search based heuristic method for the transit route network design problem. In: The 9th International Conference on Computer-Aided Scheduling of Public Transport, San Diego, California (2004) Google Scholar
  12. Fan, W., Machemehl, R.B.: Using a simulated annealing algorithm to solve the transit route network design problem. J. Transp. Eng. 122–132 (2006) Google Scholar
  13. Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962) CrossRefGoogle Scholar
  14. Israeli, Y., Ceder, A.: Designing transit routes at the network level. In: IEEE Vehicle Navigation and Information Systems Conference, pp. 310–316 (1989) Google Scholar
  15. Kidwai, F.A.: Optimal design of bus transit network: a genetic algorithm based approach. PhD. dissertation, Indian Institute of Technology, Kanpur, India (1998) Google Scholar
  16. Lampkin, W., Saalmans, P.D.: The design of routes, service frequencies and schedules for a municipal bus undertaking: a case study. OR Quarterly 18, 375–397 (1967) CrossRefGoogle Scholar
  17. Mandl, C.E.: Applied Network Optimization. Academic, London (1979) zbMATHGoogle Scholar
  18. Mandl, C.E.: Evaluation and optimization of urban public transport networks. In: Third Congress on Operations Research, Amsterdam, Netherlands (1979) Google Scholar
  19. Mandl, C.E.: Evaluation and optimization of urban public transport networks. Eur. J. Oper. Res. 5, 396–404 (1980) zbMATHCrossRefMathSciNetGoogle Scholar
  20. Pattnaik, S.B., Mohan, S., Tom, V.M.: Urban bus transit route network design using genetic algorithm. J. Transp. Eng. 368–375 (1998) Google Scholar
  21. Simman, L., Barzily, Z., Passy, U.: Planning the route system for urban buses. Comput. Oper. Res. 1, 201–211 (1974) CrossRefGoogle Scholar
  22. Tom, V.M., Mohan, S.: Transit route network design using frequency coded genetic algorithm. J. Transp. Eng. 186–195 (2003) Google Scholar
  23. White, P.: Public Transport: Its Planning, Management and Operation, 4th edn. Spon Press, 2002 Google Scholar
  24. Zhao, F., Gan, A.: Optimization of transit network to minimize transfers. Final Report, Lehman Center for Transportation Research, Florida International University (2003) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.School of Computer ScienceCardiff UniversityCardiffUK

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