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Title
Abstract
Introduction
Literature Review
Experimental Evaluation
Conclusion
References
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Computer Science
Artificial Intelligence

Transit network design by Bee Colony Optimization

Profile image of Milos NikolicMilos Nikolic

2013, Expert Systems with Applications

https://doi.org/10.1016/J.ESWA.2013.05.002
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11 pages

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Abstract

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate highquality solutions.

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References (54)

  1. Baaj, M. H., & Mahmassani, H. (1991). An AI-based approach for transit route system planning and design. Journal of Advanced Transportation, 25(2), 187-210.
  2. Baaj, M. H., & Mahmassani, H. S. (1995). Hybrid route generation heuristic algorithm for the design of transit networks. Transportation Research C, 3(1), 31-50.
  3. Bagloee, S. A., & Ceder, A. (2011). Transit-network design methodology for actual- size road networks. Transportation Research B, 45, 1787-1804.
  4. Beni, G. (1988). The concept of cellular robotic system. In Proceedings of the 1988 IEEE international symposium on intelligent control (pp. 57-62). Los Alamitos, CA: IEEE Computer Society Press.
  5. Beni, G., & Hackwood, S. (1992). Stationary waves in cyclic swarms. In Proceedings of the 1992 international symposium on intelligent control (pp. 234-242). Los Alamitos, CA: IEEE Computer Society Press.
  6. Beni, G., & Wang, J. (1989). Swarm intelligence. In Proceedings of the seventh annual meeting of the robotics society of Japan (pp. 425-428). Tokyo: RSJ Press.
  7. Bielli, M., Caramia, M., & Carotenuto, P. (2002). Genetic algorithms in bus network optimization. Transportation Research Part C: Emerging Technologies, 10(1), 19-34.
  8. Blum, J. J., & Mathew, T. V. (2012). Implications of the computational complexity of transit route network redesign for metaheuristic optimisation systems. IET Intelligent Transport Systems, 6, 124-131.
  9. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1997). Swarm intelligence. Oxford University Press.
  10. Byrne, B. F. (1975). Public transportation line positions and headways for minimum user and system cost in a radial case. Transportation Research, 9, 97-102.
  11. Ceder, A., & Israeli, Y. (1998). User and operator perspectives in transit network design. Transportation Research Record, 1623, 3-7.
  12. Ceder, A., & Wilson, H. M. (1986). Bus network design. Transportation Research, 20B(4), 331-334.
  13. Chakroborty, P. (2003). Genetic algorithms for optimal urban transit network design. Computer-Aided Civil and Infrastructure Engineering, 18, 184-200.
  14. Charkroborty, P., & Dwivedi, T. (2002). Optimal route network design for transit systems using genetic algorithms. Engineering Optimization, 34(1), 83-100.
  15. Davidovic ´, T., Šelmic ´, M., & Teodorovic ´, D. (2009). Scheduling independent tasks: Bee colony optimization approach. In Proceedings of the 17th Mediterranean conference on control and automation, MED'09, Thessaloniki, Greece, June 24-26 (pp. 1020-1025).
  16. Davidovic ´, T., Ramljak, D., Šelmic ´, M., & Teodorovic ´, D. (2011). Bee colony optimization for the p-center problem. Computers & Operations Research, 38, 1367-1376.
  17. Davidovic ´, T., Šelmic ´, M., Teodorovic ´, D., & Ramljak, D. (2012). Bee colony optimization for scheduling independent tasks to identical processors. Journal of Heuristics, 18(4), 549-569.
  18. Derrible, S., & Kenneday, C. (2011). Applications of graph theory and network science to transit network design. Transport reviews, 31, 495-519.
  19. Desaulniers, G., & Hickman, M. D. (2007). Public transit. In C. Barnhart, & G. Laporte (Eds.), Vol. 14. Handbook in OR & MS (pp. 69-127).
  20. Dimitrijevic ´, B., Teodorovic ´, D., Simic ´, V., & Šelmic ´, M. (2011). A bee colony optimization approach to solving the anti-covering location problem. Journal of Computing in Civil Engineering, 26(6), 759-768.
  21. Fan, W., & Machemehl, R. B. (2006). Optimal transit route network design problem with variable transit demand: Genetic algorithm approach. Journal of Transportation Engineering -ASCE, 132, 40-51.
  22. Fan, W., & Machemehl, R. B. (2008). Tabu search strategies for the public transportation network optimizations with variable transit demand. Computer Aided Civil and Infrastructure Engineering, 23, 502-520.
  23. Fan, L., & Mumford, C. L. (2010). A metaheuristic approach to the urban transit routing problem. Journal of Heuristics, 16, 353-372.
  24. Guan, J. F., Yang, H., & Wirasinghe, S. C. (2003). Simultaneous optimization of transit line configuration and passenger line assignment. Transportation Research Part B, 40(10), 885-902.
  25. Guihaire, V., & Hao, J. K. (2008). Transit network design and scheduling: A global review. Transportation Research A, 42, 1251-1273. <http://people.brunel.ac.uk/$mastjjb/jeb/orlib/utraninfo.html>.
  26. Kepaptsoglou, K., & Karlaftis, M. (2009). Transit route network design problem: Review. Journal of Transportation Engineering -ASCE, 135(8), 491-505.
  27. Kidwai, F. A. (1998). Optimal design of bus transit network: A genetic algorithm based approach. Phd. dissertation, Indian Institute of Technology, Kanpur, India.
  28. Lampkin & Saalmans (1967). The design of routes service frequencies and schedules for municipal bus undertaking: A case studys. Operational Research Quarterly, 18(4), 375-397.
  29. Lee, Y. J., & Vuchic, V. R. (2005). Transit network design with variable demand. Journal of Transportation Engineering -ASCE, 131, 1-10.
  30. Luc ˇic ´, P., & Teodorovic ´, D. (2001). Bee system: Modeling combinatorial optimization transportation engineering problems by swarm intelligence. In Preprints of the TRISTAN IV triennial symposium on transportation analysis, Sao Miguel, Azores Islands, Portugal (pp. 441-445).
  31. Luc ˇic ´, P., & Teodorovic ´, D. (2002). Transportation modeling: An artificial life approach. In Proceedings of the 14th IEEE ''international conference on tools with artificial intelligence'', Washington, DC (pp. 216-223).
  32. Luc ˇic ´, P., & Teodorovic ´, D. (2003a). Computing with bees: attacking complex transportation engineering problems. International Journal on Artificial Intelligence Tools, 12, 375-394.
  33. Luc ˇic ´, P., & Teodorovic ´, D. (2003b). Vehicle routing problem with uncertain demand at nodes: The bee system and fuzzy logic approach. In J. L. Verdegay (Ed.), Fuzzy sets in optimization (pp. 67-82). Heidelberg, Berlin: Springer-Verlag.
  34. Magnanti, T. L., & Wong, R. T. (1984). Network design and transportation planning: Models and algorithms. Transportation Science, 18(1), 1-55.
  35. Mandl, C. E. (1979). Evaluation and optimization of urban public transportation network. European Journal of Operational Research, 5, 396-404.
  36. Mauttone, A., & Urquhart, M. E. (2009). A route set construction algorithm for the transit network design problem. Computers & Operations Research, 36, 2440-2449.
  37. Miandoabchi, E., Farahani, R. Z., Dullaert, W., & Szeto, W. Y. (2012). Hybrid evolutionary metaheuristics for concurrent multi-objective design of urban road and public transit networks. Networks & Spatial Economics, 12, 441-480.
  38. Newell, G. (1979). Some issues relating to the optimal design of bus routes. Transportation Science, 13, 20-35.
  39. Nikolic ´, M., & Teodorovic ´, D. (2013). Empirical study of the bee colony optimization (BCO) algorithm. Expert Systems with Applications, 40(11), 4609-4620.
  40. Pacheco, J., Alvarez, A., Casado, S., & Gonzalez-Velarde, J. L. (2009). A tabu search approach to an urban transport problem in northern Spain. Computers & Operations Research, 36, 967-979.
  41. Pattnaik, S., Mohan, S., & Tom, V. (1998). Urban bus transit route network design using genetic algorithm. Journal of Transportation Engineering, 124(4), 368-375.
  42. Schoebel, A. (2012). Line planning in public transportation: Models and methods. OR SPECTRUM, 34, 491-510.
  43. Šelmic ´, M., Teodorovic ´, D., & Vukadinovic ´, K. (2010). Locating inspection facilities in traffic networks: An artificial intelligence approach. Transportation Planning and Technology, 33, 481-493.
  44. Silman, L. A., Barzily, Z., & Passy, U. (1974). Planning the route system for urban buses. Computers & Operations Research, 1, 201-211.
  45. Szeto, W. V., & Wu, Y. (2011). A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong. European Journal of Operational Research, 209, 141-155.
  46. Teodorovic ´, D. (2003). Transport modeling by multi-agent systems: A swarm intelligence approach. Transportation Planning and Technology, 26, 289-312.
  47. Teodorovic ´, D. (2008). Swarm intelligence systems for transportation engineering: Principles and applications. Transportation Research Part C: Emerging Technologies, 16, 651-782.
  48. Teodorovic ´, D. (2009). Bee colony optimization (BCO). In C. P. Lim, L. C. Jain, & S. Dehuri (Eds.), Innovations in swarm intelligence (pp. 39-60). Berlin, Heidelberg: Springer-Verlag.
  49. Teodorovic ´, D., & Dell'Orco, M. (2005). Bee colony optimization -a cooperative learning approach to complex transportation problems. In Advanced OR and AI methods in transportation. Proceedings of the 10th meeting of the EURO working group on transportation, Poznan, Poland (pp. 51-60).
  50. Teodorovic ´, D., & Dell'Orco, M. (2008). Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31, 135-152.
  51. Teodorovic ´, D., Šelmic ´, M., & Mijatovic ´-Teodorovic ´, Lj. (2012). Combining case- based reasoning with bee colony optimization for dose planning in well differentiated thyroid cancer treatment. Expert Systems with Applications, 40(6), 2147-2155.
  52. Todorovic ´, N., & Petrovic ´, S. (2013). Bee colony optimization for nurse rostering. IEEE Transactions on Systems Man & Cybernetics: Systems, 43(2), 467-473.
  53. Zhao, F., & Zeng, X. (2006). Simulated annealing-genetic algorithm for transit network optimization. Journal of Computing in Civil Engineering, 20(1), 57-68.
  54. Zhao, F., & Zeng, X. G. (2007). Optimization of transit route network, vehicle headways and timetables for large-scale transit networks. European Journal of Operational Research, 186, 841-855.

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