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A General Multi-agent Modelling Framework for the Transit Assignment Problem – A Learning-Based Approach

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Innovative Internet Community Systems (IICS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3473))

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Abstract

This paper presents the conceptual development of an innovative modelling framework for the transit assignment problem, structured in a multi-agent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and both their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behaviour (i.e. trip choices) according to their knowledge and experience with the transit system performance, and this decision-making process is based on a “mental model” of the transit network conditions. The proposed framework, with different specifications, is capable of representing current practices. The framework, once implemented, can be beneficial in many respects. When connected with urban transportation models – such as ILUTE – the effect of different land use policies, which change passenger demand, on the transit system performance can be evaluated and assessed.

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References

  1. Nielsen, O.A.: A stochastic transit assignment model considering differences in passengers utility functions. Transportation Research Part B 34, 377–402 (2000)

    Article  Google Scholar 

  2. Friedrich, M., Hofsaess, I., Wekeck, S.: Timetable-based transit assignment using branch and bound techniques. Journal of Transportation Research Record 1752, 100–107 (2001)

    Article  Google Scholar 

  3. Dial, R.B.: Transit pathfinder algorithm. Highway Research Record 205, 67–85 (1967)

    Google Scholar 

  4. Le Clercq, F.: A public transport assignment method. Traffic Engineering and Control 14(2), 91–96 (1972)

    Google Scholar 

  5. Chriqui, C.: Reseaux de transport en commun: Les problemes de cheminement et d’acces. Center of Transporation Research: University of Montrea. Publication No. 11 (1974)

    Google Scholar 

  6. Chapleau, R.: Reseaux de transport en commun: Structure informatique et affectationn. Center of Transporation Research: University of Montrea. Publication No. 13 (1974)

    Google Scholar 

  7. Andreasson, I.: A method for the analysis of transit networks. In: Ruebens, M. (ed.) 2nd European Congress on Operations Research, North-Holland, Amsterdam (1976)

    Google Scholar 

  8. Rapp, M.G., Mattenberger, P., Piguet, S., Robert-Grandpierre, A.: Interactive graphics systems for transit route optimization. Journal of Transportation Research Record 559, 73–88 (1976)

    Google Scholar 

  9. Scheele, C.E.: A mathematical programming algorithm for optimal bus frequencies. Institute of Technology, University of Linkoping (1977)

    Google Scholar 

  10. Mandle, C.: Evaluation and optimization of urban public transportation networks. European Journal of Operational Research 5, 396–404 (1980)

    Article  MathSciNet  Google Scholar 

  11. Hasselstrom, D.: Public transportation planning: A mathematical programming approach, PhD thesis, University of Goteburg (1981)

    Google Scholar 

  12. Florian, M.: A traffic equilibrium model of travel by car and public transit modes. Transportation Science 11(2), 166–179 (1977)

    Article  MathSciNet  Google Scholar 

  13. Florian, M., Spiess, H.: On binary mode choice/assignment models. Transportation Science 17(1), 32–47 (1983)

    Article  Google Scholar 

  14. Last, A., Leak, S.E.: Transept: A bus model. Traffic Engineering and Control 17(1), 14–20 (1976)

    Google Scholar 

  15. De Cea, J., Fernandez, E.: Transit Assignment Models. In: Hensher, D.A., Button, K.J. (eds.) Handbook of Transport Modeling, ch. 30, Pergamon, Oxford (2002)

    Google Scholar 

  16. Spiess, H.: On optimal route choice strategies in transit networks. Center of Transport Research, University of Montreal, Publication No. 286 (1983)

    Google Scholar 

  17. Spiess, H., Florian, M.: Optimal strategies: A new assignment model for transit networks. Transportation Research B 23(2), 83–102 (1989)

    Article  Google Scholar 

  18. De Cea, J.: Rutas y estrategias optimas en modelos de asignacion a redes de transporte publico. Presented at: IV Congreso Panamericano de ingenieria de transito y transporte, Santiago (1986)

    Google Scholar 

  19. De Cea, J., Fernandez, E.: Transit assignment to minimal routes: An efficient new algorithm. Traffic Engineering and Control 30(10), 491–494 (1989)

    Google Scholar 

  20. De Cea, J., Fernandez, E.: Transit assignment for congested public transport systems: An equilibrium model. Transportation Science 27, 133–147 (1993)

    Article  MATH  Google Scholar 

  21. Wardrop, J.G.: Some theoretical aspects of road traffic research. In: Proceedings of the Institution of Civil Engineering, Part II, pp. 325–378 (1952)

    Google Scholar 

  22. Lam, W.H.K., Gao, Z.Y., Chan, K.S., Yang, H.: A stochastic user equilibrium assignment model for congested transit networks. Transportation Research Part B 33, 351–368 (1999)

    Article  Google Scholar 

  23. Tong, C.O., Wong, S.C.: A stochastic transit assignment model using a dynamic schedule-based network. Transportation Research Part B 33, 107–121 (1999)

    Article  Google Scholar 

  24. Poon, M.H., Wong, S.C., Tong, C.O.: A dynamic schedule-based model for congested transit networks. Transportation Research Part B 38, 343–368 (2004)

    Article  Google Scholar 

  25. Nuzzolo, A., Russo, F., Crisalli, U.: Transit Network Modelling: The schedulebased approach. Collana Trasnporit, FrancoAngeli s.r.l., Milano, Italy (2003)

    Google Scholar 

  26. Raney, B., Nagel, K.: Truly agent-based strategy selection for transportation simulations. Transportation Research Board Annual Meeting, Washington, D.C., Paper 03-4258 (2003)

    Google Scholar 

  27. Garling, T.: Behavioural assumptions overlooked in travel-choice modelling. In: de Dios Ortuzar, J., Hensher, D., Jara-Diaz, S. (eds.) Travel Behaviour Research: Updating the State of Play, Pergamon, Oxford (1998)

    Google Scholar 

  28. Ettema, D., Tamminga, G., Timmermans, H., Arentze, T.: A Micro-Simulation Model System of Departure Time and Route Choice under Travel Time Uncertainty. In: 10th International Conference on Travel Behaviour Research, Lucerne (August 2003)

    Google Scholar 

  29. McNully, T.M.: Economic theory and human behaviour. The Journal of Value Inquiry 24, 325–333 (1990)

    Article  Google Scholar 

  30. Hickman, M.D., Wilson, N.H.M.: Passenger Travel Time and Path Choice: Implications of Real-Time Transit Information. Transportation Research 3(4), 211–226 (1995)

    Article  Google Scholar 

  31. Huang, R., Peng, Z.R.: An object-oriented GIS data model for transit trip planning systems. Transportation Research Board, the 81st Annual Meeting, Paper 02-3753 (November 2001b)

    Google Scholar 

  32. Salvini, P.: Design and development of the ILUTE operational prototype: a comprehensive microsimulation model of urban systems, Ph.D. Thesis, Graduate Department of Civil Engineering, University of Toronto, Toronto (2003)

    Google Scholar 

  33. Salvini, P., Miller, E.J.: ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Paper presented at the 10th International Conference on Travel Behaviour Research, Lucerne (August 2003)

    Google Scholar 

  34. Huang, R., Peng, Z.R.: An integrated of network data model and routing algorithms for online transit trip planning. In: Transportation Research Board, the 80th Annual Meeting, Washington, D.C., Paper 01-2963 (2001a)

    Google Scholar 

  35. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  36. Miller, E.: Microsimulation. In: Goulias, K.G. (ed.) Transportation System Planning: Methods and Applications, ch. 12, CRC Press, Boca Raton (2003)

    Google Scholar 

  37. Wahba, M., Shalaby, A.: A Multi-Agent Learning-Based Approach to the Transit Assignment Problem: A Prototype. In: Accepted for presentation at the 84th TRB Annual Meeting, January 9-13 (2005), Washington, D.C. and for publication in the Journal of Transportation Research Record (forthcoming)

    Google Scholar 

  38. Wahba, M.: A New Modelling Framework for the Transit Assignment Problem: A Multi-Agent Learning-Based Approach. M.A.Sc. Thesis, Graduate Department of Civil Engineering, University of Toronto, Toronto (2004) (unpublished)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ph.D. student in Transportation Engineering, Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario, Canada, M5S 1A4

    Mohammed Wahba

  2. Assistant Professor, Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario, Canada, M5S 1A4

    Amer Shalaby

Authors
  1. Mohammed Wahba
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  2. Amer Shalaby
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Editor information

Editors and Affiliations

  1. Institut für Mathematik, Technische Universität Ilmenau, PF 100565, 98684, Ilmenau, Germany

    Thomas Böhme

  2. CUCEA, Universidad de Guadalajara, Periférico Norte 799, Edif. L308, 45000, Zapopan, Jalisco, Mexico

    Victor M. Larios Rosillo

  3. Departamento de Sistema de Información, Centro Universitario de Ciencias Economicas y Administrativas (CUCEA), Universidad de Guadalajara (UdeG), Anillo Periférico Nte. No. 799, Núcleo Los Belenes, Zapopan

    Helena Unger

  4. Computer Science Dept., University of Rostock, 18051, Rostock, Germany

    Herwig Unger

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© 2006 Springer-Verlag Berlin Heidelberg

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Wahba, M., Shalaby, A. (2006). A General Multi-agent Modelling Framework for the Transit Assignment Problem – A Learning-Based Approach . In: Böhme, T., Larios Rosillo, V.M., Unger, H., Unger, H. (eds) Innovative Internet Community Systems. IICS 2004. Lecture Notes in Computer Science, vol 3473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553762_26

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  • DOI: https://doi.org/10.1007/11553762_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28880-0

  • Online ISBN: 978-3-540-33995-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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