Urban transit assignment model based on augmented network with in-vehicle congestion and transfer congestion

Article

Abstract

This paper presents an augmented network model to represent urban transit system. Through such network model, the urban transit assignment problem can be easily modeled like a generalized traffic network. Simultaneously, the feasible route in such augmented transit network is then defined in accordance with the passengers’ behaviors. The passengers’ travel costs including walking time, waiting time, in-vehicle time and transfer time are formulated while the congestions at stations and the congestions in transit vehicles are all taken into account. On the base of these, an equilibrium model for urban transit assignment problem is presented and an improved shortest path method based algorithm is also proposed to solve it. Finally, a numerical example is provided to illustrate our approach.

Keywords

Transit assignment augmented network congestion transfer algorithm 

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References

  1. [1]
    Cepeda, M., Cominetti, R. & Florian, M. (2006). A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria. Transportation Research, 40(6): 437–459CrossRefGoogle Scholar
  2. [2]
    Cominetti, R. & Correa, J. (2001). Common-lines and passenger assignment in congested transit networks. Transportation Science, 35(3): 250–267MATHCrossRefGoogle Scholar
  3. [3]
    De Cea, J. & Fernández, E. (1993). Transit assignment for congested public transport systems: an equilibrium model. Transportation Science, 27(2): 133–147MATHCrossRefGoogle Scholar
  4. [4]
    Jansson, J.O. (1980). A simple bus line model for optimization of service frequency and bus size. Journal of Transport Economics and Policy, 14(1): 53–80Google Scholar
  5. [5]
    Jansson, J.O. (1979). Marginal cost pricing of scheduled transport services. Journal of Transport Economics and Policy, 13(3): 268–294MathSciNetGoogle Scholar
  6. [6]
    Kurauchi, F., Bell, M.G.H. & Schmöcker, J.D. (2003). Capacity constrained transit assignment with common lines. Journal of Mathematical Modeling and Algorithms, 2(4): 309–327MATHCrossRefGoogle Scholar
  7. [7]
    Lam, W.H.K., Gao, Z.Y., Chan, K.S. & Yang, H. (1999). A stochastic user equilibrium assignment model for congested transit networks. Transportation Research, 33(5): 351–368CrossRefGoogle Scholar
  8. [8]
    Le Clercq, F. (1972). A public transport assignment model. Traffic Engineering and Control, 14: 91–96Google Scholar
  9. [9]
    Lo, H.K., Yip, C.W. & Wan, K.H. (2003). Modeling transfer and non-linear fare structure in multi-modal network. Transportation Research, 37(2): 149–170CrossRefGoogle Scholar
  10. [10]
    Meschini, L., Gentile, G. & Papola, N. (2007). A frequency-based transit model for dynamic traffic assignment to multimodal networks. In: 17th International Symposium on Transportation and Traffic Theory, London, July 2007Google Scholar
  11. [11]
    Mohring, H. (1972). Optimization and scale economics in urban bus transportation. American Economic Review, 62: 591–604Google Scholar
  12. [12]
    Nguyen, S. & Pallotino, S. (1988). Equilibrium traffic assignment in large scale transit networks. European Journal of Operational Research, 37(2): 176–186MATHCrossRefMathSciNetGoogle Scholar
  13. [13]
    Schmenner, R.W. (1976). The demand for urban bus transit. Journal of Transport Economics and Policy, 10(1): 68–86Google Scholar
  14. [14]
    Schmöcker, J.D., Bell, M.G.H. & Kurauchi, F. (2008). A quasi-dynamic capacity constrained frequency-based transit assignment model. Transportation Research, 42(10): 925–945CrossRefGoogle Scholar
  15. [15]
    Sheffi, Y. (1985). Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  16. [16]
    Si, B.F., Zhong, M., Sun, H.J. & Gao, Z.Y. (2009). Augmented network based passenger flow assignment model for urban transit system. Science in China Series E, 52(11): 3158–3167MATHCrossRefGoogle Scholar
  17. [17]
    Spiess, H. & Florian, M. (1989). Optimal strategies: a new assignment model for transit networks. Transportation Research, 23(2): 83–102CrossRefGoogle Scholar
  18. [18]
    Turvey, R. & Mohring H. (1975). Optimal bus fares. Journal of Transport Economics and Policy, 9(3): 280–286Google Scholar
  19. [19]
    Wu, J., Florian, M. & Marcotte, P. (1994). Transit equilibrium assignment: a model and solution algorithms. Transportation Science, 28(3): 193–203MATHCrossRefGoogle Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bingfeng Si
    • 1
  • Ming Zhong
    • 2
  • Xiaobao Yang
    • 1
  • Ziyou Gao
    • 1
  1. 1.MOE Key Laboratory for Urban Transportation Complex Systems Theory and TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Civil EngineeringUniversity of New BrunswickFrederictonCanada

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