Dependable Dynamic Routing for Urban Transport Systems Through Integer Linear Programming

  • Davide BasileEmail author
  • Felicita Di Giandomenico
  • Stefania Gnesi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10598)


Highly automated transport systems play an important role in the transformation towards a digital society, and planning the optimal routes for a set of fleet vehicles has been proved useful for improving the delivered services. Traditionally, routes are planned beforehand. However, with the advent of autonomous urban transport systems (e.g. autonomous cars), possible obstructions of tracks due to traffic congestion or bad weather conditions need to be handled on the fly. In this paper we tackle the problem of dynamically computing routes of vehicles in urban lines in the presence of potential obstructions. The problem is formulated as an integer linear optimization problem. The proposed algorithm will assign routes to vehicles dynamically, considering the track segments that are no longer available and the positions of the vehicles in the urban area. The recomputed routes guarantee the minimal waiting time for passengers. Safety of the computed routes is also guaranteed.



This work has been partially supported by the Tuscany Region project POR FESR 2014–2020 SISTER and H2020 2017–2019 S2R-OC-IP2-01-2017 ASTRail.


  1. 1.
    Assad, A.: Analysis of rail classification policies. INFOR: Inf. Syst. Oper. Res. 21(4), 293–314 (1983). doi: 10.1080/03155986.1983.11731905 zbMATHGoogle Scholar
  2. 2.
    Bodin, L.D., Golden, B.L., Schuster, A.D., Romig, W.: A model for the blocking of trains. Transp. Res. Part B: Methodol. 14(1), 115–120 (1980). CrossRefMathSciNetGoogle Scholar
  3. 3.
    Borndörfer, R., Klug, T., Schlechte, T., Fügenschuh, A., Schang, T., Schülldorf, H.: The freight train routing problem for congested railway networks with mixed traffic. Transp. Sci. 50(2), 408–423 (2016)CrossRefGoogle Scholar
  4. 4.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6, 80–91 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Flood, M.M.: The traveling-salesman problem. Oper. Res. 4(1), 61–75 (1956)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Ford, D.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (2010)zbMATHGoogle Scholar
  7. 7.
    Ford, L.R., Fulkerson, D.R.: A simple algorithm for finding maximal network flows and an application to the hitchcock problem. Canadian J. Mathe, 210–218 (1957)Google Scholar
  8. 8.
    Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: a mathematical programming language. AT & T Bell Laboratories Murray Hill (1987)Google Scholar
  9. 9.
    Ghiani, G., Guerriero, F., Laporte, G., Musmanno, R.: Real-time vehicle routing: solution concepts, algorithms and parallel computing strategies. Eur. J. Oper. Res. 151(1), 1–11 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Hemmecke, R., Koppe, M., Lee, J., Weismantel, R.: Nonlinear integer programming. In: Junger, M., Liebling, T.M., Naddef, D., Nemhauser, G.L., Pulleyblank, W.R., Reinelt, G., Rinaldi, G., Wolsey, L.A. (eds.) 50 Years of Integer Programming 1958–2008, pp. 561–618. Springer, Heidelberg (2010)Google Scholar
  11. 11.
    Klein, M.: A primal method for minimal cost flows, with applications to the assignment and transportation problems (1967)Google Scholar
  12. 12.
    Li, F., Gao, Z., Li, K., Yang, L.: Efficient scheduling of railway traffic based on global information of train. Transp. Res. Part B Methodol. 42(10), 1008–1030 (2008). CrossRefGoogle Scholar
  13. 13.
    Martinelli, D.R., Teng, H.: Optimization of railway operations using neural networks. Transp. Res. Part C Emerg. Technol. 4(1), 33–49 (1996). CrossRefGoogle Scholar
  14. 14.
    Mazzanti, F., Ferrari, A., Spagnolo, G.O.: Experiments in formal modelling of a deadlock avoidance algorithm for a CBTC system. In: Margaria, T., Steffen, B. (eds.) ISoLA 2016. LNCS, vol. 9953, pp. 297–314. Springer, Cham (2016). doi: 10.1007/978-3-319-47169-3_22 CrossRefGoogle Scholar
  15. 15.
    Pillac, V., Gendreau, M., Guéret, C., Medaglia, A.L.: A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225(1), 1–11 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Psaraftis, H.N., Wen, M., Kontovas, C.A.: Dynamic vehicle routing problems: three decades and counting. Netw. 67(1), 3–31 (2016)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Schoitsch, E.: Introduction to the special theme - autonomous vehicles. ERCIM News 2017 (109) (2017)Google Scholar
  18. 18.
    Sun, Y., Cao, C., Wu, C.: Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network. Transp. Res. Part C Emerg. Technol. 44, 1–20 (2014). CrossRefGoogle Scholar
  19. 19.
    Wallace, S.W. (ed.): Algorithms and Model Formulations in Mathematical Programming. Springer, New York (1989)Google Scholar
  20. 20.
    Yanfeng, L., Ziyou, G., Jun, L.: Vehicle routing problem in dynamic urban traffic network. In: ICSSSM 2011, pp. 1–6 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Davide Basile
    • 1
    • 2
    Email author
  • Felicita Di Giandomenico
    • 1
  • Stefania Gnesi
    • 1
  1. 1.I.S.T.I “A.Faedo”CNR PisaItaly
  2. 2.Department of Information EngineeringUniversity of FlorenceFlorenceItaly

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