Dependable Dynamic Routing for Urban Transport Systems Through Integer Linear Programming
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.
- 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). http://www.sciencedirect.com/science/article/pii/0191261580900375 CrossRefMathSciNetGoogle Scholar
- 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.Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: a mathematical programming language. AT & T Bell Laboratories Murray Hill (1987)Google Scholar
- 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.Klein, M.: A primal method for minimal cost flows, with applications to the assignment and transportation problems (1967)Google Scholar
- 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). http://www.sciencedirect.com/science/article/pii/S0191261508000337 CrossRefGoogle Scholar
- 13.Martinelli, D.R., Teng, H.: Optimization of railway operations using neural networks. Transp. Res. Part C Emerg. Technol. 4(1), 33–49 (1996). http://www.sciencedirect.com/science/article/pii/0968090X9500019F CrossRefGoogle Scholar
- 17.Schoitsch, E.: Introduction to the special theme - autonomous vehicles. ERCIM News 2017 (109) (2017)Google Scholar
- 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). http://www.sciencedirect.com/science/article/pii/S0968090X14000655 CrossRefGoogle Scholar
- 19.Wallace, S.W. (ed.): Algorithms and Model Formulations in Mathematical Programming. Springer, New York (1989)Google Scholar
- 20.Yanfeng, L., Ziyou, G., Jun, L.: Vehicle routing problem in dynamic urban traffic network. In: ICSSSM 2011, pp. 1–6 (2011)Google Scholar