Abstract
In this chapter the Global Optimisation of Signal Settings (GOSS) problem is studied and a meta-heuristic algorithm is proposed for its solution. The GOSS problem arises when the parameters of all (or some) signalised intersections of a network are jointly optimised so as to minimise the value of an objective function (such as total travel time). This problem has been widely studied elsewhere and several algorithms have been proposed, mainly based on descent methods. These algorithms require high computing times for real-scale problems and usually lead to a local optimum since the objective function is hardly ever convex. The high computing times are due to the need to perform traffic assignment to determine the objective function at any iteration. In this chapter we propose a multi-start method based on a Feasible Descent Direction Algorithm (FDDA) for solving this problem. The algorithm is able to search for a local optimal solution and requires lower computing times at any iteration. The proposed algorithm is tested on a real-scale network, also under different demand levels, by adopting different assignment algorithms proposed in the literature. Initial results show that the proposed algorithms perform well and that computing times are compatible with planning purposes also for real-scale networks.
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References
Cascetta, E., Gallo, M., Montella, B.: Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment. Ann. Oper. Res. 144, 301–328 (2006)
D’Acierno, L., Gallo, M., Montella, B.: An ant colony optimisation algorithm for solving the asymmetric traffic assignment problem. Eur. J. Oper. Res. S 217, 459–469 (2012)
Gallo, M., D’Acierno, L.: Comparing algorithms for solving the local optimisation of the signal settings (loss) problem under different supply and demand configurations. Procedia Soc. Behav. Sci. 87, 147–162 (2013)
Marcotte, P.: Network optimization with continuous control parameters. Transport. Sci. 17, 181–197 (1983)
Fisk, C.S.: Game theory and transportation systems modelling. Transport. Res. B-Meth. 18, 301–313 (1984)
Cantarella, G.E., Improta, G., Sforza, A.: Road Network Signal Setting: Equilibrium Conditions. In: Papageorgiou, M. (ed.) Concise encyclopedia of traffic and transportation systems, pp. 366–371. Pergamon Press, Amsterdam (1991)
Cantarella, G.E., Sforza, A.: Network design models and methods for urban traffic management. In: Gartner, N.H., Improta, G. (eds.) Urban traffic networks–dynamic flow modeling and control, pp. 123–153. Springer, Berlin (1995)
Cascetta, E., Gallo, M., Montella, B.: An asymmetric sue model for the combined assignment-control problem. In: Selected proceedings of 8th WCTR, vol. 2, pp. 189–202. Pergamon Press, The Netherlands (1999)
Sheffi, Y., Powell, W.B.: Optimal signal settings over transportation networks. J. Transp. Eng.-ASCE 109, 824–839 (1983)
Heydecker, B.G., Khoo, T.K.: The equilibrium network design problem. In: Proceedings of AIRO ‘90, Conference on Models and Methods for Decision Support, pp. 587–602. Sorrento, Italy (1990)
Yang, H., Yagar, S.: Traffic assignment and signal control in saturated road networks. Transport. Res. A-Pol. 29, 125–139 (1995)
Oda, T., Otokita, T., Tsugui, T., Mashiyama, Y.: Application of simulated annealing to optimization of traffic signal timings. In: Preprints of the 8th IFAC Symposium on Transportation Systems. Chania, Greece (1997)
Wong, S.C., Yang, H.: Reserve capacity of a signal-controlled road network. Transport. Res. B-Meth. 31, 397–402 (1997)
Chiou, S.-W.: Optimization of area traffic control for equilibrium network flows. Transport. Sci. 33, 279–289 (1999)
Ziyou, G., Yifan, S.: A reserve capacity model of optimal signal control with user-equilibrium route choice. Transport. Res. B-Meth. 36, 313–323 (2002)
Heydecker, B.G.: A decomposition approach for signal optimisation in road networks. Transport. Res. B-Meth. 30, 99–114 (1996)
Pillai, R.S., Rathi, A.K., Cohen, S.L.: A restricted branch and bound approach for generating maximum bandwidth signal timing plans for traffic networks. Transport. Res. B-Meth. 32, 517–529 (1998)
Wey, W.-M.: Model formulation and solution algorithm of traffic signal control in an urban network. Comput. Environ. Urban Syst. 24, 355–377 (2000)
Wong, S.C.: Group-based optimisation of signal timings using parallel computing. Transport. Res. C-Emer. 5, 123–139 (1997)
Wong, S.C., Wong, W.T., Leung, C.M., Tong, C.O.: Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control. Transport. Res. B-Meth. 36, 291–312 (2002)
Smith, M.J., Xiang, Y., Yarrow, R.: Bilevel optimisation of signal timings and road prices on urban road networks. In: Preprints of the 8th IFAC Symposium on Transportation Systems, Chania, Greece (1997)
Abu-Lebdeh, G., Benekohal, R.F.: Design and evaluation of dynamic traffic management strategies for congested conditions. Transport. Res. A-Pol. 37, 109–127 (2003)
Saraf, R.K.: Adaptive traffic control using neural networks. PhD Thesis, Vanderbilt University, Nashville (TN), USA (1994)
Spall, J.C., Chin, D.C.: Traffic-responsive signal timing for system-wide traffic control. Transport. Res. C-Emer. 5, 153–163 (1997)
Gallo, M., D’Acierno, L., Montella, B.: A meta-heuristic approach for solving the urban network design problem. Eur. J. Oper. Res. 201, 144–157 (2010)
Gallo, M., Montella, B., D’Acierno, L.: The transit network design problem with elastic demand and internalisation of external costs: an application to rail frequency optimisation. Transport. Res. C-Emer. 19, 1276–1305 (2011)
Powell, W.B., Sheffi, Y.: The convergence of equilibrium algorithms with predetermined step sizes. Transport. Sci. 6, 45–55 (1982)
Sheffi, Y., Powell, W.B.: An algorithm for the traffic assignment problem with random link costs. Networks 12, 191–207 (1982)
Cantarella, G.E.: A general fixed-point approach to multimodal multi-user equilibrium assignment with elastic demand. Transport. Sci. 31, 107–128 (1997)
D’Acierno, L., Montella, B., De Lucia, F.: A stochastic traffic assignment algorithm based on ant colony optimisation. Lect. Notes Comput. Sci. 4150, 25–36 (2006)
Dorigo, M.: Optimization, learning and natural algorithms (in Italian). PhD Thesis, Department of Electronics, Polytechnic of Milan, Italy (1992)
Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)
Cascetta, E.: Transportation Systems Analysis: Models and applications. Springer, New York (2009)
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Partially supported by the Italian MIUR under PRIN2009 grant no. 2009EP3S42_002.
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Gallo, M., D’Acierno, L., Montella, B. (2014). Global Optimisation of Signal Settings: Meta-Heuristic Algorithms for Solving Real-Scale Problems. In: de Sousa, J., Rossi, R. (eds) Computer-based Modelling and Optimization in Transportation. Advances in Intelligent Systems and Computing, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-04630-3_14
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