Abstract
In everyday routines, there are multiple situations of high traffic congestion, especially in large cities. Traffic light timed regulated intersections are one of the solutions used to improve traffic flow without the need for large-scale and costly infrastructure changes. A specific situation where traffic lights are used is on single-lane roads, often found on roads under maintenance, narrow roads or bridges where it is impossible to have two lanes. In this paper, a simulation-optimization strategy is tested for this scenario. A Particle Swarm Optimization algorithm is used to find the optimal solution to the traffic light timing problem in order to reduce the waiting times for crossing the lane in a simulated vehicle system. To assess vehicle waiting times, a network is implemented using the Simulation of Urban MObility software. The performance of the PSO is analyzed by testing different parameters of the algorithm in solving the optimization problem. The results of the traffic light time optimization show that the proposed methodology is able to obtain a decrease of almost 26% in the average waiting times.
This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abushehab, R.K., Abdalhaq, B.K., Sartawi, B.: Genetic vs. particle swarm optimization techniques for traffic light signals timing. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT), pp. 27–35. IEEE (2014)
Celtek, S.A., Durdu, A., Alı, M.E.M.: Real-time traffic signal control with swarm optimization methods. Measurement 166, 108206 (2020)
Chouikhi, N., Ammar, B., Rokbani, N., Alimi, A.M.: PSO-based analysis of echo state network parameters for time series forecasting. Appl. Soft Comput. 55, 211–225 (2017)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999 (Cat. No. 99TH8406), vol. 3, pp. 1951–1957. IEEE (1999)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000 (Cat. No. 00TH8512), vol. 1, pp. 84–88. IEEE (2000)
Elloumi, W., El Abed, H., Abraham, A., Alimi, A.M.: A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP. Appl. Soft Comput. 25, 234–241 (2014)
Garcia-Nieto, J., Olivera, A.C., Alba, E.: Optimal cycle program of traffic lights with particle swarm optimization. IEEE Trans. Evol. Comput. 17(6), 823–839 (2013)
Gökçe, M.A., Öner, E., Işık, G.: Traffic signal optimization with particle swarm optimization for signalized roundabouts. Simulation 91(5), 456–466 (2015)
Gong, Y., Zhang, J.: Real-time traffic signal control for roundabouts by using a PSO-based fuzzy controller. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, ICNN 1995, vol. 4, pp. 1942–1948. IEEE (1995)
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO-simulation of urban mobility. Int. J. Adv. Syst. Meas. 5(3 &4), 128–138 (2012)
Lopez, P.A., et al.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582. IEEE (2018)
Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011)
Panovski, D., Zaharia, T.: Simulation-based vehicular traffic lights optimization. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 258–265. IEEE (2016)
Peñabaena-Niebles, R., Cantillo, V., Moura, J.L.: The positive impacts of designing transition between traffic signal plans considering social cost. Transp. Policy 87, 67–76 (2020)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69–73. IEEE (1998)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0040810
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999 (Cat. No. 99TH8406), vol. 3, pp. 1945–1950. IEEE (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, G.O., Rocha, A.M.A.C., Witeck, G.R., Silva, A., Durães, D., Machado, J. (2022). On Tuning the Particle Swarm Optimization for Solving the Traffic Light Problem. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-10562-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10561-6
Online ISBN: 978-3-031-10562-3
eBook Packages: Computer ScienceComputer Science (R0)