2022
Silva, G. O.; Rocha, A. M. A. C.; Witeck, G. R.; Silva, A.; Durães, D.; Machado, J.
On Tuning the Particle Swarm Optimization for Solving the Traffic Light Problem Proceedings Article
Em: O., Misra S. Murgante B. Gervasi (Ed.): pp. 68-80, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 2; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299).
Resumo | Links | BibTeX | Etiquetas: Bridges; Computer software; Particle swarm optimization (PSO); Street traffic control; Traffic signals, Large cities; Particle swarm; Particle swarm optimization; Simulation of urban mobility; Swarm optimization; Traffic flow; Traffic light; Traffic light problem; Urban mobility; Waiting time, Traffic congestion
@inproceedings{Silva202268,
title = {On Tuning the Particle Swarm Optimization for Solving the Traffic Light Problem},
author = {G. O. Silva and A. M. A. C. Rocha and G. R. Witeck and A. Silva and D. Durães and J. Machado},
editor = {Misra S. Murgante B. Gervasi O.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135961179&doi=10.1007%2f978-3-031-10562-3_6&partnerID=40&md5=b2d6908c90ece4348548f01963ab55a9},
doi = {10.1007/978-3-031-10562-3_6},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13378 LNCS},
pages = {68-80},
publisher = {Springer Science and Business Media Deutschland GmbH},
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299},
keywords = {Bridges; Computer software; Particle swarm optimization (PSO); Street traffic control; Traffic signals, Large cities; Particle swarm; Particle swarm optimization; Simulation of urban mobility; Swarm optimization; Traffic flow; Traffic light; Traffic light problem; Urban mobility; Waiting time, Traffic congestion},
pubstate = {published},
tppubtype = {inproceedings}
}
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.