article

Swarm intelligence for traffic light scheduling: Application to real urban areas

Authors:

J. García-Nieto,

A. Carolina OliveraAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 25, Issue 2

Pages 274 - 283

https://doi.org/10.1016/j.engappai.2011.04.011

Published: 01 March 2012 Publication History

Abstract

Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Malaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.

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Cited By

Zhang CLi JChen CLi YZhan ZSilva SPaquete L(2023)Region-based Evaluation Particle Swarm Optimization with Dual Solution Libraries for Real-time Traffic Signal Timing OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590350(111-118)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590350
Wang LGao KLin ZHuang WSuganthan P(2023)Problem feature based meta-heuristics with Q-learning for solving urban traffic light scheduling problemsApplied Soft Computing10.1016/j.asoc.2023.110714147:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110714
Yu S(2022)Enhancement of two-stage flow shop multiprocessor scheduling problems using a target-oriented genetic algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22017443:5(6213-6228)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-220174
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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 25, Issue 2

March, 2012

236 pages

ISSN:0952-1976

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2011.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2012

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Cited By

Zhang CLi JChen CLi YZhan ZSilva SPaquete L(2023)Region-based Evaluation Particle Swarm Optimization with Dual Solution Libraries for Real-time Traffic Signal Timing OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590350(111-118)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590350
Wang LGao KLin ZHuang WSuganthan P(2023)Problem feature based meta-heuristics with Q-learning for solving urban traffic light scheduling problemsApplied Soft Computing10.1016/j.asoc.2023.110714147:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110714
Yu S(2022)Enhancement of two-stage flow shop multiprocessor scheduling problems using a target-oriented genetic algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22017443:5(6213-6228)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-220174
Shaikh PEl-Abd MKhanafer MGao K(2022)A Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control ProblemIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301429623:1(48-63)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TITS.2020.3014296
Wang LGao KLin ZHuang W(2022)Problem Feature-Based Meta-Heuristics with Reinforcement Learning for Solving Urban Traffic Light Scheduling Problems2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC55140.2022.9922317(845-850)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1109/ITSC55140.2022.9922317
Yang SYang B(2022)An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal controlInformation Fusion10.1016/j.inffus.2022.08.00188:C(249-262)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.inffus.2022.08.001
Jovanović AStevanović ADobrota NTeodorović D(2022)Ecology based network traffic controlEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105262115:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.105262
Khan HKushwah KMaurya MSingh SJha PMahobia SSoni SSahu SSadasivuni K(2022)Machine learning driven intelligent and self adaptive system for traffic management in smart citiesComputing10.1007/s00607-021-01038-1104:5(1203-1217)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00607-021-01038-1
Yang XWang LZhang ZHan XYue L(2022)A Backbone Whale Optimization Algorithm Based on Cross-stage EvolutionAdvances in Swarm Intelligence10.1007/978-3-031-09677-8_8(95-104)Online publication date: 15-Jul-2022
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Luo YTsai C(2021)Multi-Agent Reinforcement Learning based on Two-Step Neighborhood Experience for Traffic Light ControlProceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications10.1145/3491396.3506544(28-33)Online publication date: 28-Dec-2021
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