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Evolutionary Computation for Static Traffic Light Cycle Optimisation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

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Evolutionary Computation for Static Traffic Light Cycle Optimisation. / Ahmed, E. K. E.; Khalifa, A. M. A.; Kheiri, A.

2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. p. 1-6.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ahmed, EKE, Khalifa, AMA & Kheiri, A 2018, Evolutionary Computation for Static Traffic Light Cycle Optimisation. in 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, pp. 1-6. https://doi.org/10.1109/ICCCEEE.2018.8515802

APA

Ahmed, E. K. E., Khalifa, A. M. A., & Kheiri, A. (2018). Evolutionary Computation for Static Traffic Light Cycle Optimisation. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCCEEE.2018.8515802

Vancouver

Ahmed EKE, Khalifa AMA, Kheiri A. Evolutionary Computation for Static Traffic Light Cycle Optimisation. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE. 2018. p. 1-6 https://doi.org/10.1109/ICCCEEE.2018.8515802

Author

Ahmed, E. K. E. ; Khalifa, A. M. A. ; Kheiri, A. / Evolutionary Computation for Static Traffic Light Cycle Optimisation. 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. pp. 1-6

Bibtex

@inproceedings{b06fb2d2a427436f8d6dc89c13a390ce,
title = "Evolutionary Computation for Static Traffic Light Cycle Optimisation",
abstract = "Cities have become congested with traffic and changes to road network infrastructure are usually not possible. Thus, researchers and practitioners are investigating the practice of traffic light signal optimisation methodologies upon already established road networks to improve the flow of vehicles through the cities. The flow of traffic can be described by multiple factors such as mean journey time, mean waiting time, average vehicle velocity, and time loss. Static timing means that each traffic phase is active for a pre-fixed duration during the cycle. We aim to optimise traffic signal timing plans to minimise the mean journey time, which is increased by improper signalling, for vehicles during their journey across the junctions. In this research, we propose and empirically analyse several automatic intelligent decision support systems including genetic algorithms and selection hyper-heuristic methods for the optimisation of traffic light signalling problem. The empirical results indicate the success of the proposed algorithm techniques.",
keywords = "decision support systems, genetic algorithms, minimisation, road vehicles, statistical analysis, traffic engineering computing, hyper-heuristic methods, automatic intelligent decision support systems, road networks, traffic signal, traffic phase, static timing, time loss, average vehicle velocity, mean journey time, traffic light signal optimisation methodologies, road network infrastructure, static traffic light cycle optimisation, evolutionary computation, traffic light signalling problem, Genetic algorithms, Timing, Roads, Heuristic algorithms, Simulated annealing, Biological cells, Transportation, Traffic Optimisation, Genetic Algorithms, Hyper-heuristics",
author = "Ahmed, {E. K. E.} and Khalifa, {A. M. A.} and A. Kheiri",
year = "2018",
month = nov,
day = "1",
doi = "10.1109/ICCCEEE.2018.8515802",
language = "English",
pages = "1--6",
booktitle = "2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolutionary Computation for Static Traffic Light Cycle Optimisation

AU - Ahmed, E. K. E.

AU - Khalifa, A. M. A.

AU - Kheiri, A.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Cities have become congested with traffic and changes to road network infrastructure are usually not possible. Thus, researchers and practitioners are investigating the practice of traffic light signal optimisation methodologies upon already established road networks to improve the flow of vehicles through the cities. The flow of traffic can be described by multiple factors such as mean journey time, mean waiting time, average vehicle velocity, and time loss. Static timing means that each traffic phase is active for a pre-fixed duration during the cycle. We aim to optimise traffic signal timing plans to minimise the mean journey time, which is increased by improper signalling, for vehicles during their journey across the junctions. In this research, we propose and empirically analyse several automatic intelligent decision support systems including genetic algorithms and selection hyper-heuristic methods for the optimisation of traffic light signalling problem. The empirical results indicate the success of the proposed algorithm techniques.

AB - Cities have become congested with traffic and changes to road network infrastructure are usually not possible. Thus, researchers and practitioners are investigating the practice of traffic light signal optimisation methodologies upon already established road networks to improve the flow of vehicles through the cities. The flow of traffic can be described by multiple factors such as mean journey time, mean waiting time, average vehicle velocity, and time loss. Static timing means that each traffic phase is active for a pre-fixed duration during the cycle. We aim to optimise traffic signal timing plans to minimise the mean journey time, which is increased by improper signalling, for vehicles during their journey across the junctions. In this research, we propose and empirically analyse several automatic intelligent decision support systems including genetic algorithms and selection hyper-heuristic methods for the optimisation of traffic light signalling problem. The empirical results indicate the success of the proposed algorithm techniques.

KW - decision support systems

KW - genetic algorithms

KW - minimisation

KW - road vehicles

KW - statistical analysis

KW - traffic engineering computing

KW - hyper-heuristic methods

KW - automatic intelligent decision support systems

KW - road networks

KW - traffic signal

KW - traffic phase

KW - static timing

KW - time loss

KW - average vehicle velocity

KW - mean journey time

KW - traffic light signal optimisation methodologies

KW - road network infrastructure

KW - static traffic light cycle optimisation

KW - evolutionary computation

KW - traffic light signalling problem

KW - Genetic algorithms

KW - Timing

KW - Roads

KW - Heuristic algorithms

KW - Simulated annealing

KW - Biological cells

KW - Transportation

KW - Traffic Optimisation

KW - Genetic Algorithms

KW - Hyper-heuristics

U2 - 10.1109/ICCCEEE.2018.8515802

DO - 10.1109/ICCCEEE.2018.8515802

M3 - Conference contribution/Paper

SP - 1

EP - 6

BT - 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)

PB - IEEE

ER -