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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -