Evolutionary Computation for Static Traffic Light Cycle Optimisation

Ahmed, E. K. E. and Khalifa, A. M. A. and 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. ISBN 9781538641231

[thumbnail of ICCCEEE18A]
ICCCEEE18A.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (319kB)


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.

Item Type:
Contribution in Book/Report/Proceedings
?? decision support systemsgenetic algorithmsminimisationroad vehiclesstatistical analysistraffic engineering computinghyper-heuristic methodsautomatic intelligent decision support systemsroad networkstraffic signaltraffic phasestatic timingtime lossaverage ??
ID Code:
Deposited By:
Deposited On:
07 Mar 2019 11:50
Last Modified:
11 May 2024 02:47