Recent advances in the optimization of fixed time traffic signals have demonstrated a move
towards the use of genetic algorithm optimization with traffic network performance evaluated via
stochastic microscopic simulation models. This dissertation examines methods for improved
optimization. Several modified versions of the genetic algorithm and alternative genetic
operators were evaluated on test networks. A traffic simulation model was developed for
assessment purposes. Application of the CHC search algorithm with real crossover and mutation
operators were found to offer improved optimization efficiency over the standard genetic
algorithm with binary genetic operators. Computing resources are best utilized by using a single
replication of the traffic simulation model with common random numbers for fitness evaluations.
Combining the improvements, delay reductions between 13%-32% were obtained over the
standard approaches. A coding scheme allowing for complete optimization of signal phasing is
proposed and a statistical model for comparing genetic algorithm optimization efficiency on
stochastic functions is also introduced. Alternative delay measurements, amendments to genetic
operators and modifications to the CHC algorithm are also suggested.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/4900 |
| Date | 29 May 2008 |
| Creators | Kesur, Khewal Bhupendra |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1678218 bytes, application/pdf, application/pdf |
Page generated in 0.002 seconds