Abstract
Traffic congestion is a challenge in Gauteng province of South Africa and it has a negative impact on the economy of this province in that services and products are not being rendered on time. Traffic congestion affects the quality of lives of Gauteng residents and visitors alike. Historical vehicle traffic data for the freeway linking Midrand with Florida in Johannesburg was collected from Mikros Traffic Monitoring (MTM), an agency contracted by the Gauteng department of transport. This data was used for constructing the vehicle traffic flow prediction models. In this research, the Bayesian model provides a reliable alternative traffic flow prediction model to other evaluated models such as the Naive Bayes, K-Nearest Neighbor and the Decision tree model. Cross-validation and the root mean square error were used to evaluate the models. The results in this study will benefit both commuters and employers by reducing stress levels and save costs for companies, improve the South African economy as well as assist the Gauteng department of transport in aligning future road traffic strategies.
M.Com. (Information Technology Management)