Abstract
M.Com. (Information Technology Management)
Traffic congestion is a major problem in the cities of Gauteng Province (GP). There is high vehicle traffic congestion especially on the Ben Schoeman freeway from Johannesburg to Pretoria during peak travelling times of 06:00 to 09:00 and 15:00 to 18:00. The increasing number of vehicles in the freeways of GP often leads to accidents, which in turn worsen traffic congestion. Traffic impacts negatively on commuters and the businesses around Gauteng Province. Several intervention programmes were implemented by the Gauteng authorities to minimize the rapid increase of traffic volume but this has not solved the traffic congestion problem. The aim of this study was to construct a vehicular traffic prediction model using ensemble learning methods and machine learning algorithms. Vehicle traffic flow data was obtained from Mikro’s Traffic Monitoring (MTM), a company contracted by the Gauteng Department of Transport (DoT) to collect vehicle traffic data. The vehicle traffic flow data for the freeway that links Johannesburg with Pretoria (i.e. M1 North extending to the N1 North) was used in this study. Ensemble learning methods used to construct vehicle traffic prediction models, namely Bagging, Boosting, Stacking and Random Forest together with machine learning algorithms that include Decision Trees, Support Vector Machine and Multi-Layer Perceptron. A cross-validation (CV) method was used for evaluating the models. The best prediction model was selected by computing the cost prediction by using a combination of a loss matrix and a confusion matrix. The results showed that the models constructed using Random Forest ensemble method achieved the best prediction for traffic congestion at 99.991%. Commuters wishing to travel on the Ben Schoeman freeway can predict traffic flow by using an App. The App can allow the commuters to enter variables such as day of week, travel time and traffic volume. The entered variables will predict travel conditions by examining target concepts such as Freeflow, FlowingCongestion and Congested. The commuters will only be able to predict traffic flow although they will not full have knowledge of the actual vehicle traffic volume in the freeway. In that case they will depend on the media (e.g. radio) traffic reports. The implications of the results are the improvement in the competitiveness of Gauteng Province as an investment destination. This model can inform commuters of traffic flow patterns ahead of time and this enables commuters to make appropriate travel arrangements.