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The increase in both rural and urban road traffic flow in recent years has led to several disasters in the transportation sector which include traffic congestion, accidents, and high rate of pollution. Alternative traffic control measures are needed whenever there is failure of conventional traffic control or real time traffic issues at road intersection. This current study seeks to investigate the stability and efficiency of Artificial Intelligence (AI) techniques, the artificial neural network (ANN) for eliminating or reducing traffic volume in the case of non-autonomous vehicles in a mixed South African traffic flow conditions. Electronic traffic data of one hundred and twenty six (126) vehicles were observed from Mikros Traffic Monitoring (MTM) firm, a subsidiary of Syntell Group of Company, South Africa. The traffic data was obtained via the traffic technologies employed at MTM which are basically sensor embedded on road surfaces to monitor and control vehicles which passes the traffic counter daily. The dataset obtained from MTM was trained, tested and validated using artificial neural network model under signalized road intersection in heterogeneous condition by using the class description of the vehicles, and corresponding speed as input variables. After series of training, the results suggest that ANN model produced the best possible results for traffic congestion in a heterogeneous traffic condition.
Olayode et al. (Wed,) studied this question.