SHORT TERM TRAFFIC PREDICTION AT AIRPORT ROAD IN BENIN CITY USING ARTIFICIAL NEURAL NETWORK
Abstract
: Short-term traffic speed prediction is an essential part of proactive traffic control in intelligent transportation systems. This study aimed to predict short-term traffic flow along airport road in Benin city, using artificial neural networks (ANN). Traffic studies were conducted along the road, from Oyaide junction to Irrhirrhi Junction. The time mean speed, flow rate, and flow density were determined for different classes of vehicles. The independent variables used were the different classes of vehicles and their respective time mean speeds and flow rates, while the dependent variable was the flow density on both roads. The ANN model was highly accurate in predicting traffic flow along both roads and had a strong ability to generalize unseen data, as shown by the low value of validation performance in both roads. The high goodness of fit suggests that the model captured a significant portion of the variability in the traffic flow data and provides a highly accurate representation of the pattern from both roads. The best model occurred when the hidden layer was 12 with an epoch of 301 iterations along the airport road.
KEYWORDS: Intelligent transport system, traffic congestion, traffic count, traffic prediction, spot speed
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