Deep Learning Based Traffic Density Prediction: A Case Study for Istanbul

Traffic density prediction is important for users to make better travel decisions, reduce traffic congestion, save time and fuel, and increase traffic processing efficiency. With the development and widespread use of intelligent transportation systems, traffic density prediction has received more and more attention. Traffic density prediction is heavily dependent on historical and real-time traffic data. Large amounts of traffic data are obtained instantly from sources such as sensors, cameras, mobile devices and social media. Increasing traffic data reveals the use of artificial intelligence technologies to solve the traffic management problem. In this study, an LSTM-based prediction model has been developed for traffic density prediction. The developed prediction model has been tested using traffic data of Istanbul with LR, RF, SVM, MLP, CNN and RNN. Experimental results showed that the developed LSTM-based model has more successful results than the compared models. The developed model had approximately 0.9 R2 value in predicting the number of vehicles and average speed.