Traffic Density Estimation using Machine Learning Methods

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Aydın S., Taşyürek M., Öztürk C.

Journal of Artificial Intelligence and Data Science (JAIDA), vol.1, no.2, pp.136-143, 2021 (Peer-Reviewed Journal)


In cities where population density is high and transportation systems are widely used, it is necessary to manage traffic systems more effectively not to affect the daily planned works. The Intelligent Transportation System (AUS) is expressed as a system that provides users with better information and safer, more coordinated, and smarter use of transportation networks with different transportation modes and traffic management. One of the most important components of AUS models is the determination of traffic density. The traffic density of intersections is a difficult problem as it affects other interconnected intersections and varies in time. Deep learning method is a widely used method in traffic density estimation in recent years. In this study, the long-term short memory network (LSTM) model, one of the deep learning methods, is proposed to estimate the traffic density of a certain region using open data of Istanbul Metropolitan Municipality. The performance of the proposed LSTM-based model is compared with machine learning methods such as linear regression, decision tree, random forest, and the classical deep learning method (DL). Experimental evaluations show that the proposed LSTM method is more successful in traffic density estimation than the compared methods.