Image Processing, Deep Learning, ARIMA Today, with the rapid advancement of technology, artificial intelligence has become an indispensable part of ourlives. Artificial intelligence can be described as a method of predicting computer or computer-controlled activities like human or intelligent creatures. Artificial intelligence are frequently used in many application areas such as health, education, security and robotics. One of the important uses of artificial intelligence is traffic signaling systems used for the controlled and safe passage of vehicles. Traffic signaling generally works on fixed time basis at intersections with heavy traffic, regardless of the traffic density. Thus, fixed time traffic signaling systems are not preferred much today. The waiting time in smart-traffic signaling systems depends on the number of vehicles and the transit time of the vehicles. The study, video footage of the vehicles at the Otogar intersection, which is one of the intersections with heavy traffic, was taken from the Directorate of Transport and Traffic Services in Isparta. Image processing and ARIMA deep-learning method were applied on the captured images. With the ARIMA deep-learning method, the number of time-dependent vehicles and the R 2 of vehicle transit times were evaluated according to the performance evaluation criteria and accuracy rate of 82% and 89% was obtained.
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