Abstract:

As the density of people per kilometer increases in big cities, traffic congestion increases and passengers spend more time in traffic. The extra time and fuel spent due to traffic congestion is a big expense item for both users and countries. It has become a necessity to predict the time-based change in the traffic density of citizens living in metropolises and to plan accordingly. Traffic jams don't usually happen in the whole city at once. Regional traffic jams become widespread as they affect other roads. In this study, it will be tried to predict regional congestions by using historical traffic data with the proposed method using artificial neural networks (ANN). The study increases the success of forecasting modeling by taking environmental factors such as weather conditions apart from many equivalent studies. Using 150,000 data from 75 different points collected from the Istanbul Metropolitan Municipality Open Data Portal, the proposed model was tested and the regional traffic density could be determined with 90% success.

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