A Deep Learning Approach for the Intersection Congestion Prediction Problem

Traffic prediction at intersections is an important problem as it serves an essential role in minimizing wait time in large cities while reducing emissions. The problem is challenging, especially with spatial and temporal dependencies between intersections in a large metropolitan city. In this paper, we use a deep learning model to predict traffic congestion based on day, time and weather data. we evaluate our model using datasets from large cities including Atlanta, Philadelphia, Boston and Chicago.