Design and evaluation of a novel convolutional neural network for short-term vehicle multi-traffic prediction

Short-term vehicle traffic forecasting is about predicting how traffic indicators are going to be in the near future. The main traffic parameters are: traffic volume, traffic speed, and congestion state. In this thesis, we propose a convolutional neural net-work model that performs traffic forecasting for all three parameters, using historical integrated traffic data over a large area. The proposed model also predicts all three parameters for all 5-minute intervals from the initial time up to one hour into the future. Our proposed method was compared with the state of the art Stacked Long Short-Term Memory (S-LSTM) model, and showed 20% proportionally smaller percentage error and about 2% better recall. Our model also showed comparable results to Google Maps when employed for route travel time estimation, outperforming it in most scenarios. We concluded that our model is better than the current S-LSTM models and also its applications are comparable to established industry equivalents