Using Congestion to Improve Short-Term Velocity Forecasting with Machine Learning Models

The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers' travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations into classes with and without congestion. We find that this classification improves velocity forecasting, showing that using congestion as an attribute reduces the MAE by at least 6.15% for different machine and deep learning models including random forests, multi-layer perceptrons and recurrent neural networks. We propose a random forest model that identifies the future congestion state from past traffic velocity and volume data and then use it to build new short-term velocity forecasting models. These models reduce the MAE prediction error up to 3.37% over the best models that do not consider congestion. This improvement represents overcoming a 53.75% of the error due to not precisely knowing the future congestion state.