deep learning model for express lane traffic forecasting

Traffic forecasting plays a crucial role in the effective operation of managed lanes, as traffic demand and revenue are relatively volatile given parallel competition from adjacent, toll-free general purpose lanes. this paper proposes a deep learning framework to forecast short-term traffic volumes and speeds on managed lanes. a network of convolutional neural networks (cnn) was used to detect spatial features. volume and speed were converted into heatmaps feeding into the cnn layers and temporal relationships were detected by a recurrent neural network (rnn) layer. a dense layer was used for the final prediction. six months of historical volume and speed data on the i-580 express lanes in california, united states were utilized in this case study. computational results confirm the effectiveness of the proposed data-driven deep learning framework in forecasting short-term traffic volumes and speeds on managed lanes.