Deep learning model for express lane traffic forecasting

Document Type : Original Article


1 Amazon Inc., Austin, Texas, USA

2 Gradient Systematics LLC., Dallas, Texas, USA

3 Modern Mobility Partners LLC, Atlanta, Georgia, USA

4 Department of Information Technology and Decision Science, University of North Texas, Denton, Texas, USA


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.


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