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Deep learning model for express lane traffic forecasting | ||
| AUT Journal of Mathematics and Computing | ||
| مقاله 1، دوره 3، شماره 2، آذر 2022، صفحه 129-135 اصل مقاله (988.32 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2022.21395.1089 | ||
| نویسندگان | ||
| Farzad Karami1؛ Shahram Bohluli2؛ Chao Huang3؛ Nassim Sohaee* 4 | ||
| 1Amazon Inc., Austin, Texas, USA | ||
| 2Gradient Systematics LLC., Dallas, Texas, USA | ||
| 3Modern Mobility Partners LLC, Atlanta, Georgia, USA | ||
| 4Department of Information Technology and Decision Science, University of North Texas, Denton, Texas, USA | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Traffic forecast؛ Convolutional neural netwrok؛ Toll management | ||
| مراجع | ||
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