Deep learning model for express lane traffic forecasting
| Autor: | Farzad Karami, Shahram Bohluli, Chao Huang, Nassim Sohaee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | AUT Journal of Mathematics and Computing, Vol 3, Iss 2, Pp 129-135 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2783-2449 2783-2287 |
| DOI: | 10.22060/ajmc.2022.21395.1089 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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