A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting
Abstract Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks....
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78148-1 |
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| author | Semin Kwak Danya Li Nikolas Geroliminis |
| author_facet | Semin Kwak Danya Li Nikolas Geroliminis |
| author_sort | Semin Kwak |
| collection | DOAJ |
| description | Abstract Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions. To address this issue, we propose the Two-level Resolution Neural Network, which enhances interpretability by introducing two resolution blocks. The first block captures large-scale regional traffic patterns, while the second block, using a GCN, focuses on small-scale spatial correlations, informed by the regional predictions. This structure allows the model to intuitively integrate both local and distant traffic data, improving long-term forecasting. In addition to its predictive capabilities, TwoResNet offers enhanced interpretability, particularly in scenarios involving noisy or incomplete data. |
| format | Article |
| id | doaj-art-c670eb2c77c7479c8a1645dc76067b7f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c670eb2c77c7479c8a1645dc76067b7f2025-01-05T12:30:03ZengNature PortfolioScientific Reports2045-23222024-12-0114111710.1038/s41598-024-78148-1A two-level resolution neural network with enhanced interpretability for freeway traffic forecastingSemin Kwak0Danya Li1Nikolas Geroliminis2Department of Electrical and Computer Engineering, University of Southern CaliforniaDepartment of Technology, Management and Economics, Technical University of Denmark (DTU)Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL)Abstract Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions. To address this issue, we propose the Two-level Resolution Neural Network, which enhances interpretability by introducing two resolution blocks. The first block captures large-scale regional traffic patterns, while the second block, using a GCN, focuses on small-scale spatial correlations, informed by the regional predictions. This structure allows the model to intuitively integrate both local and distant traffic data, improving long-term forecasting. In addition to its predictive capabilities, TwoResNet offers enhanced interpretability, particularly in scenarios involving noisy or incomplete data.https://doi.org/10.1038/s41598-024-78148-1Multivariate time-series forecastingFreeway sensor networkTraffic predictionTwo-level resolution neural networkGeometric deep learning |
| spellingShingle | Semin Kwak Danya Li Nikolas Geroliminis A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting Scientific Reports Multivariate time-series forecasting Freeway sensor network Traffic prediction Two-level resolution neural network Geometric deep learning |
| title | A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| title_full | A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| title_fullStr | A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| title_full_unstemmed | A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| title_short | A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| title_sort | two level resolution neural network with enhanced interpretability for freeway traffic forecasting |
| topic | Multivariate time-series forecasting Freeway sensor network Traffic prediction Two-level resolution neural network Geometric deep learning |
| url | https://doi.org/10.1038/s41598-024-78148-1 |
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