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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.06440 |
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| _version_ | 1866915787780915200 |
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| author | Sutter, Carly Sulia, Kara J. Bassill, Nick P. Wirz, Christopher D. Thorncroft, Christopher D. Rothenberger, Jay C. Przybylo, Vanessa Cains, Mariana G. Radford, Jacob Evans, David Aaron |
| author_facet | Sutter, Carly Sulia, Kara J. Bassill, Nick P. Wirz, Christopher D. Thorncroft, Christopher D. Rothenberger, Jay C. Przybylo, Vanessa Cains, Mariana G. Radford, Jacob Evans, David Aaron |
| contents | Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine learning models are developed to provide additional support for the New York State Department of Transportation (NYSDOT) by automatically classifying current road conditions across the state. Convolutional neural networks and random forests are trained on NYSDOT roadside camera images and weather data to predict road surface conditions. This task draws critically on a robust hand-labeled dataset of ~22,000 camera images containing six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, including integration of operational datasets and use of representative and realistic images. The weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras. With operational deployment, this model has the potential to improve spatial and temporal awareness of road surface conditions, which can strengthen decision-making for operations, roadway maintenance, and traveler safety, particularly during winter weather events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data Sutter, Carly Sulia, Kara J. Bassill, Nick P. Wirz, Christopher D. Thorncroft, Christopher D. Rothenberger, Jay C. Przybylo, Vanessa Cains, Mariana G. Radford, Jacob Evans, David Aaron Computer Vision and Pattern Recognition Machine Learning Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine learning models are developed to provide additional support for the New York State Department of Transportation (NYSDOT) by automatically classifying current road conditions across the state. Convolutional neural networks and random forests are trained on NYSDOT roadside camera images and weather data to predict road surface conditions. This task draws critically on a robust hand-labeled dataset of ~22,000 camera images containing six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, including integration of operational datasets and use of representative and realistic images. The weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras. With operational deployment, this model has the potential to improve spatial and temporal awareness of road surface conditions, which can strengthen decision-making for operations, roadway maintenance, and traveler safety, particularly during winter weather events. |
| title | Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2510.06440 |