用摄像头和天气数据，自动识别道路状况，准确率达81.5%
Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data
- 结合图像与天气数据，用深度学习和随机森林建模
- 在未见过的摄像头上达到81.5%准确率
- 适合交通部门冬季路况监测与应急决策
交通管理部门在恶劣天气下需快速评估路况并调配资源。本研究为纽约州交通部（NYSDOT）开发机器学习模型，通过路边摄像头图像和气象数据自动分类全州道路表面状况。模型基于约2.2万张人工标注的图像训练，涵盖六类状态：严重积雪、积雪、湿滑、干燥、能见度差、遮挡。重点提升模型泛化能力，以满足实际运营需求，包括使用真实场景图像和整合操作数据。该天气相关路面状态模型在完全未见过的摄像头上实现81.5%的准确率。部署后有望显著提升对路况时空变化的感知能力，强化运营决策、道路养护及出行安全，尤其在冬季天气中。
原文摘要 · Abstract (English)
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.
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