Advancing winter road maintenance: An AI-driven web platform for real-time road condition monitoring and spatial analysis

Winter weather conditions pose significant challenges for transportation agencies, impacting road safety, traffic flow, and winter road maintenance (WRM) operations. Traditional methods for monitoring road surface conditions (RSCs) often involve time-consuming processes that require significant personnel. To address these challenges and maximize the utility of existing infrastructure, this paper presents a web-based system for real-time RSC monitoring. The system combines convolutional neural networks (CNNs) for RSC classification, a novel Nested Indicator Kriging (NIK) method for spatial interpolation, and modern web technologies to provide an intuitive interface. The system seamlessly integrates CNN models for real-time classifications using automated vehicle location (AVL) and road weather information system (RWIS) imagery. The NIK method enhances spatial coverage by classifying multiple RSC categories through two layers: the first identifies basic road conditions as bare or non-bare, while the second discriminates between more complex states, such as partially or fully snow-covered. Validated through simulations using historical data, the integrated AVL CNN model achieved a training accuracy of 99.89% and a validation accuracy of 94.62% during training, while the RWIS model reached a maximum accuracy of 98.46% and an F1 Score of 97.19%. Furthermore, the NIK method showed cross-validation accuracies averaging 73.5% for the first layer, and 86.0% for the second layer. This unified system represents an advancement in WRM decision support by automating RSC classifications and closing gaps in spatial data coverage, thus improving the efficiency and sustainability of operations and enhancing the ability of safety professionals and operators to respond to roadway hazards in real-time.