Detecting high-risk traffic congestion during snow disasters: A random forest-based spatial model

Abstract

Early identification of areas susceptible to traffic congestion caused by snow disasters is critical for formulating effective emergency response strategies. This study focuses on the 2018 heavy snowstorm in Fukui Prefecture, Japan, and integrates GPS data with multi-source remote sensing datasets, including the digital elevation model (DEM), land use data, nighttime light imagery, Normalized Difference Vegetation Index (NDVI), urban area information, and other relevant spatial indicators. A spatial machine learning model based on the Random Forest algorithm was developed to identify congestion segments. The results show that: (1) During the 2018 Fukui snow disaster, severe traffic congestion was primarily concentrated in the cities of Sabae, Fukui, and Awara. Initial congestion points were frequently located on intercity roads near administrative boundaries, especially in areas classified as “field” or “forest” in land use. (2) A pilot model trained on data from 10 northern cities in Fukui achieved an accuracy of 94.59%, confirming the feasibility of the method. (3) Feature importance analysis identified the most influential factors as: Snow Depth > Nighttime Light Difference > Elevation > Slope Angle > Urban Area > NDVI > Population Change > Forest > Field > Low-rise Buildings (Sparse) > Low-rise Buildings (Dense).