Spatiotemporal Analysis of Overloaded Vehicles on a Highway Using Weigh-in-Motion Data

Abstract

The comprehensive coverage of weighing detection systems at the entry point of every highway in China in 2020 caused congestion and unsafe traffic. In this study, weigh-in-motion data on overloaded vehicles were obtained from the Department of Highway Transportation Management of Jiangsu Province, and a spatiotemporal analysis was performed by utilizing the spatial analysis function of a geographic information system. In addition, the weight of the overloaded vehicles was calculated using the overload rate. Furthermore, the clustering and concentration of the overloaded vehicles were obtained using the global spatial autocorrelation model. The kernel density estimation method was then used to identify areas with severe overloading and calculate the local probability of overloading in that area. Our results revealed that the spatial distribution of the overloading severity was mainly influenced by the per capita income, density of highways, industry type, and freight policy in a given region, and that it was mainly concentrated in transportation hubs and areas with high traffic and complex logistics (e.g., municipal and provincial boundaries). Finally, the temporal aggregation of overloading was primarily affected by the level of law enforcement and freight policy because vehicles with high overload rates were mainly concentrated between 12:00 and 4:00 a.m.