Quantitative Assessment on Truck-Related Road Risk for the Safety Control via Truck Flow Estimation of Various Types

Abstract

Traffic conditions of truck flow is one of the critical factors influencing transportation safety and efficiency, which is directly related to traffic accidents, maintenance scheduling, traffic flow interruption, risk control, and management. The estimation of the truck flow of various types could be better to identify the irregular flow variation introduced by various trucks and quantitatively assessed the corresponding road risks. In this paper, the dynamics of truck flow are estimated first. The stochastic and uncertain trucks flow data are obtained in terms of small, medium, heavy, and the oversize truck type and regulated corresponding flow in the time series within five minutes. In order to dig the spatial-temporal correlations behind those data, the deep learning-based method is improved on the basis of the gated recurrent unit (GRU) to estimate the truck flow for various types. To quantitatively assess the truck-related effect for road risk, a multiple logistic regression method is further proposed to classify into safe, risky, and dangerous road risks levels. Different risk level could guide the traffic control and management and traffic information that broadcast drivers to help them to choose travel route. The proposed prediction of the road risk is tested in the randomly selected road segment and shows superior compared to other methods. This could promote road safety in the development of intelligent transport system (ITS).