Truck Overload Warning and Risk Assessment Method Based on In-Vehicle System Data

<div class="section abstract"><div class="htmlview paragraph">Overloading of trucks will not only damage road infrastructure, lead to exhaust pollution, and even cause serious traffic accidents, resulting in huge losses of life and property. However, most of the methods to evaluate truck overloading are limited by environmental factors, so it is impossible to monitor truck overloading in real time. In order to solve this problem, a truck overload detection method based on real-time vehicle diagnosis big data is proposed in this paper. The method comprehensively considers multiple factors affecting the actual power of trucks through mathematical modeling. It based on the effects of overload on fuel combustion efficiency, harmful gas emission, exhaust temperature, and vehicle power loss, The truck overload evaluation model is constructed to judge whether the truck is overloaded or not in real time. Based on the truck overload assessment and truck accident risk factor extraction , a real-time operation risk assessment model based on fault tree analysis is developed to evaluate the safety of overloaded and non-overloaded trucks. The fault tree model is mapped to a Bayesian network model and transformed into equivalent network model by Netica software. The network model is analyzed qualitatively and quantitatively, and the key factors that have great influence on the accidents, such as bad driving conditions, dangerous driving behavior and poor visibility. This research enhances the traffic management department's ability of monitoring and early warning of truck overloading, strengthens the deterrence and efficiency of overload control measures, helps to reduce the occurrence of overloading. This ultimately improves road transport safety, reduces exhaust emissions and environmental pollution caused by overloading.</div></div>