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.