Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Abstract

Road accidents cause hundreds of fatalities and injuries each year; due to their size and operating features, heavy trucks typically experience more severe accidents. Many factors are likely to cause such accidents; however, statistics mainly blame human error. This paper analyses the risk of accidents for heavy vehicles, focusing on driver-related factors contributing to accidents. A model is developed to anticipate the probability of an accident by using Bayesian networks (BNs) and fuzzy logic. Three axioms were verified to validate the developed model, and a sensitivity analysis is performed to identify the factors that have the most significant influence over truck accidents. Subsequently, the result provided by the model was exploited to examine the effects of in-vehicle road safety systems in preventing road accidents via an event tree analysis. The results underlined a strong link between the occurrence of accidents and parameters related to the driver, such as alcohol and substance consumption, his driving style, and his reactivity. Similarly, unfavourable working conditions significantly impact the occurrence of accidents since it contributes to fatigue, one of the leading causes of road accidents. Also, the event tree analysis results have highlighted the importance of equipping trucks with these mechanisms.

Road Accident Detection using SVM and Learning: A Comparative Study

“…When a traffic incident occurs, an alert system conducts periodic surveys and generates notifications that offer clear information about type and location of accidents [2], [3] and [4], to take the appropriate actions and minimize number of incidents death. Many digital and traditional solutions were explored to avoid and detect accidents; the digital solution has been investigated in smart city projects that handle various areas of urban development including road management and control.…”