| Summary: | The chances of a road user getting involved in a crash increase with an increased number of vehicles. Because of the rise in mobility, especially Heavy Goods Vehicle (HGV), a comprehensive study of the causes of a road crash involving HGV is required. Univariate binary logistic Regression was applied to HGV crash data collected from police records to examine the contribution of several road elements factors to accident severity. A total of 3,663 crash cases involving HGV were analyzed for three years (2015-2017). Accident severity (the dependent variable) in this study is a dichotomous variable with two categories, fatal and non-fatal. Therefore, the subjects sampled were classified as either fatal or non-fatal crashes. As a result, from eight independent variables of road elements obtained from police crash reports, five were most significantly associated with accident severity: road geometry, road defect, shoulder type, quality of surface and lane markings. A statistical interpretation is estimated in terms of the odds ratio concept. In conclusion, the significant factors identified in this study can be used as a guide to mitigate the probability of severe injuries and deaths. The significant factors can also help relevant authorities plan and design a sufficient and safe road element, especially for HGV safety. © 2023 Author(s).
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