Development of heavy goods vehicle (HGV) accident severity prediction models

Heavy Goods Vehicles (HGVs) play a crucial role in regional freight movement and economic development but pose significant risks in traffic collisions, often leading to more severe injuries compared to other vehicle types. This research aims to explore the influence of road and environmental characteristics on the severity of HGV-related crashes within the Malaysian road network. By analyzing crash data collected from the Royal Malaysia Police and MIROS Road Accidents Database System (MROADS) for the period 2015-2017, the study examines 3,663 HGV crash records using binomial logistic regression to predict crash severity, categorized as either fatal or non-fatal. The analysis identifies eleven significant factors among the fifteen variables examined, including road surface quality, road geometry, lane marking, shoulder type, control type, road type, traffic system, weather conditions, time of day, day of the week, and various states in Malaysia. In contrast, factors such as area type, location type, speed limit, and light conditions were found to be statistically insignificant in predicting fatal outcomes. Three empirical models were developed in this research, each demonstrating high accuracy in predicting the severity of HGV crashes. The significant factors identified can be leveraged to mitigate the risk of severe injuries and fatalities. These findings are crucial for assisting relevant authorities in the strategic planning and design of road elements, with a particular focus on enhancing HGV safety by addressing key variables. In conclusion, the study underscores the importance of targeted interventions and informed policymaking in reducing the severity of HGV-related crashes. Continued and advanced research in this area is imperative to ensure secure and sustainable urban freight operations while fostering a safer driving environment. These insights can contribute to the development of more effective safety measures, ultimately reducing the societal and economic impacts of HGV-related traffic collisions.