Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran’s I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis–Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.
Heavy vehicles play a vital role in the economic wellbeing of a country. Safety measures are necessary to ensure the safety of heavy vehicles and other vehicles since the majority of crashes involving heavy vehicles are frequently severe crashes. This study utilized the data provided by Malaysian Highway Authorities (MHA) to investigate the accidents involving heavy vehicles on the expressways in Malaysia. Result of the analysis shows that most of the heavy vehicle accidents on expressways occurred during the day (54.8%) and clear weather (88.1%). Most night-time accidents and fatal accidents occurred on roads without street lights, where is a total of 22.2% of the heavy vehicle accidents occurred on roads without street lights with 28.2% are fatalities cases whereas 16.8% of the accidents occurred on roads with street lights with 17.6% are fatalities cases. Heavy vehicle accidents frequently occur in flat areas, and they also cause 32.5% of the deaths in multi-vehicle accidents. The Chi-Square test was also performed in order to identify the relationship between accident severity and type of vehicle (in MVA and SVA) and the relationship between heavy vehicle accident severity and type of topography of road profile. The result shows that there was a significant association between accident severity and type of vehicle in MVA and SVA. It is also revealed type of topography of road profile affects the heavy vehicle accident severity. The finding of this study can help safety planners to develop a safety management plan for heavy vehicles, especially for heavy goods vehicles.
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