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Background The severity of the Road Traffic Injuries (RTIs) is highly influenced due to the transfer of the kinetic energy. This study is an endeavour to determine the relationship of crashes for a particular transport mode which is heavy transport vehicles (HTVs). We estimated the truck volume using a novel methodology and developed its relationship with various other factors. Objective To comprehensively assess the impact of heavy transport vehicles on road safety along the selected buffer zones at national highways and investigate its correlation with population density and estimated truck volume by developing a robust statistical model. Methods This study used geocoded data on fatal crashes in Haryana from 2017 to 2019. Subsequently, the crashes involving heavy vehicles that occurred on national highways were filtered. The present research adopts a new strategy for collecting volume data on heavy vehicles by utilising Google satellite imagery. Data from toll plazas located on national roads is also used to validate this methodology through modelling. Following this, the population data is extracted from the high-resolution density map retrieved from Meta. The road segment is divided into 500 and 1000-meter sections, and the Zero Inflated Negative Binomial (ZINB) and Hurdel models are used to estimate the relationship between fatal road crashes involving heavy vehicles, truck volume, population along varying buffer sections on national highways, and other variables. Results The findings indicate that the ZINB model outperforms the hurdle model in predicting crash frequency accurately. Fatal collisions tend to rise with increased heavy vehicle volume, population density along national highways, and access density. Conversely, fatal collisions decrease with more lanes and the presence of medians. Specifically, a one-unit increase in heavy vehicle volume corresponds to a logarithmic increase of 0.437 in crash frequency, holding all other factors constant. Conclusions This study provides significant findings on the intersection of heavy vehicle volume, roadside population, and crash occurrences on national highways. It emphasizes the importance of features such as medians and multiple lanes, for more accurate projections of crash frequency and developing safer road scenarios along the national highways.
Parikh et al. (Fri,) studied this question.