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Chapter 5 Injury severity in angle crashes involving heavy vehicles

5.1 Introduction

Heavy vehicles contribute significantly to many developed economies, including Australia,
because they are a major means of transporting goods within these countries. In Australia, for

example, over 75 per cent of the non-bulk domestic freight is carried on roads, dominating
freight between large cities, and it is predicted that truck traffic will increase by around 50

percent by 2030 (DIRE, 2014). In addition, activities involving heavy vehicles are projected

to double from 2000 to 2020 as a result of the transportation of goods in Australia (Manders,
2006). The increasingly high share of truck traffic has generated some safety concerns

because the probability of a traffic collision increases by five per cent when the percentage of
heavy vehicles is more than 30 per cent of the total traffic volume (Moridpour et al., 2015).

Moreover, the likelihood of a severe outcome (fatality or serious injury) is much higher when
a heavy vehicle is involved in a traffic collision.

In Australia, approximately 11 traffic fatalities involving articulated trucks and 7 road
fatalities involving rigid trucks occurred per month between 2009 and 2013 (Austroads,

2015). Furthermore, the number of road fatalities resulting from heavy rigid truck crashes

increased by 8.5% each year between 2012 and 2014 (BITRE, 2014). Although heavy
vehicles comprise approximately three per cent of the total registered vehicles and eight per

cent of the vehicle-kilometres travelled, this vehicle type is involved in 18 per cent of all road
fatalities (ATC, 2011).

The primary objective of this research is to identify factors contributing to occupants’ injury

severity in crashes involving at least one heavy vehicle. Specifically, two-vehicle angle

crashes in the State of Victoria are analysed using three logistic regression models. This study
will contribute to advancing knowledge in this field because little or no research has been

done on understanding angle crashes involving heavy vehicles. In addition, this study
compares the binary logistic, skewed logistic, and mixed logit models to check the robustness

of the results and to compare their relative performance when applied to data that are
moderately skewed.

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