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results show that single-vehicle crashes involving heavy vehicles at intersections are more

likely to occur on main roads and highways, whereas crashes at mid-blocks are more likely to
occur on higher speed roads, divided two-way roads, roads with special facilities or features

(e.g. bridges), and roads with higher percentages of heavy vehicle traffic. Intersection crashes
are also more likely to involve vehicles that are turning left or right, resulting in angle

crashes, whereas mid-block crashes are more likely to involve vehicle overturning.

The primary objective of the second study was to identify the neighbourhood socioeconomic
characteristics affecting injury severity in heavy vehicle collisions. Specifically, the study

explores the influences of the socio-demographic characteristics of the neighbourhoods where

road users live and where the crashes occur. This study uses a multinomial logit model. In
addition to neighbourhood socioeconomic variables, such as education, English language

proficiency, occupation, income, and birthplace, other variables affecting road user injury
severity, including environmental, temporal, road user, road, and vehicle characteristics, are

considered as control variables. The results show that road users residing in neighbourhoods
with more people born in Australia have higher injury severity, while road users living in

neighbourhoods with more people with a university education and working in the sales

profession have lower injury severity. Furthermore, crashes occurring in neighbourhoods
with more people working as professionals are more severe. The findings present mixed

results for the variables including technical education, clerical jobs and people born overseas

for the neighbourhoods where the road users live, and variables such as people born in
Australia, sales jobs and English language use for neighbourhoods where the crashes occur.

The socio-demographic characteristics of the neighbourhoods where the road user resides and
where the crash occurs contribute significantly to the road user injury severity in collisions

involving heavy vehicles. It is important to emphasise that these neighbourhood socio-
demographic characteristics should be used as a supplement to the information provided by

the standard collision hotspot analysis.

Finally, the main objective of the third study was to identify the factors contributing to injury
severity in angle crashes involving heavy vehicles, in order to provide insights into improving

traffic safety. The secondary objective was to compare the binary logic, skewed logistic

(Scobit) and random parameters logit models in terms of their accuracy in identifying the
factors contributing to injury severity in heavy-vehicle angle crashes. The findings indicate

that the skewed logit model performs slightly better than the standard binary logit and mixed
logit models in terms of the goodness of fit. The factors influencing injury severity in angle
vi