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Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

Purpose – Driver distraction and other forms of driver inattention remain significant road safety problems. The purpose of this chapter is to explore recent developments in theoretical and empirical research on driver distraction and inattention and provide the reader with a sense for, and understanding of, the key issues.

Methodology – Key references from the literature are reviewed and discussed.

Findings – First, we discuss one way of conceptualising the distinction between driver distraction and other forms of inattention, as well as the mechanisms which may underlie these forms of inattention. Second, we underscores how driver distraction may derive from a plethora of sources, and how the potential for performance degradation deriving from driver interaction with these sources may be moderated by a range of factors. Third, we review recent literature on the types of impairments in driving performance and safety associated with driver distraction. Fourth, we outline recent literature on driver distraction and inattention in the realm of highly automated vehicles that will drive the transport future. Finally, we discuss some promising strategies aimed at preventing and mitigating the impact of driver distraction.

Research implications – There are many gaps in the driver distraction literature that need to be addressed. In addition, further research needs to be undertaken to examine the role of driver distraction in the realm of highly automated vehicles.

Practical implications – The findings point towards of a range of injury prevention countermeasures that have potential to prevent and mitigate driver distraction.

Parking choice involves an individual selecting a parking place based upon various inter-related factors. This chapter examines the factors that influence parking choice decisions.

A review of the literature on parking choice has been undertaken. The influence of various factors on parking choice and recommendations for future parking policy will be outlined.

Most often it is a combination of several factors which influence individuals’ choice of parking place.

Increased knowledge of the factors which influence parking-search behaviour will inform urban parking policy applications with associated environmental and economic benefits.