Safe Mobility: Challenges, Methodology and Solutions: Volume 11

Purpose – This chapter first provides the motivation for writing this book. It then describes the challenges involved with assessing societal safety through the analysis of transport system crashes. It concludes with a summary of the contents of the remainder of the book, identifying how various dimensions of the transport system challenges are addressed.

Methodology/Approach – This chapter discusses important real-world and methodological challenges that practitioners, academics and researchers face in making a more sustainable highway system through a reduction in the number and severity of transport network crashes resulting in fatalities, injuries and property damage.

Findings – The chapter first describes important challenges, such as complexity of the driving task, the challenges of engineering transport systems for humans, unanticipated effects that arise from differences between driver safety and security, the co-mingling of mobility modes of travel, and challenges in evaluating road safety. The chapters are separated into five general themes: driver behaviour, the transportation network, vulnerable road users, methods for understanding and predicting safety performance, and methods for evaluating safety impacts of countermeasures.

Practical Implications – Comprehending the challenges associated with road crashes is a first step in making the roadway system more sustainable. This book provides a broad and understandable description of these challenges and how they can be overcome by academics and practitioners working in transport network safety management.

Originality – This book presents a clear understanding and offers insights about the challenges and potential solutions that can be brought to bear to make a more sustainable and safe transport system, whether it is located in an urban or rural area, and for a wide variety of functional classifications and designs. The topics covered in this book are intended to be useful and applied to tackle transport system management anywhere in the world.

Purpose – Driver education and licensing are two mechanisms used to reduce crash rates. The purpose of this chapter is to provide an overview of these countermeasures and consider how simulators can be used to augment more traditional approaches.

Approach – A literature review was undertaken evaluating key concepts in driver licensing including graduated driver licensing (GDL), the role of parents in licensing, compliance and enforcement, driver testing and how the driver licensing system impacts on levels of unlicensed driving. Literature regarding driver education for individuals who have and not yet obtained a licence was also reviewed.

Findings – GDL is a successful countermeasure for reducing the crash rates of young novice drivers as it limits their exposure to higher risk situations. The support for driver education initiatives is mixed. As there are big differences between education programs, there is a need to consider each program on its own merits. Driving simulators provide a safe environment for novices to gain experience. In particular, they may be bifacial for development of hazard perception and visual scanning skills.

Practical Implications – GDL systems should be introduced where appropriate. Existing systems should be strengthened where possible by including additional, best-practice and restrictions. When considering driver education as a countermeasure, the type of program is very important. Education programs that have been shown to increase crashes should not be introduced. Further research and development are necessary to ensure that driver education and licensing adequately equip novice drivers with the skills necessary to drive in the 21st century.

Purpose: This chapter synthesises a variety of findings on the topic of aggressive driving and delivers a suite of strategies for moderating such behaviours. Examples and formal definitions of aggressive driving acts are given, along with specific techniques for reducing excessive speed and other aggressive behaviours.

Methodology: Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings: Speeding is the most common type of aggressive driving, and speeding-related crashes represent a high share of traffic deaths. Speeding relates to many factors, including public attitudes, personal behaviours, vehicle performance capabilities, roadway design attributes, laws and policies. Anonymity, while encased in a vehicle, and driver frustration, due to roadway congestion or other issues, contribute to aggressive driving.

Research implications: More observational data are needed to quantify the effects of the contributing factors on aggressive driving.

Practical implications: Driver frustration, intoxication and stress can lead to serious crashes and other traffic problems. They can be addressed, to some extent, through practical enforcement, design decisions and education campaigns.

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.

Purpose – Urban and suburban arterials carry a large share of urban traffic and contend with a relatively large proportion of transport network crashes. Road crashes and their consequent societal costs diminish the sustainability of transportation systems, highlighting the need to identify road safety problems and their corresponding solutions. This chapter briefly outlines problems and solutions associated with crash risk on urban and suburban arterials. In addition, this chapter studies and discusses several safety countermeasures – ranging from local treatments to integral frameworks – and their effectiveness on improving traffic safety of urban and suburban arterials.

Approach – Crash occurrence on urban and suburban arterials is affected by numerous contributing factors. This chapter pays attention primarily to the effects of traffic characteristics and road design features. In this regard, several pertinent variables which have been extensively examined in the literature are reviewed and their contributions to the safety of urban and suburban arterials are discussed.

Findings – A review of the literature identifies a number of variables as influential factors of crashes on urban and suburban arterials. Although the associations of some variables (e.g., traffic volume) are consistent with expectations, others (e.g., lane width and speed) show mixed and sometimes counterintuitive results. These findings signify that additional research is needed to reveal the correct functional form and magnitude of these relationships.

Practical implications – The results show that while the general direction and magnitude of effects of some engineering and management-related treatments are known, additional research is needed to consolidate the impact and effectiveness of integrated approaches.

Purpose – Freeway networks are designed to higher standards and are safer infrastructures as compared to other road types, if properly designed. On the other hand, these facilities are driven at very high speeds and therefore speed and design consistency are essential for achieving safe infrastructure designs. This chapter describes the criteria for speed and design consistency and looks at new tools and criteria for improving freeway safety in new and in existing infrastructures.

Methodology – This chapter describes the criteria to evaluate if there are speed, design and human factors inconsistencies, as well as potential solutions for tackling local deficiencies and speeding issues. As one of the critical issues in freeway safety is represented by run-off-road crashes, a specific section in the chapter is devoted to newly developed design and assessment tools for improving roadside safety. The potential implications of Intelligent Transportation System (ITS) technologies on freeways design and management are also presented.

Findings and Social Implications – The important crash reduction trends observed in the decade 2001–2010 are now slowing down and new actions are required to be coupled with more traditional design checks. The full implementation of cooperative ITS systems is expected to have a very important impact on road safety, but in the short term several safety improvements can be realised: section speed enforcement techniques and high-friction wearing courses have been proven to be extremely effective, as have perceptual measures accounting for human factors principles.

Purpose – Intersections are hazardous locations and to improve their safety we need to understand the factors contributing to crashes at these locations and provide evidence-based recommendations to reduce them. This chapter provides a summary of the findings on infrastructure-related factors contributing to crashes at urban and rural intersections and some discussions on the implications and potential countermeasures.

Approach – A review of the literature on intersection crashes was performed to identify the infrastructure-related crash-contributing factors. Some discussions on the implications and potential countermeasures are then provided.

Findings – The factors contributing to road crashes are diverse and complex. While the safety effects of a few factors (e.g., exposure and speed) are relatively consistent, many factors have different impacts on crash frequency and severity (e.g., types of intersection) and different impacts on urban and rural intersections (e.g., bus stops).

Research Implications – More studies are needed on developing a stronger theoretical or conceptual foundation on the effects of roadway designs and traffic controls on different dimensions of safety (e.g., exposure, frequency, severity, etc.), types of crashes (e.g., head-on, rear-end, etc.) or road users involved (e.g., drivers, pedestrians, cyclists, etc.).

Practical Implications – Transport engineers need to be aware that some treatments may have different effects on different crash types and road users involved. Even though the overall safety may be improved by the treatments designed, they need to consider and mitigate any unintended consequences to satisfy the Pareto improvement principle and the social equity criterion.

Purpose – Measures aimed at reducing intersection crashes have high potential to be cost effective since intersections constitute only a small part of the overall highway system but intersection-related crashes constitute more than 50% of all crashes in urban areas and over 30% in rural areas. Roundabouts are a proven safety countermeasure, but several issues that significantly affect both crash frequency and severity have been observed at both existing and new roundabouts. This chapter aims to provide guidance on roundabout selection and design criteria.

Methodology – The chapter first describes the most relevant criteria to be considered for choosing a roundabout. Then, after the explanation of the roundabout design process and a clear description of the roundabout classification, the chapter provides recommendations for all the steps of the geometric design, highlighting the main design features that contribute to the best safety performances, including speed control and sight distance checks. Finally, the chapter explains traffic control devices and facilities for pedestrians and cyclists.

Findings – Roundabout design needs to balance opposing demands and it is important to adopt a performance-based design approach within an iterative process. The most important performance check is the analysis of vehicle speeds through the roundabout, since achieving appropriate vehicular speeds has a very positive safety effect.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

Purpose – This chapter aims to advise the public as well as municipal, state and national agencies about how pedestrian safety can be improved through changes in our built environment. Higher safety can lead to more walking and thereby a more sustainable society.

Methodology – The chapter is based on a review of literature, including a review of published papers and field studies by the author himself.

Findings – To reach ‘acceptable’ safety levels, all arterials and collector roads – at least segments with more than 50 pedestrians a day – should have sidewalks. The sidewalks should be separated from the roadway by a curb if speeds are low and by a barrier or wide separation strip in high-speed areas; that is, where speeds are higher than 50 km/h. Local roads also need sidewalks unless traffic volumes and speeds are very low. The major safety issue for pedestrians is, however, where they cross streets. Elderly pedestrians and pedestrians in a great hurry or under the influence of intoxicants in particular need streets to be narrow and have low speeds for them to be able to cross safely. Marking crosswalks or even signalising them will have only marginal safety effects at best. Posting them for low speed is also not enough unless we have photo speed-enforcement ensuring that everyone drives slowly. Rather, using narrow cross-sections or speed cushions at the approaches ensuring that 90-percentile speeds are no more than 30 km/h at crossing points is key to safety. In between crossing points a speed of 50 km/h is acceptable with pedestrians on adjacent sidewalks.

Social implications – We as a society need to encourage walking as a mode of transportation since walking promotes better health and a cleaner environment; that is, a more sustainable society. However, it has to be safe to walk or people will prefer to drive to their destinations. Also, distances between destination points have to be kept reasonably short and the environment, where people walk needs to be interesting and aesthetically somewhat pleasing to encourage walking.

Purpose – Bicycle riding provides a sustainable and affordable solution to many of the significant problems associated with motorised transport and physical inactivity. The provision of infrastructure plays an important role in encouraging people to begin and subsequently continue to ride bicycles and to do so safely.

Methodology – This chapter describes different types of on- and off-road infrastructure and reviews studies of their effects on rider numbers and safety. In addition, it looks at the roles that end-of-trip facilities and bikeshare programs can play in contributing to bicycle use and general transport sustainability.

Findings – Infrastructure characteristics can influence both perceived and objective levels of safety. It is important to identify and avoid treatments that increase perceived safety but are actually less safe. The type of infrastructure needed or desired differs between current and potential riders and according to trip purpose. Well-designed marked bicycle lanes on roads can reduce crash rates. Safety at intersections can be improved by: advanced green lights for cyclists, short cuts for right-hand turns, brightly coloured bicycle paths and advanced waiting positions for cyclists. Off-road facilities are generally safer, but intersections with roads must be carefully treated. Shared paths and footpaths are risky for older pedestrians (and older cyclists).

Implications – In many countries the provision of more infrastructure that increases the perceived safety of riding is needed to encourage cycling, particularly transport cycling and cycling by women.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.

Purpose – Time-series regression models are applied to analyse transport safety data for three purposes: (1) to develop a relationship between transport accidents (or incidents) and various time-varying factors, with the aim of identifying the most important factors; (2) to develop a time-series accident model in forecasting future accidents for the given values of future time-varying factors and (3) to evaluate the impact of a system-wide policy, education or engineering intervention on accident counts. Regression models for analysing transport safety data are well established, especially in analysing cross-sectional and panel datasets. There is, however, a dearth of research relating to time-series regression models in the transport safety literature. The purpose of this chapter is to examine existing literature with the aim of identifying time-series regression models that have been employed in safety analysis in relation to wider applications. The aim is to identify time-series regression models that are applicable in analysing disaggregated accident counts.

Methodology/Approach – There are two main issues in modelling time-series accident counts: (1) a flexible approach in addressing serial autocorrelation inherent in time-series processes of accident counts and (2) the fact that the conditional distribution (conditioned on past observations and covariates) of accident counts follow a Poisson-type distribution. Various time-series regression models are explored to identify the models most suitable for analysing disaggregated time-series accident datasets. A recently developed time-series regression model – the generalised linear autoregressive and moving average (GLARMA) – has been identified as the best model to analyse safety data.

Findings – The GLARMA model was applied to a time-series dataset of airproxes (aircraft proximity) that indicate airspace safety in the United Kingdom. The aim was to evaluate the impact of an airspace intervention (i.e., the introduction of reduced vertical separation minima, RVSM) on airspace safety while controlling for other factors, such as air transport movements (ATMs) and seasonality. The results indicate that the GLARMA model is more appropriate than a generalised linear model (e.g., Poisson or Poisson-Gamma), and it has been found that the introduction of RVSM has reduced the airprox events by 15%. In addition, it was found that a 1% increase in ATMs within UK airspace would lead to a 1.83% increase in monthly airproxes in UK airspace.

Practical applications – The methodology developed in this chapter is applicable to many time-series processes of accident counts. The models recommended in this chapter could be used to identify different time-varying factors and to evaluate the effectiveness of various policy and engineering interventions on transport safety or similar data (e.g., crimes).

Originality/value of paper – The GLARMA model has not been properly explored in modelling time-series safety data. This new class of model has been applied to a dataset in evaluating the effectiveness of an intervention. The model recommended in this chapter would greatly benefit researchers and analysts working with time-series data.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

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 – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies.

Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way.

Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue.

Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17.

Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.

Purpose – This chapter overviews surrogate measures of safety to help better understand the related challenges and opportunities. The chapter is meant to serve as a primer for practitioners looking for alternative methods of evaluating safety where crashes are lacking or are insufficient.

Approach – The historical perspective and the current state-of-the-art thinking are presented in an organised manner with a focus on fundamental concepts, traffic measurement techniques and estimation of the relationships between surrogate events and collisions.

Findings – An analysis of the published research and its findings indicates that traffic conflicts are the most promising surrogates. They enable evaluation of the safety implications of a wide range of road and traffic conditions. The required ecological consistency between conflicts and collisions can be ensured by sufficient nearness of conflicts to collisions. Several methods of estimating the relationship between conflicts and crashes are discussed. Behavioural measures of safety are also discussed. Although easier to measure than conflicts, behavioural measures should be used with caution. Research on surrogate measures of safety may provide a basis for improving microsimulation models as tools of safety evaluation.

Practical implications – Current changes in vehicle and road instrumentation affect safety at a rate that exceeds the efficiency of the traditional crash-based methods of safety analysis. Accurate and quick measurement of safety with surrogate measures offers a viable solution. They are also a necessary condition of gaining a better understanding of safety and finding more effective solutions for safety problems.

Purpose – The focus of this chapter is on state-of-the-art methodology for observational before–after evaluations. The purpose of such evaluations is to acquire information on the safety effects of site-specific treatments that could be used by road agencies for future interventions that may have an impact on safety.

Methodology – Information, illustrated by a detailed example, is presented mainly on what is still regarded as the gold standard – the Empirical Bayes (EB) method. The methodology accounts for observed changes in crash frequencies that may be due to regression-to-the-mean, changes in traffic volume and time trends. Two related approaches – a comparison group method and the Full Bayes approach, which have been legitimately used for evaluations under special circumstances – are discussed in brief.

Findings – The EB before–after methodology remains the gold standard but must be applied properly.

Research implications – Some issues, for example, the selection of reference groups, still require further research.

Practical implications – Key issues in the application of the before–after evaluation methods in general are also discussed.

Purpose – This chapter gives an overview of meta-analytic methods and illustrates the use of these methods for synthesising research findings. The advantages of performing a meta-analysis are described. Pitfalls in meta-analyses are also discussed. The chapter is intended to present the main elements of a meta-analysis and guide readers to literature presenting meta-analytic methods in greater detail.

Methodology – Key references in the meta-analysis literature are quoted and examples of meta-analyses are presented.

Findings – A meta-analysis is a useful tool for summarising knowledge in fields where a large number of studies have been reported. In addition to providing summary estimates of results, a meta-analysis can be applied to identify factors that produce systematic variation in study findings.

Research implications – Methods of meta-analyses keep developing to deal with complex data structures, thus extending the type of research findings that are amenable to meta-analyses.

Practical implications – Performing a meta-analysis saves labour by eliminating the need to read and digest a large number of studies in order to get an overview of the current state-of-knowledge in a field. Moreover, a meta-analysis establishes a system for easily and quickly updating knowledge as new studies become available.

Purpose – This chapter highlights main thrusts discussed in the book in its entirety.

Methodology – This chapter reviews the content of the book, drawing together the challenges safety analysts and other important road safety stakeholders confront while trying to understand and reduce transport system risks.

Findings – The chapter describes the challenges the profession is confronting. These challenges include the difficulty in analysing crashes and drawing reliable conclusions when crashes are rare, the complexities involved with piecing together and identifying actual causes of crashes, how driver behaviour is a dominant influence on crash risk, how a diverse mix of road users is challenging to manage, that estimating the safety benefits of treatments is fraught with complexity, how surrogate measures of safety – despite the analytical research needed on their linkages with crashes – can be used for proactively improving design and operational decisions, and how real-time data collected on monitored highways, which need the development of solid theoretical underpinnings moving forward – can be used reliably for estimating crash risks and manage safety. The chapter briefly summarises how these challenges may be addressed moving forward.

The chapter also identifies future research opportunities that can be pursued to further improve safe mobility. These opportunities include the use of alternate methodologies for ‘measuring safety’ such as advanced vision recognition techniques, the use of surrogate measures of safety such as time to collision, driverless and connected vehicles, naturalistic driving experiments and data, the reshaping of urban cities, and the rapid growth and motorisation of developing countries.

Practical implications – By fostering a better understanding of the challenges the profession is facing, and prompting a dialogue on how they can be overcome, will lead to a reduction in the number and severity of crashes of global transport networks, making them more sustainable. The chapter shows how some of these challenges can be tackled.

Originality/value of chapter – This chapter draws from all the chapters of this book that describes various challenges road safety professionals currently face, and directs readers to different parts of the book for more in-depth treatment on how these challenges can be met. The chapter also describes research opportunities where further safety gains can be obtained moving forward.