Determining quantitative road safety targets by applying statistical prediction techniques and a multi-stage adjustment procedure

Due to substantial progress made in road safety in the last ten years, the European Union (EU) renewed the ambitious agreement of halving the number of persons killed on the roads within the next decade. In this paper we develop a method that aims at finding an optimal target for each nation, in terms of being as achievable as possible, and with the cumulative EU target being reached. Targets as an important component in road safety policy are given as reduction rate or as absolute number of road traffic deaths. Determination of these quantitative road safety targets (QRST) is done by a top-down approach, formalized in a multi-stage adjustment procedure. Different QRST are derived under consideration of recent research. The paper presents a method to break the national target further down to regional targets in case of the German Federal States. Generalized linear models are fitted to data in the period 1991–2010. Our model selection procedure chooses various models for the EU and solely log-linear models for the German Federal States. If the proposed targets for the EU Member States are attained, the sum of fatalities should not exceed the total value of 15,465 per year by 2020. Both, the mean level and the range of mortality rates within the EU could be lowered from 28–113 in 2010 to 17–41 per million inhabitants in 2020. This study provides an alternative to the determination of safety targets by political commitments only, taking the history of road fatalities trends and population into consideration.     

A tool for safety evaluations of road improvements

Road safety impact assessments are requested in general, and the directive on road infrastructure safety management makes them compulsory for Member States of the European Union. However, there is no widely used, science-based safety evaluation tool available. We demonstrate a safety evaluation tool called TARVA. It uses EB safety predictions as the basis for selecting locations for implementing road-safety improvements and provides estimates of safety benefits of selected improvements. Comparing different road accident prediction methods, we demonstrate that the most accurate estimates are produced by EB models, followed by simple accident prediction models, the same average number of accidents for every entity and accident record only. Consequently, advanced model-based estimates should be used. Furthermore, we demonstrate regional comparisons that benefit substantially from such tools. Comparisons between districts have revealed significant differences. However, comparisons like these produce useful improvement ideas only after taking into account the differences in road characteristics between areas. Estimates on crash modification factors can be transferred from other countries but their benefit is greatly limited if the number of target accidents is not properly predicted. Our experience suggests that making predictions and evaluations using the same principle and tools will remarkably improve the quality and comparability of safety estimations.     

Older drivers and road safety: the acceptability of a range of intervention measures

A survey questionnaire was used to ascertain the views of 1932 UK drivers aged between 50 and 90 on a range of measures designed to promote safer driving among the elderly. Factor analysis of the items produced six factors, relating to statistically distinct types of measure. Differences in acceptability on the basis of age and sex were explored. Ratings of the effectiveness of each measure revealed little consensus about which measures would be most effective. Compulsory re-testing after a driving ban, a police power to require an assessment of the driving of anyone observed driving in a risky manner, and a requirement for opticians to report to the licensing authority any driver with sight problems likely to affect driving emerged as the individual measures offering the best combination of acceptability and perceived effectiveness.     

The importance of confounding in observational before-and-after studies of road safety measures

This paper discusses the importance of confounding in observational before-and-after studies of road safety measures. The importance of the approach taken to controlling for confounding factors is shown by means of examples. It is shown that the size of the effect on accidents attributed to a road safety measure can be profoundly affected by which confounding factors are controlled for in an evaluation study, and the way this is done. Simple before-and-after studies, not controlling for any confounding factors should never be trusted and are likely to overstate the effects of road safety measures.     

An exploratory analysis of models for estimating the combined effects of road safety measures

Road safety programmes consisting of a large number of road safety measures have been developed in many countries. To estimate the effects of such programmes on the number of accidents, models for estimating the combined effects of road safety measures are needed. This paper presents an exploratory analysis of such models. There is very little empirical evidence to support model building. Based on a few studies that have evaluated the effects of multiple road safety measures introduced at the same locations, the paper compares two models. One of the models, the common residuals model, assumes that the (percentage) effect of a road safety measure remains unchanged when it is combined with other road safety measures. The other model, the dominant common residuals model, assumes that the most effective measure in a set of measures has a dominant effect that weakens the effects of other road safety measures it is combined with. Evidence from the few studies that were found is consistent with both these models. A study of the effects of a road safety programme implemented in Victoria, Australia between 1990 and 1996 indicated that the effects of safety measures are weakened when these measures are combined with other road safety measures.     

Road safety performance indicators for the interurban road network

Various road safety performance indicators (SPIs) have been proposed for different road safety research areas, mainly as regards driver behaviour (e.g. seat belt use, alcohol, drugs, etc.) and vehicles (e.g. passive safety); however, no SPIs for the road network and design have been developed. The objective of this research is the development of an SPI for the road network, to be used as a benchmark for cross-region comparisons. The developed SPI essentially makes a comparison of the existing road network to the theoretically required one, defined as one which meets some minimum requirements with respect to road safety. This paper presents a theoretical concept for the determination of this SPI as well as a translation of this theory into a practical method. Also, the method is applied in a number of pilot countries namely the Netherlands, Portugal, Greece and Israel. The results show that the SPI could be efficiently calculated in all countries, despite some differences in the data sources. In general, the calculated overall SPI scores were realistic and ranged from 81 to 94%, with the exception of Greece where the SPI was relatively lower (67%). However, the SPI should be considered as a first attempt to determine the safety level of the road network. The proposed method has some limitations and could be further improved. The paper presents directions for further research to further develop the SPI.     

A conceptual framework for road safety and mobility applied to cycling safety

Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three ‘safety pillars’: infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the ‘non-linear relationship’ between them, i.e. risk tends to decrease as exposure increases. Furthermore, ‘perceived’ risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety.     

Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations

The empirical Bayes (EB) approach has now gained wide acceptance among researchers as the much preferred one for the before-after evaluation of road safety treatments. In this approach, the before period crash experience at treated sites is used in conjunction with a crash prediction model for untreated reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared to the count of crashes observed after treatment to evaluate the effect of the treatment. This procedure accounts for regression-to-the-mean effects that result from the natural tendency to select for treatment those sites with high observed crash frequencies. Of late, a fully Bayesian (FB) approach has been suggested as a useful, though complex alternative to the empirical Bayes approach in that it is believed to require less data for untreated reference sites, it better accounts for uncertainty in data used, and it provides more detailed causal inferences and more flexibility in selecting crash count distributions. This paper adds to the literature on comparing the two Bayesian approaches through empirical applications. The main application is an evaluation of the conversion of road segments from a four-lane to a three-lane cross-section with two-way left-turn lanes (also known as road diets). For completeness, the paper also summarizes the results of an earlier application pertaining to the evaluation of conversion of rural intersections from unsignalized to signalized control. For both applications, the estimated safety effects from the two approaches are comparable.     

The range of replications technique for assessing the external validity of road safety evaluation studies

This paper introduces a simple statistical technique that can be used to assess the external validity of road safety evaluation studies. External validity refers to the possibility of generalising the results of research to other contexts than those in which it was made. There are several aspects of external validity. Two aspects that are often of interest concern the applicability of the results of road safety evaluation studies across countries and time. Can the results of studies made in one or more countries be applied in countries where studies have not been made? Can the results of studies made many years ago still be applied? The technique introduced in this paper is designed to provide support in answering these questions. The technique evaluates the stability of research results in time and space. The technique is based on cumulative meta-analysis and produces statistics that show the consistency of study results in time and space (across countries). The range of replications denotes the span of time and countries in which studies have been made. The idea is that if the results of studies are stable throughout the range of replications, one may have greater confidence in their external validity than if the results of research vary in time and between countries. The technique is illustrated by means of numerical examples.     

Assessing the validity of road safety evaluation studies by analysing causal chains

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.     

The contribution of on-road studies of road user behaviour to improving road safety

For over 40 years transport safety researchers have been using methods of vehicle instrumentation to gain greater insights into the factors that contribute to road user crash risk and the associated crash factors. In the previous decade in particular the widespread availability of lower cost and more advanced methods of vehicle instrumentation and recording technologies are supporting the increasing number of on-road research studies worldwide. The design of these studies ranges from multi-method studies using instrumented test vehicles and defined driving routes, to field operational tests, through to much larger and more naturalistic studies. It is timely to assess the utility of these methods for studying the influences of driver characteristics and states, the design and operation of the road system, and the influences of in-vehicle technologies on behaviour and safety for various road user groups. This special issue considers the extent to which on-road studies using vehicle instrumentation have been used to advance knowledge across these areas of road safety research. The papers included in this issue illustrate how research using instrumented test vehicles continues to generate new knowledge, and how the larger scale United States and European naturalistic and field operational test studies are providing a wealth of data about road user behaviour in real traffic. This is balanced with a number of studies that present methodological developments in data collection and analysis methods that, while promising, need further validation. The use of on-road methods to accurately describe the behaviours occurring in everyday real-world conditions, to quantify risks for safety critical events, and an improved understanding of the factors that contribute to risk, clearly has huge potential to promote further road trauma reductions.     

A qualitative assessment methodology for road safety policy strategies

This paper proposes a qualitative assessment methodology that is comprised of a cluster analysis and an autoregression analysis that assess the effects of various road safety strategies implemented in Hong Kong over the last 10 years. The cluster analysis is first used to group over a hundred road safety projects and programs into a smaller set of meaningful road safety policy strategy clusters. These strategies, together with the trend factor, seasonal pattern, car crashworthiness and meteorological data are then used in the autoregression analysis to relate to the fatality and casualty rates of drivers, passengers, motorcyclists, and pedestrians. This method allows the evaluation of the overall effects of the road safety strategies, and the effects and relative significance of each individual strategy. The evaluation method is described, and the main findings of the study are discussed.     

New geometric design consistency model based on operating speed profiles for road safety evaluation

To assist in the on-going effort to reduce road fatalities as much as possible, this paper presents a new methodology to evaluate road safety in both the design and redesign stages of two-lane rural highways. This methodology is based on the analysis of road geometric design consistency, a value which will be a surrogate measure of the safety level of the two-lane rural road segment. The consistency model presented in this paper is based on the consideration of continuous operating speed profiles. The models used for their construction were obtained by using an innovative GPS-data collection method that is based on continuous operating speed profiles recorded from individual drivers. This new methodology allowed the researchers to observe the actual behavior of drivers and to develop more accurate operating speed models than was previously possible with spot-speed data collection, thereby enabling a more accurate approximation to the real phenomenon and thus a better consistency measurement.     

Analytic choices in road safety evaluation: Exploring second-best approaches

Conducting rigorous before-and-after studies is essential for improving knowledge regarding the effects of road safety measures. However, state-of-the-art approaches like the empirical Bayes or fully Bayesian techniques cannot always be applied, as the data required by these approaches may be missing or unreliable. The choice facing researchers in such a situation is to either apply “second-best” approaches or abstain from doing an evaluation study. An objection to applying second-best approaches is that these approaches do not control as well for confounding factors as state-of-the-art approaches. This paper explores the implications of choice of study design by examining how the findings of several evaluation studies made in Norway depend on choices made with respect to:

1.

Using the empirical Bayes approach versus using simpler approaches;     

Sources of error in road safety scheme evaluation: a quantified comparison of current methods

This paper considers the various factors that can have a confounding effect in the evaluation of road safety schemes and examines the extent to which current methods can effectively deal with these. A modification to current methods is proposed which allows the reduction in accidents attributable to risk and flow changes to be separately evaluated. Data are presented to demonstrate the relative magnitudes of the various sources of error. It is shown that a principal source of error is normally regression-to-mean (RTM) and a correction for this effect should always be applied. Changes in traffic flow can also result in substantial accident changes and it is important to establish whether flow changes have occurred and if they are attributable to the effect of the scheme.     

Five road safety education programmes for young adolescent pedestrians and cyclists: A multi-programme evaluation in a field setting

A practical approach was developed to assess and compare the effects of five short road safety education (RSE) programmes for young adolescents that does not rely on injury or crash data but uses self reported behaviour. Questionnaires were administered just before and about one month after participation in the RSE programmes, both to youngsters who had participated in a RSE programme, the intervention group, and to a comparable reference group of youngsters who had not, the reference group. For each RSE programme, the answers to the questionnaires in the pre- and post-test were checked for internal consistency and then condensed into a single safety score using categorical principal components analysis. Next, an analysis of covariance was performed on the obtained safety scores in order to compare the post-test scores of the intervention and reference groups, corrected for their corresponding pre-test scores. It was found that three out of five RSE programmes resulted in significantly improved self-reported safety behaviour. However, the proportions of participants that changed their behaviour relative to the reference group were small, ranging from 3% to 20%. Comparisons among programme types showed cognitive approaches not to differ in effect from programmes that used fear-appeal approaches. The method used provides a useful tool to assess and compare the effects of different education programmes on self-reported behaviour.     

Modelling the effects of road traffic safety measures

A model is presented for assessing the effects of traffic safety measures, based on a breakdown of the process in underlying components of traffic safety (risk and consequence), and five (speed and conflict related) variables that influence these components, and are influenced by traffic safety measures. The relationships between measures, variables and components are modelled as coefficients. The focus is on probabilities rather than historical statistics, although in practice statistics may be needed to find values for the coefficients. The model may in general contribute to improve insight in the mechanisms between traffic safety measures and their safety effects. More specifically it allows comparative analysis of different types of measures by defining an effectiveness index, based on the coefficients. This index can be used to estimate absolute effects of advanced driver assistance systems (ADAS) related measures from absolute effects of substitutional (in terms of safety effects) infrastructure measures.     

The likelihood of achieving quantified road safety targets: A binary logistic regression model for possible factors

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors – including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target – that contribute to a target’s success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed.     

Computing what the public wants: some issues in road safety cost-benefit analysis

In road safety, as in other fields, cost–benefit analysis (CBA) is used to justify the investment of public money and to establish priority between projects. It amounts to a computation by which ‘few’ – the CB analysts – aim to determine what the ‘many’ – those on behalf of which the choice is to be made – would choose. The question is whether there are grounds to believe that the tool fits the aim. I argue that the CBA tool is deficient. First, because estimates of the value of statistical life and injury on which the CBA computation rests are all over the place, inconsistent with the value of time estimates, and government guidance on the matter appears to be arbitrary. Second, because the premises of New Welfare Economics on which the CBA is founded apply only in circumstances which, in road safety, are rare. Third, because the CBA requires the computation of present values which must be questioned when the discounting is of future lives and of time. Because time savings are valued too highly when compared to life and because discounting tends to unjustifiably diminish the value of lives saved in the future, the CBA tends to bias decisions against investment in road safety.     

A review of the effect of traffic and weather characteristics on road safety

Taking into consideration the increasing availability of real-time traffic data and stimulated by the importance of proactive safety management, this paper attempts to provide a review of the effect of traffic and weather characteristics on road safety, identify the gaps and discuss the needs for further research. Despite the existence of generally mixed evidence on the effect of traffic parameters, a few patterns can be observed. For instance, traffic flow seems to have a non-linear relationship with accident rates, even though some studies suggest linear relationship with accidents. On the other hand, increased speed limits have found to have a straightforward positive relationship with accident occurrence. Regarding weather effects, the effect of precipitation is quite consistent and leads generally to increased accident frequency but does not seem to have a consistent effect on severity. The impact of other weather parameters on safety, such as visibility, wind speed and temperature is not found straightforward so far. The increasing use of real-time data not only makes easier to identify the safety impact of traffic and weather characteristics, but most importantly makes possible the identification of their combined effect. The more systematic use of these real-time data may address several of the research gaps identified in this research.