Relating traffic fatalities to GDP in Europe on the long term

Modeling road safety development can provide important insight into policies for the reduction of traffic fatalities. In order to achieve this goal, both the quantifiable impact of specific parameters, as well as the underlying trends that cannot always be measured or observed, need to be considered. One of the key relationships in road safety links fatalities with risk and exposure, where exposure reflects the amount of travel, which in turn translates to how much travelers are exposed to risk. In general two economic variables: GDP and unemployment rate are selected to analyse the statistical relationships with some indicators of road accident fatality risk.The objective of this research is to provide an overview of relevant literature on the topic and outline some recent developments in macro-panel data analysis that have resulted in ongoing research that has the potential to improve our ability to forecast traffic fatality trends, especially under turbulent financial situations. For this analysis, time series of the number of fatalities and GDP in 30 European countries for a period of 38 years (1975–2012) are used. This process relies on estimating long-term models (as captured by long term time-series models, which model each country separately). Based on these developments, utilizing state-of-the-art modelling and analysis techniques such as the Common Correlated Effects Mean Group estimator (Pesaran), the long-term elasticity mean value equals 0.63, and is significantly different from zero for 10 countries only. When we take away the countries, where the number of fatalities is stationary, the average elasticity takes a higher value of nearly 1. This shows the strong sensitivity of the estimate of the average elasticity over a panel of European countries and underlines the necessity to be aware of the underlying nature of the time series, to get a suitable regression model.     

A statistical model to compare road mortality in OECD countries

The objective of this paper is to compare safety levels and trends in OECD countries from 1980 to 1994 with the help of a statistical model and to launch international discussion and further research about international comparisons. Between 1980 and 1994, the annual number of fatalities decreased drastically in all the selected countries except Japan (+ 12%), Greece (+ 56%) and ex-East Germany (+ 50%). The highest decreases were observed in ex-West Germany (- 48%), Switzerland (- 44%), Australia (- 40%), and UK (- 39%). In France, the decrease in fatalities over the same period reached 34%. The fatality rate, an indicator of risk, decreased in the selected countries from 1980 to 1994 except in the east-European countries during the motorization boom in the late 1980s. As fatality rates are not sufficient for international comparisons, a statistical multiple regression model is set up to compare road safety levels in 21 OECD countries over 15 years. Data were collected from IRTAD (International Road Traffic and Accident Database) and other OECD statistical sources. The number of fatalities is explained by seven exogenous (to road safety) variables. The model, pooling cross-sectional and time series data, supplies estimates of elasticity to the fatalities for each variable: 0.96 for the population; 0.28 for the vehicle fleet per capita; -0.16 for the percentage of buses and coaches in the motorised vehicle fleet; 0.83 for the percentage of youngsters in the population; - 0.41 for the percentage of urban population; 0.39 for alcohol consumption per capita; and 0.39 for the percentage of employed people. The model also supplies a rough estimate of the safety performance of a country: the regression residuals are supposed to contain the effects of essentially endogenous and unobserved variables, independent to the exogenous variables. These endogenous variables are safety performance variables (safety actions, traffic safety policy, network improvements and social acceptance). A new indicator, better than the mortality rate, is then set upon the residuals. Mean estimates of this indicator for the years 1980-1982 and the years 1992-1994 rank the countries in the beginning and at the end of the study period. Countries showing the best ranks (and thus the best performance) in 1980 and 1994 are Sweden, the Netherlands and Norway. The UK and Switzerland reach the top 5 in 1994. Greece, Belgium, Portugal and Spain are the last countries in the classification along with, surprisingly, the USA. France was ranked 18th in 1980 and 15th in 1994 but is ranked amongst the five countries that most improved from 1980 to 1994. This model remains non definitive because it is not able to distinguish between safety performance and unobserved exogenous variables although these exogenous variables could explain more about the differences in levels and trends between the countries. More complex models, particularly highly sophisticated models regarding the number of fatalities with breakdowns by road users or road classes would be needed to give a precise and profound ranking of safety levels and safety improvements between countries.     

State-space based analysis and forecasting of macroscopic road safety trends in Greece

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960–2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009–2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis.     

Road safety development in Europe: A decade of changes (2001–2010)

To evaluate the road safety development of a country over time, the percentage change in the number of road fatalities is traditionally the main indicator. However, simply considering the reduction in the road fatalities may not correctly reflect the real improvement in road safety because the transport circumstances of a country underlying the road fatalities also change every year. In this study, we present a new way for measuring the road safety performance change over time, which is to use the technique of data envelopment analysis (DEA) and the Malmquist productivity index. In doing so, we can not only focus on the evolution of road safety final outcomes within a given period, but also take the changes of different measures of exposure in the same period into account. In the application, the DEA-based Malmquist productivity index (DEA-MI) is used to measure the extent to which the EU countries have improved their road safety performance over the period 2001–2010. More objective and insightful results are obtained compared to the ones based on the traditional indicator. The results show considerable road safety progress in most of the Member States during these ten years, and the fatality risk rather than the fatality number on Europe's roads has actually been reduced by approximately half. However, the situation differed considerably from country to country. The decomposition of the DEA-MI into ‘efficiency change’ and ‘technical change’ further reveals that the bulk of the improvement during the last decade was attained through the adoption of productivity-enhancing new technologies throughout the road transport sector in Europe, rather than through the relatively underperforming countries catching up with those best-performing ones.     

The stability of long-term trends in the number of traffic fatalities in a sample of highly motorised countries

This paper examines the stability of long-term trends in the number of traffic fatalities in eight highly motorised countries: Norway, Sweden, Denmark, Finland, The Netherlands, Great Britain, Australia and The United States. In all these countries, the number of traffic fatalities reached a peak around 1970-1972 and has since declined. The decline has, however, been irregular and fairly long periods of stagnation or even increase in the number of fatalities have occurred in all countries. A stable trend is defined in this paper as a trend that remains unchanged and therefore can be used to successfully predict the future number of fatalities by means of extrapolation. It is concluded that the trends towards fewer fatalities in the countries selected have not been stable and that even trend lines that fit past trends very closely are usually worthless for predictive purposes. An attempt was made to identify factors influencing long-term trends by fitting negative binomial regression models to fatality data. Although some of the models fitted the data marginally better than simple trend lines, these models are not necessarily more useful for predicting future trends than simple trend lines, since using the models for prediction requires that future changes in all explanatory variables must be predicted. The chief lesson is that past trends do not provide a reliable basis for predicting future developments with respect to the number of traffic fatalities.     

Prediction of traffic fatalities and prospects for mobility becoming sustainable-safe

The macroscopic trend of road traffic fatalities in any motorized country is described and predicted by the product of rather well fitting functions of time for the exponential decay of fatality risk per unit of traffic volume and the S-shaped Gompertz function of traffic volume growth. This product defines a single-peaked development of road traffic deaths, where its peak reaches earlier the sooner and faster a nation or region motorizes massively. Since in developing countries long series of traffic volume data are absent, another model for the fit and prediction of road traffic fatalities for developing countries is used, based on the relationships of income level per capita with road traffic mortality. Also this model implies that at some point in time road traffic deaths will start declining for ever, also worldwide. After empirically derived corrections for missing or incomplete data and police under-reporting, it is estimated that 1·2 million deaths and almost 8 million serious injuries are caused by road traffic worldwide in 2000. Using realistic income level predictions the new income-dependent model predicts markedly later and higher fatality peaks than the verified time-dependent model. It might be assumed that the developing countries could learn faster to increase their road safety by knowledge transfer from developed countries. Four prediction scenarios are specified for modified income-dependent models of road traffic death and serious injury developments up to 2050. Depending on the scenario the world total of road fatalities begins to reduce soon or only after 2035 with a global peak of about 1·8 million road traffic deaths, where the national fatality reduction starts later the lower the national income per capita is. Without the potentially achievable learning scenario the road fatality reductions in developed countries may not be enough to compensate the road fatality increases in developing countries, while road fatality increases may even occur after 2060 in countries with the lowest levels of income per capita.     

Analysis of progress in road safety in ten European countries

Classic models for the long-term forecasting of the number of fatalities in road accidents are based on a decreasing exponential form of the rate of fatalities per vehicle×km. We decided to extend this simple model to incorporate intervention functions connected with the major safety measures introduced and to replace the deterministic trend by a stochastic one. Harvey's structural model, known as the local linear trend model, is applied to ten European countries. The relationship between the slope of this trend and the elasticity in terms of the number of vehicles×km yields an indicator of the rate of progress in road safety made in the different countries. The average rate is around −6% per annum, with a minimum of −4.5% and a maximum of −13.5% for Spain in 1994. Europe's road systems can thus absorb a 6% increase in traffic per annum while maintaining the number of fatalities constant.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

The turning point in the number of traffic fatalities: Two hypotheses about changes in underlying trends

The number of traffic fatalities reached a peak in many highly motorised countries around 1970. Some previous studies have suggested that the turning point in the number of traffic fatalities was inevitable and did not reflect a change in the underlying trends influencing the number of traffic fatalities. Other studies suggest that trends in traffic growth and fatality rate changed from before to after the turning point. This paper proposes two hypotheses about the turning point in the number of traffic fatalities. One hypothesis is that the long-term trends in traffic growth and fatality rate were the same before and after the turning point. The other hypothesis is that the long-term trends in traffic growth and fatality rate were different before and after the turning point was reached, in particular that the annual percentage decline in fatality rate became greater after the turning point than before. Such a change would suggest that road safety policy became more effective. Analysis of data for six countries (Denmark, Great Britain, Netherlands, Norway, Sweden, United States) lends stronger support to the latter hypothesis than to the former. The lesson for policy makers, in particular in countries where the number of traffic fatalities is still growing, is that they should not expect a turning point to be reached without policy interventions.     

Traffic fatalities and economic growth

This paper examines the relationship between traffic fatality risk and per capita income and uses it to forecast traffic fatalities by geographic region. Equations for the road death rate (fatalities/population) and its components--the rate of motorization (vehicles/population) and fatalities per vehicle (F/V)--are estimated using panel data from 1963 to 1999 for 88 countries. The natural logarithm of F/P, V/P, and F/V are expressed as spline (piecewise linear) functions of the logarithm of real per capita GDP (measured in 1985 international prices). Region-specific time trends during the period 1963-1999 are modeled in linear and log-linear form. These models are used to project traffic fatalities and the stock of motor vehicles to 2020. The per capita income at which traffic fatality risk (fatalities/population) begins to decline is 8600 US dollars (1985 international dollars) when separate time trends are used for each geographic region. This turning point is driven by the rate of decline in fatalities/vehicles as income rises since vehicles/population, while increasing with income at a decreasing rate, never declines with economic growth. Projections of future traffic fatalities suggest that the global road death toll will grow by approximately 66% over the next twenty years. This number, however, reflects divergent rates of change in different parts of the world: a decline in fatalities in high-income countries of approximately 28% versus an increase in fatalities of almost 92% in China and 147% in India. The road death rate is projected to rise to approximately 2 per 10,000 persons in developing countries by 2020, while it will fall to less than 1 per 10,000 in high-income countries.     

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.     

Estimating the effects of crash phase injury countermeasures—II : The fatality trend and its modification by countermeasures

The trend of automobile occupant fatalities from 1950 to 1968 was studied. The influence of such factors as vehicle age, improved doorlocks, small cars, speed, seat belt use, driver age, and the Interstate Highway System was estimated on the basis of past studies. The relation of 20 other factors to this trend was explored by regression analyses. No satisfactory representation of the trend could be achieved without including either new automobile registration data or the Index of Industrial Production in the independent variables. A hypothesis explaining this was proposed. The number of automobiles, by model year groups corresponding to the availability of crash phase countermeasures, involved in potentially fatal accidents, was projected to 1968. Applying the reduction in fatality risk to these figures, fatality trends with countermeasures are projected to 1980 and compared with a projection assuming no countermeasures.     

Farm work related fatalities among adults in Victoria, Australia: the human cost of agriculture

The purpose of this study was to review the patterns of, and trends in, farm work related fatalities among adults in one Australian state for the period 1985-1996. Fatality data was provided by the Victorian Workcover Authority Health and Safety Division. Trends were determined using regression techniques assuming a Poisson error structure for annual fatality rates. Results showed an annual average of eight deaths. Males, and those 60 years and over, were over-represented, compared to persons employed. Tractor incidents were the most common type of fatality (72%), with tractor roll overs accounting for 61% of all tractor incidents. Non tractor fatalities included being hit by a falling object and transport incidents. Statistical trend analyses revealed a non significant decrease in the tractor roll over fatality rate, and significant increases in the all farm (P = 0.004) and non tractor fatality rates (P = 0.036). The 3 year moving average rate for non tractor farm fatalities has increased to the point where it exceeds that for tractor roll over fatalities, and is approaching that for all tractor fatalities. Changes within the agricultural industry, coupled with the ageing of the farm workforce, appear to placing Victorian farmers at increased risk of farm work related death.     

Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China

A large number of pedestrian fatalities were reported in China since the 1990s, however the exposure of pedestrians in public traffic has never been measured quantitatively using in-depth accident data. This study aimed to investigate the association between the impact speed and risk of pedestrian casualties in passenger vehicle collisions based on real-world accident cases in China. The cases were selected from a database of in-depth investigation of vehicle accidents in Changsha-IVAC. The sampling criteria were defined as (1) the accident was a frontal impact that occurred between 2003 and 2009; (2) the pedestrian age was above 14; (3) the injury according to the Abbreviated Injury Scale (AIS) was 1+; (4) the accident involved passenger cars, SUVs, or MPVs; and (5) the vehicle impact speed can be determined. The selected IVAC data set, which included 104 pedestrian accident cases, was weighted based on the national traffic accident data. The logistical regression models of the risks for pedestrian fatalities and AIS 3+ injuries were developed in terms of vehicle impact speed using the unweighted and weighted data sets. A multiple logistic regression model on the risk of pedestrian AIS 3+ injury was developed considering the age and impact speed as two variables. It was found that the risk of pedestrian fatality is 26% at 50 km/h, 50% at 58 km/h, and 82% at 70 km/h. At an impact speed of 80 km/h, the pedestrian rarely survives. The weighted risk curves indicated that the risks of pedestrian fatality and injury in China were higher than that in other high-income countries, whereas the risks of pedestrian casualty was lower than in these countries 30 years ago. The findings could have a contribution to better understanding of the exposures of pedestrians in urban traffic in China, and provide background knowledge for the development of strategies for pedestrian protection.     

Analysis of road traffic accidents data in Zambia

The article is based on the findings of a research project on “Road traffic problems in Zambia” which was started in January 1974. Trend curves have been defined for the number of road accidents, casualties, fatalities and other relevant quantities, and on the basis of surveys and analysis carried out for other countries, the situation in Zambia has been compared to the international one. The number of vehicles for 10,000 persons has increased from 163 in 1964 to 332 (about one vehicle for every 30 persons) in 1974, an increase of 103%. Road traffic fatalities have also increased by 170%, climbing from 330 in 1964 to 890 in 1974, while the total number of accidents has increased by 194%, reaching the figure of 10,829 by 1974. The number of fatalities per 10,000 vehicles for the year 1974 was 57. The present trend means, in figures, that unless appropriate measures are taken, 1100 road fatalities are to be expected in 1980, 200 more than in 1974. It has been established that the annual number of fatalities per vehicle in Zambia is ten times larger than the average for European countries. Also the ratio of fatalities to number of injured persons is much higher for Zambia than for neighbouring countries and about twice that of the European average.     

Trends in alcohol-impaired driving in Canada

While a general decreasing trend in the number of persons killed in a traffic crash involving a drinking driver has occurred in Canada since the 1980s, it is evident that much of this decrease occurred in the 1990s. Since 2002, less progress has been made as the number of persons killed in crashes involving drinking drivers remains high. To better understand the current situation, this paper describes trends in drinking and driving in Canada from 1998 to 2011 using multiple indicators based on data collected for the Traffic Injury Research Foundation's (TIRF) Road Safety Monitor (RSM), the National Opinion Poll on Drinking and Driving, and trends in alcohol-related crashes based on data collected for TIRF's national Fatality Database in Canada. There has been a continued and consistent decrease in the number of fatalities involving a drinking driver in Canada. This remains true when looking at the number of fatalities involving a drinking driver per 100,000 population and per 100,000 licensed drivers. This decreasing trend is also still apparent when considering the percentage of persons killed in a traffic crash in Canada involving a drinking driver although less pronounced. Data from the RSM further show that the percentage of those who reported driving after they thought they were over the legal limit has also declined. However, regardless of the apparent decreasing trend in drinking driving fatalities and behaviour, reductions have been relatively modest, and fatalities in crashes involving drivers who have consumed alcohol remain high at unacceptable levels.     

Using data linkage to generate 30-day crash-fatality adjustment factors for Taiwan

Different countries have their own police reporting time standards for counting the number of fatalities in reported crashes. A rapid estimation method (such as adjustment factor) for the comparison is important. The data-linkage technique was used to combine police-reported crash data and vital registration data, in order to generate 30-day fatality adjustment factors for various reporting time standards, which could also shed light on the fatal injury trend over time. The major findings were as follows. Firstly, a conservative 30-day fatality adjustment factor for the first day (or 24 h) would be 1.54 (or 1.35) in an area with a large motorcycle population, like Taiwan. This produced 20-40% higher 30-day fatalities than UK Transport Research Laboratory predicted, and 15-25% higher fatalities than those in Europe/Japan. Secondly, after excluding motorcycle impacts, the Taiwanese factors suggested 8-14% higher fatalities within 30 days than those in Europe/Japan. Third, motorcycle fatalities influenced the overall 30-day fatality trend within 3 days. In the future, both the police under-reporting problem and the motorcycle/overall fatal injury pattern within 3 days after crashing in developing countries like Taiwan merit further investigation.     

Combining road safety information in a performance index

In this paper we focus on an essential step in the construction process of a composite road safety performance indicator: the assignment of weights to the individual indicators. In the composite indicator literature, this subject has been discussed for a long time, and no agreement has been reached so far. The aim of this research is to provide insights in the most important weighting methods: factor analysis, analytic hierarchy process, budget allocation, data envelopment analysis and equal weighting. We will give the essential theoretical considerations, apply the methods on road safety data from various countries and discuss their advantages and disadvantages. This will facilitate the selection of a justifiable method. It is shown that the position of a country in the ranking is influenced by the method used. The weighting methods agree more for countries with a relatively bad road safety performance. Of the five techniques, the weights based on data envelopment analysis resulted in the highest correlation with the road safety ranking of 21 European countries based on the number of traffic fatalities per million inhabitants. This method is valuable for the development of a road safety index.     

Use of citation per publication as an indicator to evaluate pentachlorophenol research

The objective of the study was to perform a bibliometric analysis of all pentachlorophenolrelated publications in the Science Citation Index (SCI). Analyzed parameters included document type, language of publication, page count, publication output, authorship, keywords plus, publication pattern, citation and country of publication. The US produced 29% of the total single country publications where the seven major industrial countries accounted for the majority of the total production (66%). An indicator citation per publication was successfully applied in this study to evaluate the impact of number of authors, countries, and journals. The mean value of citation per publication of collaborative papers was higher than that of single country publications. In addition analysis of keywords plus in different period was applied to indicate a research trend.