Eleonora Papadimitriou

When may road fatalities start to decrease

INTRODUCTION The comparative analysis of macroscopic trends in road safety has been a popular research topic. The objective of this research is to propose a simple and, at the same time, reliable multiple regime model framework for international road safety comparisons, allowing for the identification of slope changes of personal risk curves and respective breakpoints. METHOD The trends of road traffic fatalities in several EU countries have been examined through the temporal evolution of elementary socioeconomic indicators, namely motorized vehicle fleet and population, at the country level. RESULTS Piece-wise linear regression models have been fitted, using a methodology that allows the simultaneous estimation of all slopes and breakpoints. The number and location of breakpoints, as well as the slope of the connecting trends, vary among countries, thus indicating different road safety evolution patterns. IMPACT ON INDUSTRY Macroscopic analysis of road accident trends may be proved beneficial for the identification of best examples and the implementation of appropriate programmes and measures, which will lead to important benefits for the society and the economy through the reduction of road fatalities and injuries. Best performing countries and the related programmes and measures adopted may concern several safety improvements at the processes of the road, the vehicle and the insurance industries. CONCLUSIONS Lessons from the analysis of the past road safety patterns of developed countries provide some insight into the underlying process that relates motorization levels with personal risk and can prove to be beneficial for predicting the road safety evolution of developing countries that may have not yet reached the same breakpoints. Furthermore, the presented framework may serve as a basis to build more elaborate models, including more reliable exposure indicators (such as vehicle-km driven).

On statistical inference in time series analysis of the evolution of road safety

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research.

Estimation of Fatality and Injury Risk by Means of In-Depth Fatal Accident Investigation Data

Objective: In this article the factors affecting fatality and injury risk of road users involved in fatal accidents are analyzed by means of in-depth accident investigation data, with emphasis on parameters not extensively explored in previous research. Methods: A fatal accident investigation (FAI) database is used, which includes intermediate-level in-depth data for a harmonized representative sample of 1300 fatal accidents in 7 European countries. The FAI database offers improved potential for analysis, because it includes information on a number of variables that are seldom available, complete, or accurately recorded in road accident databases. However, the fact that only fatal accidents are examined requires for methodological adjustments, namely, the correction for two types of effects on a road users baseline risk: “accident size” effects, and “relative vulnerability” effects. Fatality and injury risk can be then modeled through multilevel logistic regression models, which account for the hierarchical dependences of the road accident process. Results: The results show that the baseline fatality risk of road users involved in fatal accidents decreases with accident size and increases with the vulnerability of the road user. On the contrary, accident size increases nonfatal injury risk of road users involved in fatal accidents. Other significant effects on fatality and injury risk in fatal accidents include road user age, vehicle type, speed limit, the chain of accident events, vehicle maneuver, and safety equipment. In particular, the presence and use of safety equipment such as seat belt, antilock braking system (ABS), and electronic stability program (ESP) are protection factors for car occupants, especially for those seated at the front seats. Conclusions: Although ABS and ESP systems are typically associated with positive effects on accident occurrence, the results of this research revealed significant related effects on accident severity as well. Moreover, accident consequences are more severe when the most harmful event of the accident occurs later within the accident chain.

Mobility Patterns of Motorcycle and Moped Riders in Greece

This paper investigates the mobility patterns of powered two-wheeler riders in comparison with those of passenger car drivers in Greece, a country in which an increased rate of two-wheeler ownership and related traffic is observed. A nationwide travel survey targeted at active two-wheeler and passenger car drivers was carried out for that purpose. The results of the survey were exploited in two ways. First, the use of the vehicle types examined in Greece was investigated in relation to driver characteristics by calculation of the respective sample distributions. The results demonstrated a clear difference between vehicle ownership rates and vehicle use rates by vehicle type. Moreover, the mobility patterns of each vehicle type were compared on the basis of the average yearly mileage traveled in relation to driver (age, gender, experience), vehicle (engine size), type of trip (weekday or weekend), and road environment (area type, lighting conditions, road type). The findings indicated that driver gender, age, and experience appeared to be a stronger determinant of mobility patterns than vehicle type. But different mobility patterns among vehicle types in different road environments were identified; this suggests that mopeds and motorcycles are preferred for particular types of trips (e.g., traveling in residential areas and weekdays during the daytime), whereas passenger cars may be used in all cases.

Multilevel analysis in road safety research

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.

Road Casualties and Enforcement: Distributional Assumptions of Serially Correlated Count Data

Objective. Road safety data are often in the form of counts and usually temporally correlated. The objective of this research is to investigate the distributional assumptions of road safety data in the presence of temporal correlation. Methods. Using the generalized linear model framework, four distributional assumptions are considered: normal, Poisson, quasi-Poisson and negative binomial, and appropriate models are estimated. Monthly casualty and police enforcement data from Greece for a period of six years (January 1998–December 2003) have been used. The developed models include sinusoidal latent terms to capture the temporal serial correlation of observations. Several statistical goodness-of-fit diagnostic tests have been performed for the results of the estimated models, and the predictive capabilities of the models are investigated. Results. The residuals of the quasi-Poisson and negative binomial models do not show any serial correlation. The signs of the estimated coefficients for all models are consistent and intuitive. In particular, a negative coefficient value for the number of breath alcohol controls indicates that the number of persons killed and seriously injured decreases as the intensity of breath alcohol controls increases. The Poisson model fails to capture the overdispersion in the data, thus underestimating the standard errors of the estimated coefficients. Conclusion. The results suggest that the quasi-Poisson and negative binomial outperform the normal and Poisson models in this application. The findings of this research demonstrate a clear link between the intensification of police enforcement and the reduction of traffic accident casualties. In particular, an increase in the number of breath alcohol controls in Greece after 1998 contributed to a reduction in the number of persons killed and seriously injured from traffic accidents.

Exposure data and risk indicators for safety performance assessment in Europe

The objective of this paper is the analysis of the state-of-the-art in risk indicators and exposure data for safety performance assessment in Europe, in terms of data availability, collection methodologies and use. More specifically, the concepts of exposure and risk are explored, as well as the theoretical properties of various exposure measures used in road safety research (e.g. vehicle- and person-kilometres of travel, vehicle fleet, road length, driver population, time spent in traffic, etc.). Moreover, the existing methods for collecting disaggregate exposure data for risk estimates at national level are presented and assessed, including survey methods (e.g. travel surveys, traffic counts) and databases (e.g. national registers). A detailed analysis of the availability and quality of existing risk exposure data is also carried out. More specifically, the results of a questionnaire survey in the European countries are presented, with detailed information on exposure measures available, their possible disaggregations (i.e. variables and values), their conformity to standard definitions and the characteristics of their national collection methods. Finally, the potential of international risk comparisons is investigated, mainly through the International Data Files with exposure data (e.g. Eurostat, IRTAD, ECMT, UNECE, IRF, etc.). The results of this review confirm that comparing risk rates at international level may be a complex task, as the availability and quality of exposure estimates in European countries varies significantly. The lack of a common framework for the collection and exploitation of exposure data limits significantly the comparability of the national data. On the other hand, the International Data Files containing exposure data provide useful statistics and estimates in a systematic way and are currently the only sources allowing international comparisons of road safety performance under certain conditions.

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries.

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.

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 statistical analysis of the impact of advertising signs on road safety

This research aims to investigate the impact of advertising signs on road safety. An exhaustive review of international literature was carried out on the effect of advertising signs on driver behaviour and safety. Moreover, a before-and-after statistical analysis with control groups was applied on several road sites with different characteristics in the Athens metropolitan area, in Greece, in order to investigate the correlation between the placement or removal of advertising signs and the related occurrence of road accidents. Road accident data for the ‘before’ and ‘after’ periods on the test sites and the control sites were extracted from the database of the Hellenic Statistical Authority, and the selected ‘before’ and ‘after’ periods vary from 2.5 to 6 years. The statistical analysis shows no statistical correlation between road accidents and advertising signs in none of the nine sites examined, as the confidence intervals of the estimated safety effects are non-significant at 95% confidence level. This can be explained by the fact that, in the examined road sites, drivers are overloaded with information (traffic signs, directions signs, labels of shops, pedestrians and other vehicles, etc.) so that the additional information load from advertising signs may not further distract them.