Although driver distraction can be considered as part of everyday driving it constitutes a basic contributory factor for increased risk for road accidents internationally. Within this content, cell-phone use and conversation with passenger are two critical in-vehicle distraction conditions with respect to driver behavior and safety. The objective of this research is the investigation of the effect of area and traffic conditions on driving performance of drivers while talking on the mobile phone or conversing with the passenger. For this purpose, a large driving simulator experiment is carried out, in which 95 drivers from all different age groups (young, middle aged and older) were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, in low and high traffic. In the next step, an appropriate modelling methodology has been developed, including first descriptive analysis in order to explore the large database. Then generalized linear models as well as generalized linear mixed models regarding average speed and reaction time were implemented in order to estimate the effect of the examined distraction sources as well as area and traffic characteristics on driving behaviour and road safety. Results indicate that female drivers, especially in rural areas, were found to have the worst reaction times, while being distracted (either conversing with a passenger or talking on the cell phone). This is probably explained by the fact that in urban area, the complex road environment alerts the drivers in order to self-regulate their driving to compensate for any decrease in attention to the driving task. Furthermore, regarding average speed, it is observed that in rural areas drivers reduce the speed while distracted either by talking on the mobile phone (older drivers) or by conversing with the passenger (young and middle aged drivers), especially at high traffic volume while in urban areas suggesting a driver's compensatory behaviour. The next steps of the present research could focus on the investigation of the impact of mobile phone use, not only when the drivers talk on mobile phone using a hand-held device, but also when they use a hands-free device, a Bluetooth, or when they type messages.@en

Driving requires possessing cognitive, motor and visual skills and drivers need to have adequate motor strength, coordination and speed. More importantly, cognitive skills including concentration, attention, adequate visual perceptual skills, insight and memory must be present. The normal ageing process leads to declines in these motor and cognitive skills, and when combined with a brain pathology, it may significantly impair the person's driving performance, especially when unexpected incidents occur. The objective of this paper is to identify the critical driving performance measures in which drivers with cognitive impairments significantly deviate from the general population, through a driving simulator experiment. The brain pathologies considered concern Alzheimer's disease (AD), Parkinson's disease (PD) and Mild Cognitive Impairment (MCI). More analytically, a full neurological and neuropsychological assessment was carried out at the “ATTIKON” General Hospital in Athens, by neurologists and neuropsychologists of the UoA. Then, participants drove at the driving simulator of the NTUA. The driving tasks included driving in urban and rural road environment, while various unexpected incidents were scheduled to occur during the drives. The driving performance of 109 drivers with cognitive impairments (MCI, AD or PD) was compared to the driving performance of 31 healthy drivers of similar demographics. For each driver, the following driving performance measures were calculated and examined: speed, speed variability, lateral position, lateral position variability, headway, headway variability, and reaction time. All these parameters were compared to the range of “typical” values of the respective distribution of healthy drivers, and significant deviations from the “typical” distribution were identified. Results indicated that group of patients had various difficulties in driving performance compared with the control drivers. More specifically, more than 50% of the patients had extremely low mean speeds, and they often had lower speed variability (in 40% of the cases) than controls. Regarding the lateral position, the patients had difficulty in positioning the vehicle in the lane, and more than 1 out of 5 patients had very large variability in their lateral position. Also, 44% of the drivers with a brain pathology kept very large mean headways from the vehicle ahead. Finally, 50% of patients had very slow reaction times in unexpected incidents; in rural area 70% of the patients had reaction times larger than 2 seconds.@en

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.@en

Objective: Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Methods: Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. Results: The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. Conclusions: A concluding section providing perspectives and directions for future research is presented.@en

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).@en

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.@en

In this research, the effect of the intensification of police enforcement on the number of road accidents and related fatalities at national and regional level is investigated, focusing on two most important infringements: speeding and drinking-and-driving. Distributional assumptions of the Poisson-family are considered for the counts of road accidents and fatalities of the various regions of Greece. A multilevel analysis technique is then applied to investigate the effect of the intensification of enforcement on the reduction of road accidents in different regions of Greece. Multivariate multilevel models are developed, in order to assess the combined effects of police enforcement in different regions and for different road safety outcomes (accidents with casualties, and fatalities), and to quantify these effects. Results show a significant overall effect of enforcement on both road accidents and fatalities. As regards the regional variation of the effect, which results from different levels of intensified police enforcement per region, a significant difference between accidents and fatalities is identified. In particular, the regional variation of the effect of enforcement on accidents is highly significant, whereas no significant regional variation of the effect on fatalities is identified. The combination of the model results with additional behavioural data led to the conclusion that enforcement intensification has a direct impact on the improvement of driver behaviour and attitude and subsequently on the reduction of road accident and fatalities in Greece.@en

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.@en

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.@en

This paper investigates the effect of the intensification of Police enforcement on the number of road accidents at national and regional level in Greece, focusing on one of the most important road safety violations: drinking-and-driving. Multilevel negative binomial models are developed to describe the effect of the intensification of alcohol enforcement on the reduction of road accidents in different regions of Greece. Moreover, two approaches are explored as far as regional clustering is concerned: the first one concerns an ad hoc geographical clustering and the second one is based on the results of mathematical cluster analysis through demographic, transport and road safety characteristics. Results indicate that there are significant spatial dependences among road accidents and enforcement. Additionally, it is shown that these dependences are more efficiently interpreted when regions are determined on the basis of qualitative similarities than on the basis of geographical adjacency.@en

Purpose The objective of this paper is to provide a parsimonious model for linking motorization level with the decreasing fatality rates observed across EU countries during the last three decades. Methods A macroscopic analysis of road-safety in Europe at the country level is proposed through the application of non-linear models correlating fatalities and vehicles for the period between 1970 and 2002. Given the time series nature of road safety data, these models result in auto-correlated residuals, thus violating at least one of the assumptions of non-linear regression. Autoregressive forms of the considered models that overcome these limitations and provide superior predictive capabilities are also considered. Results An autoregressive log-transformed model seems to outperform the base autoregressive non-linear model in this respect. The use of these models allowed for the identification of the best and worst performing countries. Conclusions The proposed models can prove useful for assessing the road safety performance of the examined countries, as well as for obtaining some insight on the current and future trends of less developed countries.@en

The objective of this research was the analysis of the driving performance of drivers with Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD), in different road and traffic conditions, on the basis of a driving simulator experiment. In this experiment, healthy “control” drivers, patients with MCI, and patients with AD, drove at several scenarios at the simulator, after a thorough neurological and neuropsychological assessment. The scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of healthy and impaired drivers was analysed and compared by means of Repeated Measures General Linear Modelling techniques. A sample of 75 participants was analysed, out of which 23 were MCI patients and 14 were AD patients. Various driving performance measures were examined, including longitudinal and lateral control measures. The results suggest that the two examined cerebral diseases do affect driving performance, and there were common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, cognitively impaired drivers drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. The group of patients had difficulties in all road and traffic environments, and especially when traffic volume was high. Most importantly, both cerebral diseases appear to significantly impair reaction times at incidents. The results of this research suggest that compensatory behaviours developed by impaired drivers are not adequate to counterbalance the direct effects of these cerebral diseases on driving skills. They also demonstrate that driving impairments increase as cognitive impairments become more severe (from MCI to AD).@en