Prof. D.Sc. Péter Holló; KTI, Hungary Vojtech Eksler, PhD ; CDV, Czech Republic

1. 16&17 November, 2009 ‘To which extent Road Safety Performance Indicatorsallow to explain road safety development:A critical view based on the experience of Central European Countries’ Prof. D.Sc. Péter Holló; KTI, Hungary Vojtech Eksler, PhD; CDV, Czech Republic Joanna Zukowska, PhD; GUT, Poland

2. „Iron curtain” brought down two decades ago • - 20 years later, countries struggling to satisfactory reduce road toll • - Road mortality in EU-10 in 1990 only 12% higher thanin EU-15, in 2008, 95% higher... • EU enlargement contributed to this trend • How to understand the development and what it unveils? -55% -30% -45% Background -20%

3. Lesson 1 • Unprecedented rise in vehicular travel, vehicle ownership cannot fully explain the development in road safety since 1980 • General culture and organizational structure could be the key • Two aspects: 1. Rise started in 1986 alongside with social climate • 2. Peak reached in different time frames Road deaths (1= avg (1979,1980,1981))

4. Lesson 1 • Singular Value Decomposition (SVD) of trends in road deaths since 1980 in 9 CEEC together with trends observation points to significant differences in trend • Countries with mass privatization faced to significant rise in mortality (e.g. speed of structural changes in society is a major determinant of the trend) • Social changes, together with development of accountability and professionalism could explain different trends Big-bang approach – East Germany, Poland, Baltic countries, Hungary Gradual approach – Slovenia, Czechoslovakia, Poland DIM1 – SVD analysis of trends in road deaths Factors Cumulative proportion of variation -------------------------------------------- Dim1 67% Dim2 87% Dim3 92%

5. Lesson 1 Similarly, Delorme and Lassarre (2008) noted that the three major risk factors (speed, alcohol, non-wearing of safety belts) explain 80 to 90% of the gap in risk between France and Great Britain. So even when accounted for basic structural differences, there is still a void in knowledge on the factors causing the difference in risk levels. Commander and Oppe (in Wegman et al., 2008) noticed the existence of constant differences in risk levels between countries over time. Based on a singular value decomposition (SVD) analysis of road fatality risk development (killed per vehicle kilometers) in 11 Western European countries, they concluded, that there is one common trend behind all country specific trends and that despite shrinking absolute differences in fatality risk between countries over time, the relative differences have stayed the same over time. They have further noted that country specific temporal deviations from this trend cannot be explained by the development in vehicle kilometres as it is often suggested.

6. Lesson 1 There is a need to consider other underlying factors in road safety management.

7. Lesson 1 Road safety pyramid translated into the chain, now include organizational and strategic aspects of road safety governance. Local RS plans RS agency, observatory Accountability, professionalism Vision Zero Sustainable safety

8. Lesson 2 In some cases, reduction in the number of accidents (which seems to indicate an improving road safety situation) has in fact deteriorating performance indicators in the background (1), or sometimes improving road safety situation cannot be explained by any particular road safety measures (2) Example case (1) In the period from 1990 to 2000, road safety improved spectacularly in Hungary.

9. Lesson 2 Number of road motor vehicles, accidents with personal injuries and the number of related fatalities between 1976 and 2008 in Hungary

10. Lesson 2 At the same time the wearing rate of safety belts decreased significantly. The observed safety belt wearing rate in the front and back passenger car seats in 1992-2008

11. Lesson 2 All this shows well, that although the general road safety situation improved to a great extent, the work of those who were “responsible” for increasing the safety belt wearing rate (police enforcement, information campaigns) was not really efficient; consequently the success was not the fruit of their activity, but it was due to other factors. In this case the improving road safety situation has obscured a deteriorating sub-trend (safety belt wearing)

12. Lesson 2 The distribution of people killed due to road accidents by their mode of participation in traffic (1984-2008) According to the Figure, it can be stated, that the proportion of passenger car fatalities within the total number of accident fatalities in 1990-2000 – contrary to the decreasing safety belt wearing rates – did not grow, moreover, it rather decreased than increased. This may be due to many factors, however further research, an in-depth analysis is necessary in order to reveal the factors behind this change. Presumably, the up-to-date bodyworks offering a greater passive safety, the wider use of airbags, the decennial progress of the rescue work and the medicine contributed also to the fact that the number of persons killed in passenger cars did not increase despite the decreasing safety belt wearing rates.

13. Lesson 2 Greatest changes in road safety in Hungary Number of killed as a result of road accidents inside and outside built-up areas In the Figure it can be seen clearly without any detailed analysis that in the period following the change of the political and social system, the biggest improvement (1993) and the biggest deterioration (2002) in the number of road accident fatalities were accompanied with the changes in speed limits.

14. Lesson 2 Changes in speed limits in Hungary 1993 60 km/h → 50km/h inside built-up areas 2001 80 km/h → 90 km/h outside built-up areas (rural roads) 100 km/h → 110 km/h semi-motorways 120 km/h → 130 km/h motorways

15. Lesson 2 Monthly number of road fatalities in Hungary outside built-up areas The Figure shows an important result of a time series analysis using the ARIMA model. It clearly shows the break in the declining trend of road fatalities outside built-up areas after the introduction of the increased speed limit (Holló, 2006.)

16. Lesson 2 In accordance with the earlier research results (Nilsson, 2004) (Elvik, 2004) and experience, the decrease of the speed limit has led to positive, whereas the increase of the speed limit has led to negative effects on road safety in Hungary, too.

17. Lesson 3 Sudden, short-lived drop in road fatalities trend in Poland

18. Questionaskedin Poland in the beginning of 2002: Is this a permanent trend of road fatalities? Lesson 3

19. In fact there was no evidence for permanent drop of road fatalities trend after 2001 since there were no extra road safety countermeasures introduced in Poland at that time Lesson 3

20. Lesson 3 Question: What was the reason of the drop? Thesis: The short-term economic crisis observed in Poland in that time could affect the mobility and through this the number of fatalities Tool to prove it: Structural time-series modeling

21. Fatalities and GNP in Poland - is there a relation between them? Lesson 3

22. Lesson 3 Components of thestructural time-series: Proposed model: ln(Fatalities) = Trend + Fixedseasonal + Explvars + Irregular Estimationsample: 1997 – 2008 Explanatoryvariable: GNP

23. Lesson 3 Result of the modeling: Estimated coefficient of expl. var.=1,11 (1% of GNPs growth leads, other things being equal, to an increase of no. of fatalities of 1,11) Interpretation: There is a relationship between GNP and the number of observed road fatalities

24. Conclusions – The performance indicators (PIs) establish relationships between the consequences of road accidents and the preventive measures. – Collection, publication and international comparison of PIs are contributing to • better understanding of accident/injury processes • identification of “bottlenecks” • elaboration of new safety strategies – Despite the good results, PIs need to be developed further in order to explain all factors influencing road safety outcomes.