Appendix A. Identified detailed causalities per priority indicator Appendix B. Overview of the SafeFITS database
Appendix C. Groups of countries
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Appendix A. Identified detailed causalities per priority indicator Table A.1 Identified relations regarding indicator I01 ("GDP per capita") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable TypeEquation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative level Road typesRoad user typesTime period I01GDP per capitaEconomy & ManagementRoad safety managementarithmetic value for low GDP per capita (< ~3.000$): => increase of GDP => increase in fatality rates for high GDP per capita (> ~3.000$): => increase of GDP => decrease in fatality rates
Individual effects5%not applicable not applicable Economic development and traffic accident mortality in the industrialized world, 1962-19902000van Beeck E., Borsboom G., Mackenbach J.
International Journal of Epidemiology, 29 (2000), 503-509.
21 O ECD countriesnational allall1962- 1990 I01GDP per capitaEconomy & ManagementRoad safety managementarithmetic valueAssociation of GDP per capita changes to fatalities per capita changes (Table 3.2.1.1)Statistical Modeladjusted R2 = 0.8695Log-linear regression - GDP per capita - Geographic regionTraffic fatalities and economic growth.2005Kopits E., Cropper M.Accident Analysis and Prevention 37 (2005), 169- 178.
88 c
ountriesnational allall1963- 1999 I01GDP per capitaEconomy & ManagementRoad safety managementarithmetic valueAssociation of GDP per capita changes to annual fatalities per capita changes (Table 3.2.1.2)Statistical Modeln/a Mixed linear regression - Change in GDP per capita - Geographic regionEffect of GDP changes on road traffic fatalities 2014Yannis G., Papadimitriou E., Folla K. Safety Science 63 (2014) 42- 4927 European countriesnational allall1975- 2011 Table A.2 Identified relations regarding indicator I02 ("Country has a national road safety strategic plan") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable TypeEquation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I02Country has a national road safety strategic planEconomy & ManagementRoad safety managementyes / noyes => -5% in fatalities per yearIndividual effects5%not applicable not applicable Updates of Road Safety Status in Malaysia 2005
Um ar R.IATSS Research, Vol.29 (2005) No.1, 106-108 M1972- alaysia national allall2000 Country has a nationalDaCoTA Deliverable 1.5 Vol.II.: Economy &Road safety Statistical notPapadimitriou E. I02road safety strategic yes / nono direct statistical relationshipn/a not applicable Analysis of Road Safety Management in2012ManagementmanagementModelapplicable et al. planthe European countries
R30 European 2001- esearch Project DaCoTA national allallcountries2010 Table A.3 Identified relations regarding indicator I03 ("Country has time-based, quantified national road safety targets") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTSType ofAdminis- Road Variable Type ofLevel of Explanatory variables Issue Road Time no.Description LayerPillar Equation / Relationstatistical Title AuthorPublisher Origintrativeuser2 Typestudysignificance / Rof statistical model Date typesperiodmodel leveltypes Country has time- based, quantified I03national road safety targets
Economy & Management Road safety managementyes / noinconclusiveIndividual effectsn/a not applicable not applicable Aktion „Minus 10 Prozent“ in Österreich1987 Risser R., Michalik C. Forshnungsbericthe der Bundesanstalt für Strassenwesen, Bereich Unfallforshung, Heft 159Austria national allall1980's I03Country has time- based, quantified national road safety targets
Economy & Management Road safety managementyes / noyes => -10% in injury accidents the 1st year, gradually no effect Individual effectsn/a not applicable not applicable The target –10% programme 1989Lebrun D. PTRC Summer Annual Meeting (proceedings of seminar H), 163-169France national allall1980's I03Country has time- based, quantified national road safety targets
Economy & Management Road safety management yes / noyes => 2% increase in injury accidents and 3% increase in fatal accidents Individual effectsnot significantnot applicablenot applicable The TAG-1 model for France 2000Jaeger L., Lassarre S.
Structural Road Accident Models. The International DRAG Family, 157-184. Oxford, Pergamon Press (Elsevier Science) France national allall1980's I03Country has time- based, quantified national road safety targets
Economy & Management Road safety managementyes / noyes => -8.2 in injury accidents (one year) Individual effectsn/a not applicable not applicable Erhöhung der Verkehrssicherheit nach Plan. Realistischer Ansatz oder perspektivlose Träumerei?1990Schlabbach, K. Zeitschrift für Verkehrssicherheit, 36, 146- 155.
Germany (city of Darmstadt) local allall1980's I03Country has time- based, quantified national road safety targets
Economy & Management Road safety managementyes / noyes => -0.77% in fatalities per yearIndividual effects5%not applicable not applicable Quantified road safety targets. An 2001Elvik R. assessment of evaluation methodology Report 539. Institute of Transport Economics, Oslo 14 O
ECD countriesnational allall1970- 1998 I03Country has time- based, quantified national road safety targets
Economy & Management Road safety managementyes / noyes => -0.92% in fatalities per yearIndividual effects1%not applicable not applicable Association between setting quantified road safety targets and road fatality reduction.2006Wong S.C., Sze N.N., Yip H.F., Loo B.P.Y., Hung W.T., Lo H.K.
Accident Analysis and Prevention 38 (2006), 997- 1005.
9 O ECD countriesnational allall1981- 1999 I03Country has time- based, quantified national road safety targets
Economy & Management Road safety managementyes / nono direct statistical relationshipStatistical Modeln/a not applicable not applicable DaCoTA Deliverable 1.5 Vol.II.: Analysis of Road Safety Management in the European countries2012 Papadimitriou E. Research Project DaCoTA et al. 30 E
uropean countriesnational allall2001- 2010 Table A.4 Identified relations regarding indicator I04 ("Country has a clearly empowered agency leading road safety") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable TypeEquation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I04Country has a clearly empowered agency leading road safety Economy & ManagementRoad safety managementyes / nono direct statistical relationshipStatistical Modeln/a not applicable not applicable DaCoTA Deliverable 1.5 Vol.II.: Analysis of Road Safety Management in the European countries2012Papadimitriou E. et al. Research Project DaCoTA 30 European countriesnational allall2001- 2010
A pp endix A. I den tified detailed c ausalities p er priorit y indic at or
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Table A.5 Identified relations regarding indicator I05 ("Country has a defined allocation of expenditure for dedicated road safety programmes") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable Type Equation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I05Country has a defined allocation of expenditure for dedicated road safety programmes Economy & Management Road safety managementyes / nono direct statistical relationshipStatistical Modeln/a not applicable not applicable DaCoTA Deliverable 1.5 Vol.II.: Analysis of Road Safety Management in the European countries2012 Papadimitriou E. Research Project DaCoTA et al. 30 E
uropean countriesnational allall2001- 2010 Table A.6 Identified relations regarding indicator I06 ("Share of trips / traffic per mode") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable Type Equation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisherOriginAdminis- trative levelRoad typesRoad user typesTime period I06Percentage of traffic per modeTransport demand & Exposure
Road safety managementyes / no (existence of public transport) no => +18% in injury accidents Individual effects 95% confidence intervals: -1% to +41%not applicable not applicable Changes in the road accident pattern as a result of a strike at the municipal public transport undertaking in The Hague.
1982
Boot M., Wassenberg P., van Zwam H. SWOV Institute for Road Safety Research. Proceedings of Seminar on Short-term and Area-wide Evaluation of Road Safety Measures, Amsterdam, April 19–21, 1982 The Neturban herlandslocal allunknown roads(Hague) Transport Percentage ofI06demand & traffic per modeExposure
Road safety managementpercentage change-14% in public transport => +4% in number of injuriesIndividual effects95% confidence intervals: +3% to +6%not applicable not applicable Road Casualties and Public Transport Fares in London.1986Allsop R. & Turner E.Accident Analysis and Prevention Vol. 18 No.2, pp. 147-156 Uurban K (London)local allroads 1978
-1983 I06Percentage of traffic per modeTransport demand & Exposure
Road safety management percentage Fatalities estimated according Risk-analysis Statistical n/a to model (direct model (Case Fatality Modelequation not avaialble)Ratios) - probability of a crash per mode - probability of fatality in the event ofa crashpermode - percentage of traffic per mode - transport growth
A Risk-Based Method for Modeling Traffic Fatalities. 2007Bhalla K., Ezzati M., Mahal A., Salomon J., Reich M. (2007).
Risk Analysis, Vol. 27, No. 1 (2007), pp. 125-136 mostly low- and middle- income countriesnational allallunknown I06Percentage of traffic per modeTransport demand & Exposure
Road safety managementannual public transit trips per capita Annual fatalities per capita estimated according to graph of Figure 3.2.6.1 Statistical ModelR2 = 0.6391Graphical Curve Fit -Safer Than You Think! annual transit trips per capitaRevising the Transit Safety 2015 Narrative.
Litman T.
V35 North urban ictoria Transport Policy Institute (VTPI) American local allunknown roadsCities Table A.7 Identified relations regarding indicator I07 ("Country has a target to eliminate high-risk roads") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar
VType ofAdminis- Road Type ofLevel of Explanatory variables Issue Road Time ariable Type Equation / Relationstatistical Title AuthorPublisher Origintrative user2 study significance / Rof statistical model Date typesperiodmodel leveltypes Country has a target to Economy &Road no quantitative relation identified in I07yes / noeliminate high-risk roadsManagementInfrastructureliterature Table A.8 Identified relations regarding indicator I08 ("Number of passenger cars per 1,000 inhabitants") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTSLevel of Variable Type ofno.Description LayerPillar Equation / Relationsignificance /Typestudy2 R Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I08
Number of passenger cars Economy &arithmetic Vehicle per 1000 inhabitants Managementvalue as2 sociation of motorization rate Statistical R= 0.747 to Lognormal to fatalities (Table 3.2.8.1)Model0.940regression - Number of registered vehicles - % of PTWs - Population (different model per country or countries group)
An exploration of road safety parameters in Greece and Turkey2007Yannis G., Laiou A., Vardaki S., Kanellaidis G.
Journal of Transport and Shipping, Issue 4 (December 2007), 125-134
Greece, Turkey
1985- and 20 Eurnational allallopean 2004 Countries I08
Nassociation of motorization rate Piece-wiseumber of passenger cars Economy &arithmetic Statistical varying perVehicle to fatalities per capita (Figure linear per 1000 inhabitants ManagementvalueModelcountry3.2.8.1)regression - Number of vehicles per 1,000 population (different model per country) WYannis G., hen may road fatalitiesAntoniou C., 2011Papadimitriou E., start to decrease? Katsohis D.
Journal of Safety Research, Vol. 42, Issue 1, February 2011, 17-25 8 E
uropean Countriesnational allall1960- 2005 Table A.9 Identified relations regarding indicator I09 ("Share of powered two-wheelers in the vehicle fleet") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable Type Equation / RelationType of studyLevel of significance / R2 Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I09
SIncrease of PTW share from 23.4% to hare of powered two- Economy &Individual notVehicle percentage 63.2% resulted in increase of PTWnot testednot applicable wheelers in the vehicle fleetManagementeffectsapplicable fatalities by 5.5 times. MZhang J., Norton otorcycle ownership and injury in R., Tang K.C., Lo2004ChinaS.K., Jiatong Z., Wenkui G.
Injury Control & Safety Promotion, 11 (2004), 159 - 163Chinanational allPTWs1987- 2001 Possible influence of other factors has not been statistically controlled.
I09 Share of powered two- wheelers in the vehicle fleetEconomy & Management Ve
hicle
per centage association of share of PTWs in the vehicle fleet to fatalities (Table 3.2.9.1)Statistical Model 2 R
= 0.747 to 0.940Lognormal regression - Number of registered vehicles - % of PTWs - Population (different model per country or countries group)
An exploration of road safety parameters in Greece and Turkey 2007
Yannis G., Laiou A., Vardaki S., Kanellaidis G.
Journal of Transport and Shipping, Issue 4 (December 2007), 125-134 Greece, Turkey and 20 European Countries nat
ional
all all 1985- 2004
106
Table A.10 Identified relations regarding indicator I10 ("Country has a comprehensive helmet use law") SafeFITS MODEL PRIORITY INDICATORIDENTIFIED DETAILED CAUSALITYREFERENCE / SOURCE OF DETAILED CAUSALITY DATA USED COMMENTS no.Description LayerPillar Variable TypeEquation / RelationType of studyLevel of significance / R2
Type of statistical model Explanatory variables of statistical model Title Issue Date AuthorPublisher OriginAdminis- trative levelRoad typesRoad user typesTime period I10
Country has a Economy &comprehensive Useryes / noManagementhelmet use law yes => -10% in PTWIndividual notfatalities per 10,000 not significantnot applicable effectsapplicable registered PTWs The reinstated comprehensive motorcycle helmet law in Texas. Insurance Institute for Highway Safety
1992
Mounce N., Brackett Insurance Institute for Highway1985- Possible influence of other factors has not been Q., Hinshaw W., Lund USA (Texas) local allPTWsSafety. Arlington VA 1990statistically controlled.A., Wells J. I10
Cyes => - 11% in PTWountry has a Economy &serious injuries & fatalitiescomprehensive Useryes / noManagementper 10,000 registered helmet use lawPTWs Individual effects5%not applicable not applicable The reinstated comprehensive motorcycle helmet law in Texas. Insurance Institute for Highway Safety
1992
Mounce N., Brackett Insurance Institute for Highway1985- Possible influence of other factors has not been Q., Hinshaw W., Lund USA (Texas) local allPTWsSafety. Arlington VA 1990statistically controlled.A., Wells J. Country has a no => +21.3% in PTWEconomy &Individual notFlorida’s motorcycle helmetAmerican Journal of Public USA 1994- Possible influence of other factors has not been I10comprehensiveUseryes / nofatalities per 10,000 5%not applicable 2004Muller A. local allPTWsManagementeffectsapplicable law repeal and fatality ratesHealth, 94 (2004), 556 – 558(Florida)2001statistically controlled.helmet use lawregistered PTWs Country has a no => +20.8% in PTWFlorida Motorcycle HelmetTurner P., Hagelin C., Economy &Individual notI10comprehensive Useryes / nofatalities per 10,000 not testednot applicable Use: Observational Survey2004Chu X., Greenman Managementeffectsapplicable helmet use lawregistered PTWs and Trend Analysis M., Read J., West M. Center for Urban Transportation Research - University of South FloridaUSA (Florida)local allPTWs1999- 2001Possible influence of other factors has not been statistically controlled. I10Country has a comprehensive helmet use lawEconomy & ManagementUseryes / nono => +13.8% in PTW fatalities per 100 million PTW Vehicle Miles Travelled
Individual effectsnot testednot applicable not applicable Florida Motorcycle Helmet Use: Observational Survey and Trend Analysis 2004Turner P., Hagelin C., Chu X., Greenman M., Read J., West M.
Center for Urban Transportation Research - University of South Florida US
A (Florida)local allPTWs1999- 2001 I10Country has a comprehensive helmet use lawEconomy & ManagementUseryes / nono => +9.1% in PTW fatalities (controlled for no. of registered PTWs and for nationwide trends)
Individual effects1%not applicable not applicable Evaluation of the repeal of the all-rider motorcycle helmet law in Florida2005 UNational Highway Trafficlmer R., Northrup Safety Administration V.S.(NHTSA), Washington DC US
A (Florida)local allPTWs1998- 2002 I10Country has a comprehensive helmet use lawEconomy & ManagementUseryes / nono => +21% in PTW fatalities Individual effectsnot testednot applicable not applicable Evaluation of motorcycle helmet law repeal in Arkansas and Texas2000Preusser D.F., Hedlund J.H., Ulmer R.G.
National Highway Traffic Safety Administration (NHTSA), Washington DC USA (Arkansas) local allPTWs1996- 1998Possible influence of other factors has not been statistically controlled. I10Country has a comprehensive helmet use lawEconomy & ManagementUseryes / nono => +31% in PTW fatalities Individual effectsnot testednot applicable not applicable Evaluation of motorcycle helmet law repeal in Arkansas and Texas2000Preusser D.F., Hedlund J.H., Ulmer R.G.
National Highway Traffic Safety Administration (NHTSA), Washington DC
U1996- Possible influence of other factors has not been SA (Texas) local allPTWs1998statistically controlled. Country has a National Highway Trafficno => +37.5% in PTWEvaluation of the Repeal ofEconomy &Individual notUlmer R.G. &USA 1996- Possible influence of other factors has not been I10comprehensive Useryes / nofatalities per 10,000 not testednot applicable Motorcycle Helmet Laws2003Safety Administration local allPTWsManagementeffectsapplicable Preusser D.F. (Kentucky) 2000statistically controlled.in Kentucky and Louisianahelmet use lawregistered PTWs (NHTSA), Washington DC Country has a no => +75% in PTWEvaluation of the Repeal ofNational Highway TrafficEconomy &Individual notUlmer R.G. &USA 1996- Possible influence of other factors has not been I10comprehensive Useryes / nofatalities per 10,000not testednot applicable 2003Safety Administration local allPTWsMotorcycle Helmet LawsManagementeffectsapplicable Preusser D.F. (Louisiana)2000statistically controlled.in Kentucky and Louisianahelmet use lawregistered PTWs (NHTSA), Washington DC I10
Country has a Economy &comprehensive Useryes / noManagementhelmet use law yes => -25% in PTWIndividual notfatalities per 10,000 5%not applicable effectsapplicable registered PTWs Impact of a helmet law on two wheel motor vehicle crash mortality in a southern European urban area
2000
Ferrando J., Injury Prevention 6 (3), 184- Spain urban 1990- Plasencia A., Oros M., local PTWs1881995(Barcelona)roadsBorrell C., Kraus J.
1. Possible influence of other factors has not been statistically controlled. 2. Restricted to urban areas only. yes => - 31.7% in PTWCountry has a Economy &traumatic brain injury (TBI) I10comprehensive Useryes / noManagementadmissions per 10,000 helmet use lawregistered PTWs
Individual effects0.1%not applicable not applicable Effect of Italy’s motorcycle helmet law on traumatic brain injuries2003Servadei F., Begliomini C., Gardini E., Giustini M., Taggi F., Kraus J.
InjItaly ury Prevention 9 (2003), 1999- (Romagnalocal allPTWs257–2602001region) 1. Effects refer to number of PTW-related traumatic brain injury (TBI) admissions (not fatalities). Country has a Economy &I10comprehensive Useryes / noManagementhelmet use law
yeThe Effect of the Taiwan s => - 33% in PTW head Individual notMotorcycle 0.1%not applicable 2000injurieseffectsapplicable Helmet Use Law on Head Injuries Chiu W.T., Kuo C.Y., Hung C.C., Chen M. A1996- merican Journal of Public Taiwannational allPTWsHealth, 90 (2000), 793 – 7961998 1. Effects refer to number of motorcycle-related head injuries (not fatalities). 2. Possible influence of other factors has not been statistically controlled. I10Country has a comprehensive helmet use lawEconomy & ManagementUseryes / noyes => - 14% in PTW fatalities Individual effects1%not applicable not applicable Effect of the mandatory helmet law in Taiwan1999Tsai M.-C., Hemenway D. Injury Prevention 5 (1999), 290-291
T1996- Possible influence of other factors has not been aiwannational allPTWs1997statistically controlled. Country has a Ichikawa M., Economy &yes => -20.8% in fatalitiesIndividual notEffect of the helmet act forAccident Analysis and I10comprehensive Useryes / nonot significantnot applicable 2003Chadbunchachai W., Managementamong injured PTWs effectsapplicable motorcyclists in ThailandPrevention 35 (2003), 183-189helmet use lawMarui E. 1. Changes in exposure, climate and demographic parameters have not been statistically controlled in original studies. 2. Time period refers to publication dates of original studies. I10
Country has a Economy &yes => -27% in PTWMeta-comprehensive Useryes / noManagementinjuriesanalysis helmet use law 95% confidence intervals: -28% to -25%
not applicable not applicable The Handbook of Road Elvik R., Hoye A., Vaa1967- Safety Measures (2nd 2009Emerald Group Publishing Ltd USA national allPTWsT. & Sorensen M. 1992Edition) 1. Changes in exposure, climate and demographic parameters have not been statistically controlled in original studies. 2. Time period refers to publication dates of original studies. I10
not applicable not applicable The Handbook of Road Elvik R., Hoye A., Vaa1967- Safety Measures (2nd 2009Emerald Group Publishing Ltd USA national allPTWsT. & Sorensen M. 1992Edition) 1. Changes in exposure, climate and demographic parameters have not been statistically controlled in original studies. 2. Time period refers to publication dates of original studies. I10