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SafeFITS A Road Safety Decision-Making Tool

Submitted by Secretariat Informal document WP.29-175-31 17 5 th WP.29, 19 - 22 June 2018 Agenda item 8.2. SafeFITS A Road Safety Decision-Making Tool. UNECE World Forum for Harmonization of Vehicle Regulations (WP.29) Geneva, 20 June 2018. SafeFITS.

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SafeFITS A Road Safety Decision-Making Tool

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  1. Submitted by Secretariat Informal document WP.29-175-31 175th WP.29, 19 - 22 June 2018 Agenda item 8.2 SafeFITS A Road Safety Decision-Making Tool UNECE World Forum for Harmonization of Vehicle Regulations (WP.29) Geneva, 20 June 2018

  2. SafeFITS • Road safety decision-making tool • Aim to assist governments and decision makers • Database on road safety indicators (i.e. fatalities and injuries, performance indicators, road safety measures, economy and background) for all countries worldwide • Statistical model of global causalities allowing “intervention”, “forecasting” and “benchmarking” analyses • CurrentStatus • Model finalized (after June 2017 peer review) • www.unece.org/trans/theme_safefits.html • Final version of web application presented at Inland Transport Committee (ITC) February 2018 • Pilot studiesbeingconducted in Albania and Georgia

  3. Conceptual framework • Based on five pillars of WHO Global Plan of Action and improved version of SUNflower pyramid SafeFITS layers • Economy and Management • Transport Demand and Exposure • Road SafetyMeasures • Road Safety Performance Indicators • Fatalities and Injuries SafeFITSpillars • Road Safety Management • Road Infrastructure • Vehicle • User • Post-Crash Services

  4. Database • Data for 130 countries • Population greaterthan 2.8 million • From international databases: WHO, UN, IRF, OECD and others • Refers to 2013 or latestavailableyear • Availability • Data available for large majority of countries and indicators • Low data availability in some cases • Restraint use rates • Fatalitiesattributed to alcohol use and fatalities by road user type • Transport demand and exposureindicators • Imputation where value missing – mean value of countries withsimilar road safety and socio-economiccharacteristics

  5. Data analysis methodology • Two-step modeling approach • Estimation of composite variable for each layer • Development of regression model by correlating road safety outcomes with composite variable • Otherconsiderations • Previousyearfatality rate • GNI per capita • Country grouping by socio-economiccharacteristics • Modeling assessment • Meanpercentagepredictionerror – 15% • More robust for countries withlowerfatality rates • Model cross-validatedwithsubset of full data set

  6. Introduction

  7. User manual

  8. Definitions

  9. Disclaimer

  10. Benchmark

  11. Benchmark – edit base case, compare against country cluster

  12. Benchmark – compare Transport Demand and Exposureindicators

  13. Forecast

  14. Forecast – change intervention year

  15. Forecast – no confidence intervals

  16. Report Generation

  17. Model limitations and recommendations • Model developed with best available data • But data missing for some countries – imputed using cluster averages where necessary • Outcomes for countries withveryparticularcharacteristics (eg, low GDP, high modal share of motorcycles) may not beproperlycaptured • Output based on extrapolation of short-termdevelopments • Takeintoaccount confidence intervals! • Use base case scenario as reference point • Test combinations of similar interventions – whatwouldbelikely to change together? • Note when changes or interventions are outside of historicalnorms – model not calibrated for these inputs • Model currentlybased on 2013 data – to beupdatedwith 2016 WHO data as publishedthisfall

  18. Suggestions? Comments? Contact UNECE stat.trans@unece.org

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