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Optimizing Road Safety Rankings through Methodological Choices

This study examines the impact of methodological choices on road safety rankings, focusing on indicator selection, index processes, study design, and uncertainty analysis. Through a detailed exploration of weighting methods, expert input, and indicator sets, the research highlights the significance of uncertainty and sensitivity analyses in the ranking process. The results underscore the importance of utilizing uncertainty and sensitivity assessments in improving road safety indices. The conclusions emphasize the critical role of uncertainty and sensitivity analysis in developing robust and accurate road safety rankings for effective policy formulation and decision-making.

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Optimizing Road Safety Rankings through Methodological Choices

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  1. Impact of methodological choices on road safety ranking SAMO conference: 20/06/07 Elke Hermans: elke.hermans@uhasselt.be Transportation Research Institute - Hasselt University (Belgium)

  2. Overview 1. Introduction to indicators 2. Introduction to road safety 3. The road safety index process 4. Study design 5. Method: UA & SA 6. Results 7. Conclusions

  3. 1. Introduction to indicators • Popular concept • Represent large amount of information • Communicate simplified, clear message • Use: trends, bottlenecks, policy targets and priorities, communication, … • # indicators <-> aggregation in 1 index (e.g. TAI, IMI) • Road safety index = new, challenging and necessary matter!

  4. 2. Introduction to road safety • Causes of accidents and casualties: human-vehicle-environment • International literature defines risk domains: • Alcohol and drugs • Speed • Protective systems • Vehicle park • Infrastructure • Trauma management • Others: youth, VRUs, DRL, tiredness, … • Country-specific factors (low policy impact)

  5. General framework Billions of EUROs 49.286 injury acc. 1.089 fatalities 7.253 seriously inj. 58.057 slightly inj. Social cost - Final outcomes + Safety Performance Indicators Belgium, 2005 Safety measures and safety programs

  6. 3. The road safety index process • A methodologically sound road safety index (RSI) will be developed • Objective: • Comprehensive presentation of information • Better understanding of accident process • Comparing RS performance of regions • Measuring progress to objectives • Supporting policy by means of specific actions

  7. The RSI framework Index process Road Fatality Ranking Road unsafety Indicators Indicator selection Alcohol & drugs  % road users < BAC limit Imputation Speed  % road users < speed limit Normalisation C A S U A L T I E S A C C I D E N T S Weighting Protective systems  seatbelt wearing % in front Aggregation Visibility  daytime running lights law Uncertainty and sensitivity analysis Vehicle  % cars < 6 years Infrastructure  network density Road Safety Index Trauma management  health expenditure as GDP%

  8. 4. Study design • 3 methodological aspects • Weighting method: AHP or BA • Expert: 9 RS experts assigned weights • Indicator set: 7 or 6 RS indicators • RSI = ∑ stand. ind.values x weights • Output = avg. Δ in country ranking based on RSI compared to RFR

  9. Dataset • 7 road safety indicators • Data available for 18 European countries (≠ sources) Zwitserl.

  10. 5. Method: UA & SA • UA & SA are essential for indexes • Several subjective choices are made • Focus on ranking and 1 position • Offer correct and robust results • UA estimates uncertainty in output taking into account uncertainty in input • SA studies how uncertainty in output can be apportioned to different sources of uncertainty • Global variance based sensitivity method • Factors prioritisation setting • SIMLAB

  11. Step-by-step analysis(Saltelli et al., 2004) • Output = average shift in rank • 3 input factors: weighting, exp., ind. • Uniform distributions • Extended FAST method • Generation of 10,000 x 3 sample • Calculation of 10,000 output values • Analysis of the output • Conclusions

  12. Determining the output F11 F12 F13 F21 F22 F23 … … … FN1 FN2 FN3 e.g. F21 = BA F22 = expert 2 F23 = all 7 indicators M = W = [0.286; 0.429; 0.071; 0.000; 0.071; 0.071; 0.071] ZAT = [0.14; 0.62; -0.17; 1.30; -0.21; -0.32; 1.35] … ZUK = [0.99; -0.05; 0.97; -1.46; 0.58; -0.96; -1.31] for row 2

  13. 6. Results • UA: output distribution • μ = 5.64 • σ = 0.75 • Large ≠: • More and better ind. • Small EU data set BA; exp. 6; 6 indic. (no infrastr.)

  14. Results (2) • SA: first order and total effect index for each input factor

  15. 7. Conclusions • Importance and usefulness of UA & SA has been shown  essential part in the RSI development process • Set of indicators is most influencing input factor  focus on theoretical framework and indicator selection • Expert selection and weighting method had an impact mostly by interaction effects • Weighting method BA or AHP had the least impact but they have some similarities • These three aspects proved important in other studies as well

  16. Further research • Incorporate more methodological aspects: normalisation, imputation, aggregation and more possible weighting techniques in UA & SA • Other output of interest: country level • Methodological adaptations

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