Combining road safety information in a performance index

Hermans, Elke; Van den Bossche, Filip; Wets, Geert · 2008 · OpenAlex-citations

DOI: 10.1016/j.aap.2008.02.004

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Summary

This paper addresses the methodological challenge of constructing a composite road safety performance index, specifically focusing on the assignment of weights to individual indicators. The authors argue that while composite indicators are valuable for benchmarking and policy prioritization, the weighting process is often arbitrary and lacks consensus in the literature. To resolve this, the study evaluates five common weighting techniques—factor analysis, analytic hierarchy process (AHP), budget allocation, data envelopment analysis (DEA), and equal weighting—to determine which method provides the most justifiable and robust framework for aggregating road safety data. The researchers applied these five methods to a dataset comprising 21 European countries. The data included seven standardized indicators representing key risk domains: alcohol and drugs, speed, protective systems, visibility, vehicle condition, infrastructure, and trauma care. These indicators were sourced from international databases such as IRTAD, Eurostat, and the World Health Organization. The study provides a theoretical overview of each method, detailing their mechanisms, advantages, and disadvantages. For instance, Factor Analysis relies on statistical correlations, AHP and Budget Allocation depend on expert judgment, DEA optimizes weights to maximize individual country scores, and Equal Weighting assigns uniform importance. The authors then applied these techniques to the road safety data to generate comparative rankings. The results demonstrate that the choice of weighting method significantly influences the final ranking of countries. However, the methods showed greater agreement for countries with relatively poor road safety performance. When comparing the generated indices against a baseline ranking based on traffic fatalities per million inhabitants, the weights derived from Data Envelopment Analysis (DEA) exhibited the highest correlation. The authors note that while methods like AHP and Budget Allocation are subject to expert bias and inconsistency, and Factor Analysis may rely on spurious correlations, DEA offers a data-driven approach that effectively captures the relative efficiency of each country’s safety profile. The significance of this research lies in its recommendation to use Data Envelopment Analysis for developing road safety indices. By identifying DEA as the method that best aligns with actual fatality statistics, the paper provides policymakers with a validated tool for benchmarking national performance. This approach allows for a more objective assessment of road safety, facilitating better resource allocation and policy prioritization across European nations. The study concludes that a sound weighting framework is essential for the credibility of composite indicators in multidisciplinary fields like road safety.

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• comparative international

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• Empirical Findings: crash risk outcomes
• Methodological Resource: metric or index