02. Abstract.md 1.9 KB
Abstract
Road traffic fatalities remain a leading cause of premature death worldwide, posing a substantial obstacle to achieving United Nations Sustainable Development Goal 3.6, which calls for halving global road‑traffic deaths and injuries by 2030. Although the road‑fatality rate (deaths per 10 000 vehicles) is the most frequently used indicator of road‑traffic safety (RTS), it captures only one dimension of risk and therefore provides an incomplete picture for evidence‑based policymaking. This study proposes a multi‑indicator RTS measurement framework that integrates four complementary metrics: (a) road‑fatality rate, (b) total number of fatalities, (c) number of fatal accidents, and (d) total number of road accidents. Using daily traffic‑volume and accident data from Estonia for the period 2010–2018 (N = 3,285 days), we estimated a structural path model in which traffic volume influences RTS both directly through accident frequency and indirectly through accident severity. Results indicated that the indirect pathway via fatalities was substantially stronger (standardized indirect effect = 0.22, p < 0.001) than the pathway via total accidents (indirect effect = 0.13, p = 0.003). The multi‑indicator model demonstrated superior fit (χ²(2) = 3.45, RMSEA = 0.032, CFI = 0.99) and explained 64 % of the variance in RTS, compared with only 34 % accounted for by a single‑indicator road‑fatality‑rate model. These findings suggest that policies targeting accident severity—such as enhanced speed‑control measures, infrastructure upgrades at high‑risk locations, and driver‑behavior interventions—are likely to be more effective than strategies focused solely on reducing accident frequency. The proposed framework offers a comprehensive tool for monitoring progress toward SDG 3.6 and for guiding sustainable transport policy.