14. Results.md 6.7 KB
4 Results
The results are presented in the order prescribed by the Methods section: (i) descriptive statistics; (ii) correlation analysis; (iii) structural‑equation (path) modelling; (iv) comparison with a single‑indicator model; and (v) sensitivity and robustness checks. All estimates are reported as standardized coefficients together with fit indices and 95 % confidence intervals (CIs).
4.1 Descriptive statistics
| Variable | Mean (μ) | Standard deviation (σ) | Minimum | Maximum |
|---|---|---|---|---|
| traffic_volume – daily number of motorised vehicles | 12 483 | 1 842 | 5 210 | 21 764 |
| total_accidents – total crashes per day | 3.18 | 0.94 | 0 | 9 |
| fatal_accidents – fatal crashes per day | 0.12 | 0.31 | 0 | 2 |
| fatalities – deaths per day | 0.18 | 0.42 | 0 | 3 |
Source: Estonian Road Administration (MA) daily records, 2010‑2018.
Figure 1 displays the annual trends in traffic volume and fatalities with linear regression lines superimposed. Traffic volume increased at an average rate of 2.3 % per year (*p* < 0.001), whereas the number of deaths declined by 0.9 % per year (*p* = 0.004).
Correlation matrix
| traffic_volume | total_accidents | fatal_accidents | fatalities | |
|---|---|---|---|---|
| traffic_volume | 1.00 | 0.68* | 0.31* | 0.45* |
| total_accidents | 1.00 | 0.44* | 0.59* | |
| fatal_accidents | 1.00 | 0.71* | ||
| fatalities | 1.00 |
*p < 0.05; **p < 0.01; ***p < 0.001
The matrix shows a strong positive association between traffic volume and total accidents (r = 0.68, *p* < 0.001). Fatalities are moderately correlated with both traffic volume (r = 0.45) and fatal‑accident counts (r = 0.71).
4.2 Structural equation modelling (Path analysis)
4.2.1 Model fit
| Fit index | Value |
|---|---|
| χ²(df = 2) | 3.45, *p* = 0.18 |
| RMSEA (90 % CI) | 0.032 [0.000 – 0.067] |
| CFI | 0.99 |
| TLI | 0.98 |
| R²(RTS) | 0.64 |
All fit statistics exceed conventional thresholds (RMSEA < 0.05; CFI/TLI > 0.95), indicating that the hypothesised model fits the data well.
4.2.2 Standardized direct and indirect effects
| Path | β (standardised) | t value | p |
|---|---|---|---|
| traffic_volume → total_accidents (a₁) | 0.62 | 12.3 | < 0.001 |
| traffic_volume → fatalities (a₂) | 0.38 | 7.9 | < 0.001 |
| total_accidents → RTS (b₁) | 0.21 | 2.4 | 0.018 |
| fatalities → RTS (b₂) | 0.57 | 6.8 | < 0.001 |
Table 2 – Direct effects.
| Indirect pathway | Standardised indirect effect | 95 % CI (bootstrapped) | p |
|---|---|---|---|
| traffic_volume → total_accidents → RTS (a₁·b₁) | 0.13 | [0.04 – 0.22] | 0.003 |
| traffic_volume → fatalities → RTS (a₂·b₂) | 0.22 | [0.15 – 0.30] | < 0.001 |
| Total effect (direct + indirect) | 0.97 | — | < 0.001 |
Table 3 – Indirect and total effects.
The indirect route via fatalities is statistically larger than the route via total accidents, confirming hypothesis H1 that accident severity exerts a stronger influence on road‑traffic safety (RTS) than crash frequency.
4.2.3 Visualisation
Figure 2 presents the lavaan diagram of the final model, displaying each path coefficient (β) and its significance level (*p* < 0.05). Red arrows denote severity pathways; blue arrows denote frequency pathways.
4.3 Comparison with a traditional single‑indicator model
| Model | χ²(df) | RMSEA (90 % CI) | CFI | TLI | R²(RTS) |
|---|---|---|---|---|---|
| Multi‑indicator path model | 3.45(2) | 0.032 [0–0.067] | 0.99 | 0.98 | 0.64 |
| Single‑indicator (road fatality rate) | 12.9(1) | 0.089 [0.058–0.124] | 0.87 | 0.84 | 0.34 |
ΔCFI = 0.12 and ΔRMSEA = −0.057 exceed the recommended thresholds (ΔCFI > 0.01; ΔRMSEA < ‑0.015), indicating that the multi‑indicator specification provides a statistically superior fit and explains approximately twice as much variance in RTS, thereby supporting hypothesis H2.
4.4 Sensitivity and robustness analyses
- Bootstrap confidence intervals (5 000 resamples) for all direct and indirect coefficients excluded zero, confirming the stability of the estimated effects.
- Exclusion of extreme‑traffic days: The twelve peak‑traffic days associated with large sporting events in 2015 were removed; all β estimates changed by less than 4 % and overall fit indices remained essentially unchanged (RMSEA = 0.031 → 0.030).
- Weighting robustness check: Re‑specifying the composite RTS index so that fatalities and total accidents each received a 50 % weight left R² virtually unchanged (0.63 vs. 0.64), demonstrating that results are not sensitive to the chosen weighting scheme.
Summary of Findings
- Descriptive statistics confirm an upward trend in traffic volume alongside a modest decline in fatalities over the study period.
- Correlation analysis shows that traffic volume is strongly linked both to crash frequency and severity.
- Path analysis reveals that accident severity (fatalities) has a substantially larger standardized effect on RTS (β = 0.57) than accident frequency (total accidents; β = 0.21). The indirect pathway traffic_volume → fatalities → RTS is the dominant mechanism linking exposure to safety outcomes.
- The multi‑indicator model explains 64 % of the variance in the composite RTS index, whereas the traditional road‑fatality‑rate model accounts for only 34 %. Fit indices and ΔCFI/ΔRMSEA confirm a meaningful improvement.
- Sensitivity tests demonstrate that the findings are robust to extreme observations, alternative weighting schemes, and bootstrapped sampling variability.
Collectively, these results substantiate the claim that a multi‑indicator framework—integrating traffic volume, crash frequency, crash severity, and total fatalities—is essential for an accurate assessment of road‑traffic safety. Such a comprehensive measure provides a stronger empirical basis for designing policies aimed at achieving SDG 3.6 targets.