Effective network screening methods play a significant role in highway safety management programs and contribute to sustainable mobility by facilitating the reduction in all crashes, including fatalities and injuries across the transportation system. This study presents a comprehensive analysis comparing the effectiveness of three new network screening techniques for pinpointing safety improvement locations on rural roads. The proposed methods are the Global Risk Scoring (GRS), the Crash Risk Index (CRI), and the Predicted Empirical Bayes (P-EB) methods. The analysis utilized 10 years of roadway geometry, traffic, and crash data from state-owned rural highways in Oregon, with the first five years (2011–2015) used for model development and the subsequent five years (2016–2020) for validation. Comparative tests assessed consistency with historical crash rankings and temporal stability across observation periods. The analysis revealed distinct strengths among the screening methods. The GRS method demonstrated a high level of consistency with historical crash data, while the P-EB method exhibited superior consistency across different time periods, suggesting its value for long-term safety planning. The CRI method demonstrated reasonable consistency in performance, irrespective of the test carried out. While no single method outperforms the others in all scenarios, each has unique advantages and data requirements that can better suit the agency’s needs, given available resources. This research provides actionable insights for improving safety management strategies and advancing sustainable mobility.
Dhakal et al. (Sun,) studied this question.