Combining Climate, Crash, and Highway Data for Improved Ranking of Speed and Winter-Weather Related Crash Locations in Oregon

In recent years, the techniques for screening transportation networks to identify high crash locations have become more sophisticated with significant data requirements. This paper presents the results of an empirical analysis of screening and ranking for weather related crashes on rural 1.6 km (1 mi) highway sections of Oregon highways. The analysis includes data generated with the extensive use of spatial techniques and incorporates climate data to enhance environmental considerations. The paper compares the results of five ranking methods: Critical rate (by functional class), critical rate (by functional class and climate zone), potential for crash reduction, expected frequency (adjusted by empirical-bayes), and frequency. For the empirical-bayes methods, safety performance functions were generated using negative binomial regression techniques. The 20 top 1.6 km (1 mi) sections were identified for each method and compared. The results reveal that the frequency and expected frequency methods identified the most sites in common, followed by the rate-based methods. The potential for the crash reduction method identified the most unique ranked list. The results highlight the differences in ranking methods.

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