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Traffic safety -- Oregon, Traffic accidents -- Investigation -- Oregon, Ice accidents -- Oregon -- Analysis


In recent years, the techniques for screening transportation networks to identify high crash locations have become more sophisticated. Many transportation agencies, however, lack sufficient data, either in timeliness, completeness, or accuracy to implement many of the recent advances. This paper presents the results of an empirical analysis of screening and ranking for specific crash type (speed and ice 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 (EB) methods, safety performance functions were generated using negative binomial regression techniques. The twenty 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 crash reduction method identified the most unique ranked list. The results highlight the differences in ranking methods and confirm that even with significant aggregation to improve the rate-based methods they did not identify segments similar to the more sophisticated EB-techniques.


This is the author’s version of a work that was accepted for publication in the Journal of Transportation Engineering. The final published version of this article is available at ASCE Civil Engineering Database( and is copyrighted 2008 by American Society of Civil Engineers.



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