Highway safety evaluation has traditionally been performed using crash data. However crash data based safety analysis has limitations in terms of timeliness and efficiency. Previous studies show that the use of surrogate safety data allows for earlier evaluation of safety in comparison to the significantly longer time horizon required for collecting crash data. However, the predictive capability of surrogate measures is an area of ongoing research. Previous studies have often resulted in inconsistent findings in the relationship between surrogates and crashes, one of the primary reasons being inconsistent definitions of a conflict.
This study evaluated the effectiveness of certain surrogate measures (Acceleration-Deceleration profile, intersection entering speed of through vehicles, and Post Encroachment Time (PET)) in assessing the safety of opposing left-turn interactions at 4-legged signalized intersections by collection of time resolved video from eighteen selected intersections throughout Georgia. Overall, this research demonstrated that surrogate measures can be effective in safety evaluation, specifically demonstrating the use of PET as a surrogate for crashes between left-turning vehicles and opposing through vehicles. The analysis of data found that the selected surrogate threshold is critical to the effectiveness of any surrogate measure. For example, the required PET threshold was found to be as low as 1 second to identify high crash intersections, significantly lower than the commonly reported 3 second threshold. Non-parametric rank analysis methods and generalized linear modeling techniques were used to model PET with other intersection and traffic characteristics to demonstrate the degree to which these surrogates can be used to identify potential high-crash intersections without resorting to a crash history. Finally, the effectiveness of PET and its assistance to decision makers is also been demonstrated through an example that helped find errors in reported crash data.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/52324 |
| Date | 27 August 2014 |
| Creators | Peesapati, Lakshmi Narasimham |
| Contributors | Hunter, Michael P. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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