The extreme value theory approach to safety estimation.
Crash-based safety analysis is hampered by several shortcomings, such as randomness and rarity of crash occurrences, lack of timeliness, and inconsistency in crash reporting. Safety analysis based on observable traffic characteristics more frequent than crashes is one promising alternative. In this research, we proposed a novel application of the extreme value theory to estimate safety. The method is considered proactive in that it no longer requires historical crash data for the model calibration. We evaluated the proposed method by applying it to right-angle collisions at signalized intersections. Evaluation results indicated a promising relationship between safety estimates and historical crash data. Crash estimates at seven out of twelve sites remained within the range of Poisson-based confidence intervals established using historical crash data. The test has yielded large-variance safety estimates due to the short 8-h observation period. A simulation experiment conducted in this study revealed that 3-6 weeks of observation are needed to obtain safety estimates with confidence intervals comparable to those being obtained from 4-year observed crash counts. The proposed method can be applied to other types of locations and collisions as well.
Texas Transportation Institute, 2929 Research Pkwy, College Station, 77843-3135, USA. praprut@tamu.edu
Praprut
P
Songchitruksa
Andrew P
AP
Tarko
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D018570:Risk Assessment; D012449:Safety