title: Evaluating the performance of network screening methods for detecting high collision concentration locations on highways.
abstract: This paper documents findings from evaluating performances of three different methods for segmenting freeway sites for the purpose of identifying high collision concentration locations: Sliding Moving Window (SMW), Peak Searching (PS) and Continuous Risk Profile (CRP). The traffic collision data from sites segmented in each method under two different roadway definitions were used to estimate excess expected average crash frequency with Empirical Bayes adjustment with respect to two different sets of Safety Performance Functions (SPFs). The estimates from each of the methods were then used to prioritize the detected sites for safety investigation and these lists were compared with previously confirmed high collision concentration locations (or hot spots). The input requirements for each of three methods were identical, yet their performance markedly varied. The findings revealed that CRP method has the lowest false positive (i.e., requiring a site for safety investigation while it is not needed) rate. The performances of SMW and PS significantly varied when different sets of SPFs were used while that of CRP was less affected.
authors.affiliation: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Korea.
authors.forename: Oh Hoon
authors.initials: OH
authors.lastname: Kwon
authors.forename: Min Ju
authors.initials: MJ
authors.lastname: Park
authors.forename: Hwasoo
authors.initials: H
authors.lastname: Yeo
authors.forename: Koohong
authors.initials: K
authors.lastname: Chung
mesh_terms: D000056:Accident Prevention / Q000379:methods*; D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D001499:Bayes Theorem; D002140:California; D003661:Decision Support Techniques; D015233:Models, Statistical; D012044:Regression Analysis; D012449:Safety / Q000706:statistics & numerical data