%0 Journal Article
%T Employment of ATMS Traffic Control Device Data to Assist in Identification of Crash-Prone Intersections
%J IATSS research
%D 2008
%A Hwang, Kevin P.
%V 32
%N 1
%P 32-43
%X This article describes a study of using the Advanced Traffic Management Systems (ATMS) to identify crash prone intersections in Kaohsiung, Taiwan.  Traditional methods of identifying high crash locations (HCLs) involve using both traffic volume data as well as number of crashes per intersection. Yet after governmental restructuring in Taiwan, no cities except Taipei and Kaohsiung have been able to count traffic volume, thus hindering accurate identification of HCLs. Without exposure data, crash counts may be misleading and hide dangerous intersections that have low traffic volume. The study tried to accurately identify HCLs without exposure data.  After a description of the statistical methods used, the authors draw several conclusions and make various recommendations.  Their conclusions describe the limitations and advantages of their method of HCL identification.  The recommendations provide suggestions for directions of future research in this area.<p />
%G 
%I Elsevier Publishing
%@ 0386-1112
%U http://dx.doi.org/