CONTACT US: Contact info
|
Citation |
Harirforoush H, Bellalite L. Accid. Anal. Prev. 2016; 130: 62-74. |
|
Affiliation |
Department of Applied Geomatics, University of Sherbrooke, 2500 boul. de l'Université Sherbrooke, J1K 2R1 Quebec, Canada. Electronic address: lynda.bellalite@usherbrooke.ca. |
|
Copyright |
(Copyright © 2016, Elsevier Publishing) |
|
DOI |
|
PMID |
27567730 |
|
Abstract |
This paper proposes a two-step integrated method for identifying traffic accident (TA) hotspots on a roadway network. The first step includes a spatial analysis method called network kernel density estimation (KDE). The second step is a network screening method using the critical crash rate, which it described in the Highway Safety Manual (HSM). The method was examined by using three years of TAs (2011-2013) in Sherbrooke, Canada. The network KDE uses TAs to graphically display sites with a high crash density. Two different crash patterns were used for identifying these locations: (1) a crash pattern that includes three-year aggregated crash data, and (2) a crash pattern that involves three-year merged crash data. The results of the two crash patterns were evaluated based on a prediction accuracy index (PAI). It was found that the results obtained from the merged crash data outperformed the other. On the other hand, crash clustering in a site does not imply a site is hotspot and it is better to tested by other factors. High crash density locations were then tested by the critical crash rate, which helps to create an accurate comparison of sites. The importance of the critical crash rate is that it takes several factors into account such as the amount of exposure, the type of intersection, variance in crash data, etc. We realized that the hotspots determined using the two methods reflect very problematic locations and filter out the locations that do not have a problem. This approach could help transportation authorities and safety specialists to identify and prioritize sites that require more safety attention. Language: en |