New methods to identify and rank high pedestrian crash zones: an illustration.
Identifying and ranking high pedestrian crash zones plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This paper presents (1) a Geographical Information Systems (GIS) methodology to study the spatial patterns of pedestrian crashes in order to identify high pedestrian crash zones, and (2) an evaluation of methods to rank these high pedestrian crash zones. The GIS based methodology to identify high pedestrian crash zones includes geocoding crash data, creating crash concentration maps, and then identifying high pedestrian crash zones. Two methods generally used to create crash concentration maps based on density values are the Simple Method and the Kernel Method. Ranking methods such as crash frequency, crash density, and crash rate, as well as composite methods such as the sum-of-the-ranks and the crash score methods are used to rank the selected high pedestrian crash zones. The use of this methodology and ranking methods for high pedestrian crash zones are illustrated using the Las Vegas metropolitan area as the study area. Crash data collected for a 5-year period (1998-2002) were address matched using the street name/reference street name intersection location reference system. A crash concentration map was then created using the Kernel Method as it facilitates the creation of a smooth density surface when compared to the Simple Method. Twenty-two linear high crash zones and seven circular high crash zones were then identified. The GIS based methodology reduced the subjectivity in the analysis process. Results obtained from the evaluation of methods to rank high pedestrian crash zones show a significant variation in ranking when individual methods were considered. However, rankings of high pedestrian crash zones were relatively consistent with little to no variation when the sum-of-the-ranks method and the crash score method were used. Thus, these composite methods are recommended for use in ranking high pedestrian crash zones instead of individual methods.
Pulugurtha
Srinivas S
SS
Department of Civil & Environmental Engineering, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States. sspulugu@uncc.edu
Krishnakumar
Vanjeeswaran K
VK
Nambisan
Shashi S
SS
D000063:Accidents, Traffic; D000465:Algorithms; D029681:Arabidopsis Proteins; D002958:City Planning; D004779:Environment Design; D040362:Geographic Information Systems; D006801:Humans; D018570:Risk Assessment; D014157:Transcription Factors; D016138:Walking / Q000293:injuries*
D029681:Arabidopsis Proteins; C512212:GIS protein, Arabidopsis; D014157:Transcription Factors