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- W206805370 abstract "In order to identify high crash locations, the Tennessee Department of Transportation (TDOT) has an extensive road safety audit program which uses criteria based on the ratio of crashes to average daily traffic but does not target locations with a high number of pedestrian crashes since there are no pedestrian counts. Apart from ratio approach, a robust methodology is not currently available to identify pedestrian high-crash locations in Tennessee. The objective of this study is to develop a different methodology based on Anselin’s Local Moran I index in Geographic Information System (GIS) to detect high crash clusters and investigate the factors that influence the concentration of pedestrian crashes. Using pedestrian crash data from Shelby County in Tennessee, the study found that spatial dependence plays a strong role during the analyses of pedestrian crashes. These spatial dependencies, accounted through spatial autocorrelation, helped to detect statistically significant clusters of crashes in a GIS framework. These clusters were then overlaid with selected socio-economic and population demographic data in order to identify their association with high crash clusters. The study found the following factors to be associated with high crash clusters: when more than 25% percentage of the population is 18 years of age and younger, when the population of seniors is greater than 13%, when there’s a high population density of low income people, and when the percentage of families below poverty level is greater than 10%. The cluster maps may help transportation agencies to understand issues of pedestrian crashes for safety enhancements." @default.
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- W206805370 date "2013-01-01" @default.
- W206805370 modified "2023-09-28" @default.
- W206805370 title "A Methodology to Identify Factors associated with Pedestrian High Crash Clusters Using GIS Based Local Spatial Autocorrelation" @default.
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