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• W613132203 abstract "Transportation professionals have long recognized the importance of accounting for accident and incident impacts when designing and constructing transportation networks. Studies have used statistical models of accidents to explore associations of traffic injuries/harm with driver, vehicle, roadway and environmental factors. A critical characteristic of most studies is their reliance on traditional models (referred to as global models) that assume the efficacy of a single set of estimated parameters to forecast crash impacts — an approach characterized as “one size fits all.” However, in spatially diverse metropolitan regions, accident impacts and their associations with variables can vary across space, resulting in unobserved spatial heterogeneity. Overcoming this weakness has led to the use of various spatial analysis techniques. Using collision data from the Hampton Roads region of southeastern Virginia and the estimated economic costs of accidents, this paper explores spatial relationships and provides comparisons of results obtained from global models and those obtained using the technique of Geographically Weighted Regression (GWR). Estimation of collision harm (assumed to be related to the approximate monetary costs of accidents) indicates that GWR methods yield significantly more accurate results. The results provide valuable information on high-risk factors associated with collision harm and the spatial variations in these associations and suggest improved data application in dynamic traffic simulations." @default.
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• W613132203 date "2011-01-01" @default.
• W613132203 modified "2023-09-23" @default.
• W613132203 title "All Accidents are Not Equal: Using Geographically Weighted Regressions Models to Assess and Forecast Accident Impacts" @default.
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