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Characterizing and Predicting Traffic Accidents in Extreme Weather Environments

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Motorists are vulnerable to extreme weather events, which are likely to be exacerbated by climate change throughout the world. Traffic accidents are conceptualized in this article as the result of a systemic failure that includes human, vehicular, and environmental factors. The snowstorm and concurrent accidents that occurred in the Northeastern United States on 26 January 2011 are used as a case study. Traffic accident data for Fairfax County, Virginia, are supplemented with Doppler radar and additional weather data to characterize the spatiotemporal patterns of the accidents resulting from this major snowstorm event. A kernel density smoothing method is implemented to identify and predict patterns of accident locations within this urban area over time. The predictive capability of this model increases over time with increasing accidents. Models such as these can be used by emergency responders to identify, plan for, and mitigate areas that are more susceptible to increased risk resulting from extreme weather events.
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Keywords: accidentes de tránsito; eventos meteorológicos extremos; extreme weather events; geographic information systems; modelo predictivo; predictive model; sistemas de información geográfica; traffic accidents

Document Type: Research Article

Affiliations: 1: University of Utah, 2: Pennsylvania State University, 3: University of Calgary,

Publication date: January 2, 2017

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