Mackenzie Chance Scott1, Shouraseni Sen Roy2, Shivangi Prasad2. 1. a Center for Computational Sciences, University of Miami , Coral Gables , Florida. 2. b Department of Geography and Regional Studies , University of Miami , Coral Gables , Florida.
Abstract
OBJECTIVE: The objective of this study is to analyze the spatial distribution of the vehicles involved in crashes in Miami-Dade County. In addition, we analyzed the role of time of day, day of the week, seasonality, drivers' age in the distribution of traffic crashes. METHOD: Off-the-system crash data acquired from the Florida Department of Transportation during 2005-2010 were divided into subcategories according to the risk factors age, time of day, day of the week, and travel season. Various spatial statistics methods, including nearest neighbor analysis, Getis-Ord hot spot analysis, and kernel density analysis revealed substantial spatial variations, depending on the subcategory in question. RESULTS: Downtown Miami and South Beach showed up consistently as hotspots of traffic crashes in all subcategories except fatal crashes. However, fatal crashes were concentrated in residential areas in inland areas. CONCLUSION: This understanding of patterns can help the county target high-risk areas and help to reduce crash fatalities to create a safer environment for motorists and pedestrians.
OBJECTIVE: The objective of this study is to analyze the spatial distribution of the vehicles involved in crashes in Miami-Dade County. In addition, we analyzed the role of time of day, day of the week, seasonality, drivers' age in the distribution of traffic crashes. METHOD: Off-the-system crash data acquired from the Florida Department of Transportation during 2005-2010 were divided into subcategories according to the risk factors age, time of day, day of the week, and travel season. Various spatial statistics methods, including nearest neighbor analysis, Getis-Ord hot spot analysis, and kernel density analysis revealed substantial spatial variations, depending on the subcategory in question. RESULTS: Downtown Miami and South Beach showed up consistently as hotspots of traffic crashes in all subcategories except fatal crashes. However, fatal crashes were concentrated in residential areas in inland areas. CONCLUSION: This understanding of patterns can help the county target high-risk areas and help to reduce crash fatalities to create a safer environment for motorists and pedestrians.
Entities:
Keywords:
Miami; day of the week; hotspot analysis; off-the-system crashes; seasonality