A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Exploring Spatial Relationship between Roadway Safety and Wet Condition Risk Factors Based on Systemic Safety Analysis Approach
This study applies spatial analysis to investigate the relationship between crashes, wet road conditions, and physical roadway variables. Wet roadway and flood risk factors are not included in the data-driven systemic safety analysis approach currently used by the Federal Highway Administration’s Office of Safety. The presented methodology identifies variables that influence crash frequency under wet road conditions by examining spatial correlations between crash data and factors expected to influence roadway drainage patterns. Predicted variables were examined of the geographical information system software ArcMap, such as surface slope, urban influence, and stream influence (relative location within the floodplain). Analysis results revealed a statistical relationship between the predicted variables and the crash severity level. Specifically, urban influence was identified as the most influential factor (37% correlation) followed by stream influence (10% correlation). This information reveals correlations between variables in the physical environment, which may eventually be used to identify areas of high crash risk in existing roadway infrastructure and thus improve the ability for practitioners to develop proactive safety measures.
Exploring Spatial Relationship between Roadway Safety and Wet Condition Risk Factors Based on Systemic Safety Analysis Approach
This study applies spatial analysis to investigate the relationship between crashes, wet road conditions, and physical roadway variables. Wet roadway and flood risk factors are not included in the data-driven systemic safety analysis approach currently used by the Federal Highway Administration’s Office of Safety. The presented methodology identifies variables that influence crash frequency under wet road conditions by examining spatial correlations between crash data and factors expected to influence roadway drainage patterns. Predicted variables were examined of the geographical information system software ArcMap, such as surface slope, urban influence, and stream influence (relative location within the floodplain). Analysis results revealed a statistical relationship between the predicted variables and the crash severity level. Specifically, urban influence was identified as the most influential factor (37% correlation) followed by stream influence (10% correlation). This information reveals correlations between variables in the physical environment, which may eventually be used to identify areas of high crash risk in existing roadway infrastructure and thus improve the ability for practitioners to develop proactive safety measures.
Exploring Spatial Relationship between Roadway Safety and Wet Condition Risk Factors Based on Systemic Safety Analysis Approach
Park, Seri (author) / Smith, Virginia (author) / Saldutti, Thomas (author) / Zoccoli, Nicholas (author)
2018-08-16
Article (Journal)
Electronic Resource
Unknown
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