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Modeling Traffic Accidents at Signalized Intersections in the City of Norfolk, VA.

Filetype[PDF-618.84 KB]


  • English
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    • Creators:
    • Corporate Creators:
      Hampton University. Eastern Seaboard Intermodal Transportation Applications Center
    • Subject/TRT Terms:
    • Resource Type:
      Tech Report
    • Geographical Coverage:
      Virginia
    • Corporate Publisher:
      Hampton University. Eastern Seaboard Intermodal Transportation Applications Center
    • Abstract:
      This study was an attempt to apply a proactive approach using traffic pattern and signalized intersection characteristics to predict accident rates at signalized intersections in a city’s arterial network. An earlier analysis of accident data at selected intersections within the City of Norfolk indicated that in addition to traffic volume, other controllable factors contributed to traffic accidents at specific intersections. These factors included area topography, lane patterns, type of road signs, turning lanes, etc. It is also known that administrative factors such as signal types, signal polices, road closures, etc., and maintenance factors such as road conditions, condition of the signals, condition of road signs, etc. also impact road accidents.

      The objective of this study was to relate these variables to accident rate and delineate variables that are statistically more significant for accident rate. Data on several topographical variables was collected in the City of Norfolk. These variables included number of lanes, turn lanes, pedestrian crossing, restricted lanes, etc. A linear regression model was used to establish relationship between these variables and the accident rate. The resulting regression model explained 60% of the variability. It also showed that four topographical variables are more important than other variables. These variables include number of lanes, number of turn lanes, presence of median and presence of permanent hazard like railway crossing. However, validation of model showed higher than expected variation. The model developed, in this study, overestimates the accident rate by 33%, thus, limiting its practical application.

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