Comprehensive Data Analytics of Pedestrian Involved Hit-and-Run Road Crashes

The main objective of the study is to discover the contributing factors for hit-and-run drivers in pedestrian crashes, in perspectives of individual driver, vehicle, residence characteristics of the driver, and environmental factors such as roadway, weather and lighting conditions. Three-year crash data (2008-2010) were collected from the Florida Department of Transportation (FDOT). Subsequently, the associated data were collected from FDOT and U.S. Census Bureau. Since the authors targeted the hit-and-run at-fault drivers causing pedestrian crashes data were defined as the case group. In order to find out contributing factors for the hit-and-run, hit-and-run driver data were compared with a reference population: non-hit-and-run but at-fault drivers, who caused traffic crashes but stayed at the scene. The hit-and-run case group was matched to the comparison group which was a set of randomly selected non-hit-and-run at-fault drivers in an approximately 1:4 ratio. A Bayesian Binary Logistic Regression Model was utilized, and the model revealed that contributing factors for hit-and-run in pedestrian crashes. It is expected that the results from this study can be used for establishing policies to effectively prevent hit-and-run in pedestrian crashes.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 13p
  • Monograph Title: Proceedings of the 25th World Road Congress - Seoul 2015: Roads and Mobility - Creating New Value from Transport

Subject/Index Terms

Filing Info

  • Accession Number: 01715706
  • Record Type: Publication
  • ISBN: 9782840604235
  • Files: TRIS
  • Created Date: Aug 30 2019 3:48PM