A novel approach to modeling and predicting crash frequency at rural intersections by crash type and injury severity level.

Details

• Creators:
  Deng, Jun ; Castro, Marisol ; Bhat, Chandra R.
• Corporate Creators:
  University of Texas at Austin. Center for Transportation Research
• Corporate Contributors:
  University Transportation Centers Program (U.S.)
• Subject/TRT Terms:
  • Rural areas
  • Intersections
  • Accident risk forecasting
  • Accident types
  • Injury severity
  • Accident data
  • Choice models
• Publication/ Report Number:
  SWUTC/15/600451-00077-1
• Resource Type:
  Tech Report
• Geographical Coverage:
  Texas
• Corporate Publisher:
  Southwest Region University Transportation Center (U.S.)
• Abstract:
  Safety at intersections is of significant interest to transportation professionals due to the large number of ; possible conflicts that occur at those locations. In particular, rural intersections have been recognized as ; one of the most hazardous locations on roads. However, most models of crash frequency at rural ; intersections, and road segments in general, do not differentiate between crash type (such as angle, rear-end or sideswipe) and injury severity (such as fatal injury, non-fatal injury, possible injury or property ; damage only). Thus, there is a need to be able to identify the differential impacts of intersection-specific ; and other variables on crash types and severity levels. This report builds upon the work of Bhat et al. ; (2014) to formulate and apply a novel approach for the joint modeling of crash frequency and ; combinations of crash type and injury severity. The proposed framework explicitly links a count data ; model (to model crash frequency) with a discrete choice model (to model combinations of crash type and ; injury severity), and uses a multinomial probit kernel for the discrete choice model and introduces ; unobserved heterogeneity in both the crash frequency model and the discrete choice model. The results ; show that the type of traffic control and the number of entering roads are the most important determinants ; of crash counts and crash type/injury severity, and the results from our analysis underscore the value of ; our proposed model for data fit purposes as well as to accurately estimate variable effects.
  More +
• Format:
  PDF
• Funding:
  DTRT12-G-UTC06
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29318/dot_29318_DS1.pdf
• File Type:
  [PDF - 1.23 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:c87e8a518180fdd24ae0ef894e3bcbec42e1cb78512f13bf3ca2df0aa77ba4f0