Evaluating Curve Speed Behavior Using Shrp 2 Data

Details

• Creators:
  Geedipally, Srinivas R. ; Pratt, Michael P. ; Dadashova, Bahar ; Wu, Lingtao ; Shirazi, Mohammadali
• Corporate Creators:
  Texas A&M Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; ATLAS Center (Mich.)
• Subject/TRT Terms:
  • Behavior
  • Forecasting
  • Highway curves
  • Speed Crash rates
  • Strategic Highway Research Program 2
• Publication/ Report Number:
  ATLAS-2017-21
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  ATLAS Center (Mich.)
• Abstract:
  Horizontal curves are associated with a disproportionate number of severe crashes. Factors that mostly influence horizontal curve safety are speed limit compliance, geometric features of the curve, sight distance, and traffic volume. In recent years, several attempts have been made to explore horizontal curve safety using the Second Strategic Highway Research Program (SHRP 2) data, but none focused on operating speeds on curves. The SHRP 2 data include vehicle speeds that can be used to validate the speed prediction models developed in other states and check if the calibration is needed. The first objective of this study was to use the SHRP 2 database to assess the transferability of the speed prediction models developed in Texas to the state of Indiana. The second objective is to identify any relationship between speeds and the frequency that the given driver traverses the curve or road of interest. The third objective is to simultaneously assess curve severity and crash rates at horizontal curves using operating characteristics and safety data, respectively. The study results revealed that the speed differential models developed in Texas can be extended to multiple states with the application of a multiplicative adjustment factor. It was also found that the 85th - percentile speeds of familiar drivers are greater than that of unfamiliar drivers at the curve midpoint and the prediction models worked better for unfamiliar drivers than familiar drivers. Finally, the results show that, for the curves belonging to more severe categories, side friction differential is positively associated with the crash rate.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC54
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/36257/dot_36257_DS1.pdf
• File Type:
  [PDF - 1.08 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:428be7f06f95204793d792320c7de82da8cab27d981ba9ff8145f460f6bee649a956af611ad12e70d10af4f9fadef4ad248914ccf56fd0754cc3aa43c4a2ba48