Advanced operations focused on connected vehicles/infrastructure (CVI-UTC).

Details

• Creators:
  Smith, Brian L. ; Kluger, Robert ; Park, Hyungjun
• Corporate Creators:
  Virginia Tech Transportation Institute ; University of Virginia. Center for Transportation Studies ; Morgan State University
• Subject/TRT Terms:
  • Intelligent transportation systems
  • Intelligent vehicles
  • Accident locations
  • Accident data
  • Algorithms
  • Mathematical prediction
  • Splines
  • Multiple regression analysis
• Publication/ Report Number:
  DUNS: 0031370150000 ; EIN: 54-6001805
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Dept. of Transportation. Research and Innovative Technology Administration
• Abstract:
  The goal of the Infrastructure Safety Assessment in a Connected Vehicle (CV) Environment ; project was to develop a method to identify infrastructure safety “hot spots” using CV data. ; Using these basic safety messages to detect hot spots may allow for quicker discovery than ; traditional methods, such as police-reported crashes. The basic safety message may be able to ; detect events that police normally cannot obtain, including unreported crashes and near-crashes. ; The project successfully explored some models and algorithms to detect crashes and near-crashes ; and also designed a methodology to apply to hot spot identification. With the data available, ; conclusive results were not achieved; however, the models showed some potential. Three ; techniques were tested to predict crashes using vehicles’ kinematic data. To predict where a ; crash was occurring, multivariate adaptive regression splines, classification and regression trees, ; and a novel pattern matching approach were all tested. The models were able to identify the ; majority of 13 known crashes with different amounts of false positives. The pattern matching ; approach outperformed a simple acceleration threshold by identifying nearly 70% of crashes in a ; crash-only test set and 74% of near-crashes in a near-crash only test set. On the training set, it ; was able to identify more crashes than the thresholds without increasing the number of false ; positives observed. Based on the work described in this report, the CVI-UTC is fully prepared to ; apply the methodology to data collected on the field test bed.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29654/dot_29654_DS1.pdf
• File Type:
  [PDF - 1.88 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:a283ae5d67394213b9cfdb0b97f798ae49079927a1b9c0ad62617c080f51a65e