Investigating Critical Incidents, Driver Restart Period, Sleep Quantity, and Crash Countermeasures in Commercial Vehicle Operations Using Naturalistic Data Collection

Details

• Creators:
  Blanco, Myra ; Hickman, Jeffrey S. ; Olson, Rebecca L ; Bocanegra, Joseph L ; Greening, Mike ; Madison, Phillip ; Holbrook, G. Thomas ; Hanowski, Richard J. ; Nakata, Akiko ; Bowman, Darrell S.
• Corporate Creators:
  Virginia Tech Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. Federal Motor Carrier Safety Administration
• Subject/TRT Terms:
  • Commercial drivers
  • Truck drivers
  • Circadian rhythms
  • Drowsiness
  • Fatigue (Physiological condition)
  • Vehicle to infrastructure communications
  • Mobile communication systems
  • Automatic data collection systems
  • Data collection
  • Highway safety CMV
  More +
• Publication/ Report Number:
  FMCSA-RRR-13-017
• DOI:
  https://doi.org/10.21949/1502656
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Department of Transportation. Federal Motor Carrier Safety Administration
• Abstract:
  The aim of this study was to compare the Large Truck Crash Causation Study (LTCCS) and Naturalistic Driving (ND) datasets to identify discrepancies and to determine the source(s) of these discrepancies. The project included a generalized comparative analysis of the LTCCS, General Estimates System (GES), and ND datasets and then focused on five specific analyses using only two datasets (LTCCS and ND). The analyses investigated one selected crash/event type, two selected contributing factors, one selected environmental condition of occurrence, and one complex event scenario involving multiple elements. The rationale for these analyses was twofold: to identify the discrepancies and the reasons for these discrepancies between the LTCCS and ND datasets, and to evaluate the feasibility of cross-comparisons using these types of datasets, thereby minimizing the weaknesses and combining the strengths in each dataset (e.g., ND has the advantage of seeing the driver’s response to the threat, but the disadvantage of not seeing vehicle mechanical conditions that might have contributed to the inadequate response).
• Format:
  PDF
• Funding:
  DTFH61-01-C-00049, Task Order # 23
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31518/dot_31518_DS1.pdf
• File Type:
  [PDF - 3.84 MB]
• Collection(s):
  Federal Motor Carrier Safety Administration
• Main Document Checksum:
  urn:sha-512:16d64749d744612f4ec6c8a5388f2968396373c42854bb8affe8df51a2bb24d03fc789ae4f5f512cff8f1542e559c1fa8817d5e35aa8f05943649f22b4a6994a