Influence of Pavement Conditions on Commercial Motor Vehicle Crashes [Research Brief]

Details

• Creators:
  Arhin, Stephen ; Manandhar, Babin ; Gatiba, Adam
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Commercial vehicles
  • Neural networks
  • Traffic safety
  • Pavements
  • Highways
  • Motor carriers
  • Public transit
  • Artificial Neural Network
  • Confusion Matrix
  • Road Safety
  More +
• Publication/ Report Number:
  2343
• Resource Type:
  Brief
• Geographical Coverage:
  Washington (District of Columbia) ; United States
• Edition:
  Final Report
• Corporate Publisher:
  San Jose State University. College of Business. Mineta Transportation Institute
• Abstract:
  The study examined traffic crash data obtained from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database and pavement condition data maintained by the District Department of Transportation (DDOT) in the District of Columbia. CMV crash data was specifically collected for crashes occurring on designated CMV routes between the years 2016 to 2020. Recent measurements of International Roughness Index (IRI) and Pavement Condition Index (PCI) data for the years 2016, 2017, 2019, and 2021 were acquired from the DDOT to correlate the crash data with the pavement's condition. The research team used QGIS, a robust open-source geographic information system, to effectively manage data integration and analysis.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73108/dot_73108_DS1.pdf
• File Type:
  [PDF - 262.66 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:5973421960966c7130bfcc34c44bb407f39e49331f61cff4b76f56adc06a42fa0427e0d9abf11c38b2e32de33573fb201bec6d9ddb2b7cfdf1083829fef1de51