Influence of Pavement Conditions on Commercial Motor Vehicle Crashes [supporting dataset]

Abstract of the final report is stated below for reference: Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary logistic regression to explore relationships between injury occurrence after a CMV crash and multiple independent variables. Additionally, Artificial Neural Network (ANN) models were developed to classify CMV crash injury severity. Importantly, the inclusion of pavement condition variables (International Roughness Index and Pavement Condition Index) substantially enhanced the accuracy of the logistic regression model, increasing predictability from 0.8% to 41%. The study also demonstrates the potential of Artificial Neural Network models in predicting CMV crash injury severity, achieving an accuracy of 60% and an F-measure of 0.52. These results highlight the importance of considering road pavement conditions in road safety policies and interventions. The study provides valuable insights for policymakers and stakeholders aiming to enhance road safety for CMVs in the District of Columbia and showcases the potential of machine learning techniques in understanding the complex interplay between road conditions and CMV crash occurrences.

• Dataset URL:
  • https://doi.org/10.31979/mti.2023.2343
• Record URL:
  • https://scholarworks.sjsu.edu/mti_publications/474/
• Summary URL:
  • https://transweb.sjsu.edu/sites/default/files/2343-RB-Pavement-Conditions-Commercial-Motor-Vehicle-Crashes.pdf
• Summary URL:
  • https://rosap.ntl.bts.gov/view/dot/73108
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/73107
• Dataset URL:
  • https://rosap.ntl.bts.gov/view/dot/73221
• Supplemental Notes:
  • The dataset supports report: Influence of Pavement Conditions on Commercial Motor Vehicle Crashes, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Mineta Transportation Institute

  College of Business
  San José State University
  San Jose, CA  United States  95192-0219

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  California Department of Transportation

  Sacramento, CA  United States  95819
• Authors:
  • Arhin, Stephen
  • 0000-0002-1306-4017
  • Manandhar, Babin
  • 0000-0003-3623-8347
  • Gatiba, Adam
  • 0000-0003-3131-0960
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Dataset
• Dataset publisher:

  Mineta Transportation Institute Publications

  San José State University
  San José, California  United States 

Subject/Index Terms

• TRT Terms: Commercial vehicles; Crash rates; Data; Injury severity; Machine learning; Pavement distress; Roughness; Statistical analysis
• Geographic Terms: Washington (District of Columbia)
• Subject Areas: Data and Information Technology; Highways; Motor Carriers; Pavements; Public Transportation; Safety and Human Factors;

Filing Info

• Accession Number: 01904537
• Record Type: Publication
• Contract Numbers: 69A3551747127
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Jan 16 2024 9:02AM