Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management

Details

• Creators:
  Hu, Xianbiao ; Tang, Qing ; Liu, Jenny
• Corporate Creators:
  Pennsylvania State University ; Missouri University of Science and Technology ; Washington State University. National Center for Transportation Infrastructure Durability & Life Extension (TriDurLE) UTC
• 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:
  • Algorithms
  • Autonomous vehicles
  • Car following
  • Highway capacity
  • Highway maintenance
  • Maintenance equipment
  • Sensors
  • Traffic flow
• Publication/ Report Number:
  2020-MST-05
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Washington State University. National Center for Transportation Infrastructure Durability & Life Extension (TriDurLE) UTC
• Abstract:
  Work zones are critical for efficient and safe operation of a highway transportation system. Performing the maintenance required for a roadway infrastructure, however, could involve risks. In 2017 alone, a total of 158,000 total vehicle crashes occurred in our nation’s work zones, accounting for 61,000 injuries 1. Many of these frequently involved State Department of Transportation (DOT) employees. The Autonomous Maintenance Technology (AMT) is a quickly emerging autonomous-vehicle-based technology for improving transportation infrastructure maintenance by removing drivers from risk. Its impact to transportation capacity, although critically important to transportation infrastructure condition monitoring and performance management, has not been studied before. In this project, models and algorithms are developed to reveal the fundamental working mechanism of AMT, and analyze the resulted traffic flow capacity discount associated with AMT vehicles as a moving bottleneck.
• Format:
  PDF
• Funding:
  ORSO 135461
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/68696/dot_68696_DS1.pdf
• File Type:
  [PDF - 1.12 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:009cde65ab02ec51144c614fa8b89afdb7a3636613d820f3019a2760abc6cf0c0e7d6193cfd098c66e6deae0c2cd6b3ce45f69ed2ab0e25f1d97e329a1e8d8e0