Leverage AI for Asset Inventories & Management

Texas Department of Transportation (TxDOT) owns and maintains a large number of safety and traffic-related devices including signs, delineators, guardrails, roadway lights, and traffic signals. While TxDOT maintains inventories for large infrastructure assets such as bridges, there is a need to develop more robust, dynamic inventory databases. This tool would enable TxDOT to make strides toward a cutting-edge proactive and holistic asset management practice, prioritize assets and corridors for maintenance, and maintain an updated understanding of asset location and condition. The main objective of this research is to develop a framework and prototype that uses data collected from CAVs to augment TxDOT's asset inventory and management. Integrating cutting-edge data sources and training AI to (1) identify assets and (2) detect changes to asset condition can improve TxDOT's awareness of the location and condition of assets. These data sources would augment, not replace, the traditional remote sensing and mobile mapping applications used by TxDOT teams. Components of the research work would included: [1] Compile asset inventory and condition summaries using one or multiple third party datasets. [2] Develop a condition rating system for each infrastructure to understand and monitor the status. [3] Collect data during nighttime operations in order to evaluate reflectivity. [4] Develop and end-to-end framework to integrate third-party data into TxDOT's asset inventory toolkit.

• Record URL:
  • https://library.ctr.utexas.edu/Presto/project=0-7235

Language

• English

Project

• Status: Active
• Funding: $387141
• Contract Numbers:

  0-7235

• Sponsor Organizations:

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Project Managers:

  Odell, Wade

• Performing Organizations:

  University of Houston, Texas

  Houston, TX  United States 

  Texas Southern University, Houston

  Center for Transportation Training and Research
  3100 Cleburne
  Houston, TX  United States  77004
• Principal Investigators:

  Bian, Zheyong

• Start Date: 20241014
• Expected Completion Date: 20260630
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asset management; Autonomous vehicles; Condition surveys; Connected vehicles; Data collection; Databases; Inventory
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01935547
• Record Type: Research project
• Source Agency: Texas Department of Transportation
• Contract Numbers: 0-7235
• Files: RIP, STATEDOT
• Created Date: Oct 30 2024 3:11PM