Hazardous Material Commodity Flow

Details

• Creators:
  Barr, Jonathan L ; Godinez, Melanie A ; Willett, Jesse A ; Girard, Michael K
• Corporate Creators:
  Pacific Northwest National Laboratory
• Corporate Contributors:
  United States. Department of Transportation. Pipeline and Hazardous Materials Safety Administration. Office of Hazardous Materials Safety ; United States. Department of Energy
• Subject/TRT Terms:
  • Hazardous materials
  • Commodity flow
  • Placarding
  • Machine vision
  • Artificial intelligence
• Publication/ Report Number:
  PNNL-37744
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Dept. of Transportation. Pipeline and Hazardous Materials Safety Administration
• Abstract:
  The Hazardous Material Commodity Flow project for the U.S. Department of Transportation's Pipeline and Hazardous Materials Safety Administration report focuses on the evaluation, testing, and implementation of systems for monitoring the transportation of hazardous materials (Hazmat) on roadways. This report details the current state of technology for hazardous material flow as well as approaches to increase public safety on roadways through the application of cost-saving automated monitoring systems employing Artificial Intelligence (AI) to enhance Hazmat identification, flow modeling, and flow monitoring on U.S. roadways. The report provides in-depth assessment of technology and data, capabilities, testbed selection criteria, and testing protocols that can be used towards hazardous materials flow tracking.
• Format:
  PDF
• Funding:
  A2306-069-089-046080 ; DE-AC05-76RL01830
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/85043/dot_85043_DS1.pdf
• File Type:
  [PDF - 1.49 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; PHMSA Collection
• Main Document Checksum:
  urn:sha-512:0edee32c81f13c8cfee8e14c3b187bcb9bbbcc66d9d7ad4dea4618231fb41ac1c5bb1fd5e7b2607e5816466ef95fe2e6fad776ecfedd8822d8b4ad98b380cc7e