Connected and Automated Vehicles Investment and Smart Infrastructure in Tennessee - Part 5: A Comprehensive View of Intelligent Mobility in Tennessee

Details

• Creators:
  Khattak, Asad ; Mahdinia, Iman ; Taherkhani, Mohammad Safari ; Lee, Steve
• Corporate Creators:
  University of Tennessee, Knoxville ; University of Tennessee at Chattanooga
• Corporate Contributors:
  Tennessee Department of Transportation. Long Range Planning Research Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Connected vehicles
  • Intelligent transportation systems
  • Intelligent vehicles
  • Investments
  • Surveys
  • Evaluation and assessment
  • Feasibility analysis
• Publication/ Report Number:
  RES2019-07
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Tennessee
• Edition:
  Final Report. May 2019-May 2022
• Corporate Publisher:
  Tennessee. Department of Transportation
• Abstract:
  Transportation innovations captured through intelligent mobility strategies are critical to achieving economic development, safety, mobility, energy, environmental, and equity goals. Intelligent mobility is broadly defined to encompass connected and automated vehicles, electric vehicles, and multi-modal personal and freight movements, all done safely and securely. It is enabled by large-scale data, tools for transportation modeling and artificial intelligence, simulation and visualization, multi-scale connectivity, and high-performance computing. To assess intelligent mobility strategies in Tennessee, this report first provides a framework by exploring focus areas, including physical infrastructure, digital infrastructure, electric vehicle infrastructure, policies, regulations, and associated public knowledge and acceptance.
• Content Notes:
  The project has developed a series of five (5) reports that support intelligent mobility strategies. This is report 5 of 5.
• Format:
  PDF
• Funding:
  RES2019-07
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73417/dot_73417_DS1.pdf
• File Type:
  [PDF - 6.51 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:19196d8451fa8672278af8999d6be15e2ccbfb68cc193133503e7bca642839befd4b9a6a9c68193903726b034f6c9bad7f492be56b002f9ed7d8e4aa1e4b40ea