The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation 2022 Updates
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation 2022 Updates

Filetype[PDF-9.17 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed
English
Details You May Also Like

Details:

  • Corporate Creators:
    Exploratory Advanced Research Program (U.S.)
  • Corporate Contributors:
    United States. Federal Highway Administration
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FHWA-HRT-22-026
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    United States. Federal Highway Administration
  • Abstract:
    When people hear the phrase "artificial intelligence" (AI), they might think of robots and machines that perform some of the functions that humans do, as such searching for victims in a partially collapsed building or comforting the elderly with personalized advice as part of monitoring their moods and home activity. When defined within the context of the transportation sector, AI conjures up futuristic images of self-driving, fully automated cars. Though these fantasies aren’t yet real, AI does have a lot of potential to positively impact our Nation’s transportation system in the near future. The use of AI to enable computers to digest and analyze large amounts of data and form conclusions—a process known as machine learning—can create many improvements to transportation beneficial to the American public. Highlights include: Improving traffic flows at signalized intersections along specific routes or as part of integrated corridor management, Aiding traffic management centers with improving crash detection, predicting traffic slowdowns, and recommending detours, Facilitating traffic safety by warning vehicles of pedestrians obscured by parked vehicles who are starting to cross the street and monitoring real-time traffic and weather conditions, Discerning and anticipating how drivers might react in certain traffic situations, Providing information to travelers with disabilities to assist in their trip planning and increase their situational awareness, Allowing traffic engineers and urban planners to better understand what variables may reduce the potential for traffic crashes or injuries, Reducing highway infrastructure repair and reconstruction costs by augmenting data from structural health monitoring of highway assets.
  • Format:
    PDF
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at rosap.ntl.bts.gov