A White Paper on Artificial Intelligence & Big Data in Transportation

Advances in computing techniques and processing capacity as well as increased data collection are beginning to enable artificial intelligence applications in a myriad real-world setting. Artificial intelligence (AI) algorithms at their most advanced can provide decision support, ease labor-intensive operations, perform predictive analysis, and inform targeted outreach. In the transportation sector such applications could reduce the administrative burden at public agencies such as Texas Department of Transportation (TxDOT) and the Department of Motor Vehicles (DMV), and collect higher resolution traffic data with less infrastructure, thus enabling detailed transportation planning models and predicting and identifying traffic incidents. Artificial intelligence is also being applied to traffic control devices, and preliminary deployments have been promising. However, with the advent of advanced models and the significantly higher quantity of data they typically consume and produce, key challenges will include managing complex data sources, ensuring their ethical application in decision-making, protecting the privacy of the public, and reducing cybersecurity risks. This white paper provides an overview of key technologies that are enabling AI, a menu of AI applications across five transportation application areas, and case-studies from deep-dive interviews with technology companies.

• Record URL:
  https://www.txinnovationalliance.org/learning-hub-publications-pdfs-whitepapers/artificial-intelligence-and-big-data-in-transportation
• Summary URL:
  https://library.ctr.utexas.edu/Presto/content/Detail.aspx?ctID=OWE3NjYzNTktYzJmNC00ZTAwLThmMjItYzhmNzNiYTFmNzdh&rID=MzI0MjU%3D&sID=MQ%3D%3D&qrs=VHJ1ZQ%3D%3D&rrtc=VHJ1ZQ%3D%3D&utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+NewestTxdotResearch+%28New+TxDOT+Research+Publications+%28Feed%29%29
• Corporate Authors:

  University of Texas, Austin

  Center for Transportation Research
  3925 W. Braker Lane, 4th Floor
  Austin, TX  United States  78759

  Texas Department of Transportation

  Research and Technology Implementation Division
  3925 W. Braker Lane, 4thFloor
  Austin, TX  United States  78759

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Fong, Amy
  • Arredondo, David
  • Hoyt, Hali
  • Gold, Andrea
  • Chin, Kristie
  • Walton, C Michael
• Publication Date: 2018-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 58p

Subject/Index Terms

• TRT Terms: Algorithms; Artificial intelligence; Case studies; Data collection; Data management; Machine learning; Technological innovations; Transportation planning
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01743626
• Record Type: Publication
• Report/Paper Numbers: FHWA/TX-18/0-6806-CTR-4, 0-6806-CTR-4
• Contract Numbers: 0-6806-CTR
• Files: TRIS, ATRI, USDOT, STATEDOT
• Created Date: Jun 23 2020 12:26PM