A White Paper on Artificial Intelligence & Big Data in Transportation

Details

• Creators:
  Fong, Amy ; Arredondo, David ; Hoyt, Hali ; Gold, Andrea ; Chin, Kristie ; Walton, C. Michael
• Corporate Creators:
  University of Texas at Austin. Center for Transportation Research
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Algorithms Artificial Intelligence Big Data Case Studies Data Collection Decision Making Emerging Data Sources Emerging Technology Internet Of Things Maintenance Predictive Models Service Planning Technology Transportation System Management

  More +
• Publication/ Report Number:
  FHWA/TX-18/0-6806-CTR-4
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Texas
• Edition:
  May 2018 - August 20 18
• Corporate Publisher:
  University of Texas at Austin. Center for Transportation Research
• Abstract:
  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 TxDOT and the 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 and 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.
• Format:
  PDF
• Funding:
  0-6806-CTR
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:d0fe35f3c8b1e2edcf332bcc66eda56ba8cacec6f81109f8aa3c52ba01d8a6c0d51189e9105495d8f4bb111171519abe5c5fa86be69b81f1c8f16eb3ee0e2d99
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86428/dot_86428_DS1.pdf
• File Type:
  [PDF - 8.57 MB ]