Detection Technology Testbed on I 475: Technology Feasibility Study

Details

• Creators:
  Guin, Angshuman ; Hunter, Michael ; Kim, Han Gyol ; Choudhary, Nishu
• Corporate Creators:
  Georgia Tech Research Corporation
• Corporate Contributors:
  Georgia. Dept. of Transportation. Office of Performance-Based Management and Research ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Automatic incident detection
  • Crowdsourcing
  • Data collection
  • Machine learning
  • Noise (Communications)
  • Smartphones
  • Traffic delays The Infra-Red Traffic Logger (TIRTL)
  • Incident Delay Estimation
• Publication/ Report Number:
  FHWA-GA-21-1720
• Resource Type:
  Tech Report
• Geographical Coverage:
  Georgia;United States
• Edition:
  Final; August 2017 – October 2020
• Corporate Publisher:
  Georgia. Department of Transportation
• Abstract:
  This project evaluates the feasibility of use and potential benefits of a video-based automatic incident detection (AID) technology relative to existing detection via the Georgia 511 (NaviGAtor) incident reports and transportation management center operators’ manual observations. This study proposes a clustering machine learning framework for developing consolidation strategies and filters that will eliminate most noncritical alarms and associate confidence values with the alerts, thereby allowing for a focus on higher confidence alerts during busy periods. The project also investigates the potential of crowdsourced smartphone app-based incident detection and notification, in reducing the time to detection. Finally, the project reviews several of the conventional methods of incident delay estimation, evaluates their accuracy in the presence of noisy data, and develops a new regression-based method to quantify impact of traffic incidents in terms of vehicle delay.
• Format:
  PDF
• Funding:
  PI#0015715
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/56905/dot_56905_DS1.pdf
• File Type:
  [PDF - 5.55 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:2b5c5a3c53e8fa4cae8e309d25859c1ca55a0650e0f6c9f0449b8db3cfe7aeb05326c4c7e71023b318d99758d7118b3a79b0fef87ff22cd644c425429da8c061