Effectiveness of TMC AI Applications in Case Studies

Details

• Creators:
  Yang, H. ; Cetin, Mecit ; Wang, Z ; Huang, Z. ; Nallamothu, Sudhakar ; Huang, P.
• Corporate Creators:
  Leidos, Inc. ; Old Dominion University
• Corporate Contributors:
  United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Artificial Intelligence Incident Detection Neural Networks Traffic Control Centers Traffic Incidents
• Publication/ Report Number:
  FHWA-HRT-21-081
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Contracting Officer:
  Huang, Peter
• Corporate Publisher:
  United States. Department of Transportation. Federal Highway Administration
• Abstract:
  Traffic incident detection is a crucial task in traffic management centers (TMCs) that typically manage large highway networks with limited staff. An effective automatic incident-detection approach could benefit TMCs by helping to report abnormal events in a timely and accurate manner and optimize operating resources. During the past decades, researchers have made significant progress in developing such automatic approaches. Nevertheless, the majority of the developed approaches have shown limited success in the field, largely because of concerns about their often-costly false alarms (e.g., misdispatching response teams to a nonexistent incident). Fortunately, recent advances in artificial intelligence (AI) are expected to provide opportunities for improving conventional TMC operations. This project aimed to propose an AI-based incident-detection framework that can leverage large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. Researchers investigated the generic algorithmic problems in designing a detection approach and emphasized the architecture of the AI-based detection framework by including learning and evolving capabilities. The proposed framework was assessed with a fully controlled experiment in simulation that consisted of numerous traffic and incident scenarios. The results indicated that the proposed AI-based framework achieved higher detection rates, lower false alarm rates, and shorter time to detect the incidents in the studied scenarios than conventional approaches. Some extensions of the proposed framework are also discussed.
• Format:
  PDF
• Funding:
  DTFH61-16-D00030
• Collection(s):
  Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:ed37589d5405a4e79ea2dfe7cf7b013e5efa6062a7ecf91ff9358f6f8b479bb71724985a98802ba3a9a132c3fb3f72540ff9921300e782c79e8594b643e7bf9c
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67357/dot_67357_DS1.pdf
• File Type:
  [PDF - 4.44 MB ]