Predictive Real-Time Traffic Management in Large-Scale Networks Using Model-Based Artificial Intelligence

Details

• Corporate Creators:
  Exploratory Advanced Research Program (U.S.)
• Contributors:
  Nallamothu, Sudhakar
• Corporate Contributors:
  United States. Department of Transportation. Federal Highway Administration ; United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center
• Subject/TRT Terms:
  • Advanced traffic management systems
  • Artificial intelligence
  • Predictive models
  • Traffic speed
  • Traffic control
• Publication/ Report Number:
  FHWA-HRT-23-107
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Department of Transportation. Federal Highway Administration
• Abstract:
  This fact sheet highlights the research's team holistic framework to address the challenges in large-scale predictive traffic incident management (TIM). The research can be summarized as interconnecting subtask models that accomplish the following: Predict traffic speed. Detect traffic anomalies. Approximate traffic flow physics. Control traffic. Estimate network benefits (e.g., mobility, safety, and energy use). The researchers want to predict nonrecurrent traffic conditions in large-scale networks up to 30 min ahead of the earliest time an incident is reported and proactively recommend real-time operational management strategies.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/74165/dot_74165_DS1.pdf
• File Type:
  [PDF - 1.38 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:e05c8e44512fcc18a7618922a7f505e8add33198c24959b734f340aab08aa8f8969869b48c2f1ea467aec0f8cfe73d756ecf031373717289c153cdc1fec676cb