Regional Emergency Evacuation Analysis in Traffic With Connected and Autonomous Vehicles [Research Brief]

Details

• Creators:
  Lu, Pan ; Huang, Ying ; Ali, Asad ; Ahmad, Salman ; Yang, Xinyi ; Ren, Yihao ; Ahmed, Hafiz Usman
• Corporate Creators:
  North Dakota State University ; Mountain-Plains Consortium
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous vehicles
  • Wildfires
  • Hazards and emergency operations
  • Evacuation
  • Connected vehicles
  • Environmental impacts
  • Climate
  • Data collection
  • Weather conditions
  • Emergencies Emergency evacuations
  More +
• Publication/ Report Number:
  MPC 23-509 (project 685)
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  Researchers focused on three research issues: 1) predicting and making recommendations for future evacuations under wildfire conditions using connected vehicle data, 2) driving behaviors in wildfire evacuations, and 3) autonomous vehicle perceptions in winter conditions. This research addresses the urgent need for enhanced emergency evacuation strategies in the MPC region during natural disasters, particularly wildfires, by capitalizing on data from CAVs. Leveraging a dataset from connected vehicles, the study evaluates driving behavior and traffic conditions during wildfire evacuations, providing crucial insights for disaster response. Furthermore, it investigates the role of CAVs in disaster management and assesses public attitudes toward their integration in a medium-sized metropolitan area with cold weather. This research offers a data-driven foundation for optimizing emergency evacuation plans and underscores the potential of CAVs in improving disaster response, highlighting the importance of public perception in realizing this potential.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73133/dot_73133_DS1.pdf
• File Type:
  [PDF - 387.63 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:bfb83c8eae5c090dc3b7e1dad6d7c4d0daaa2610f79288aeeea607287c96268ce8d63ecc8b43ae6c6522626882858b044edc5451310d04cd99e564efe4e5339d