Socially Optimal Personalized Routing With Preference Learning [Research Brief]

Details

• Creators:
  Vayanos, Phebe ; Dessouky, Maged M.
• Corporate Creators:
  METRANS Transportation Center (Calif.) ; University of Southern California. Department of Industrial and Systems Engineering
• 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:
  • Demographics
  • Machine learning
  • Mathematical models
  • Optimization
  • Regression analysis
  • Route choice
  • Traffic congestion
  • Travel behavior
  • Travel surveys
  • Utility theory
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  METRANS Transportation Center (Calif.)
• Abstract:
  Spurred by rapid population growth and city development, traffic congestion has become inescapable in metropolitan areas across the U.S. and its direct and indirect effects can be dire. With support lacking for taxes to fund expansion of the existing network, it is imperative to find novel ways to improve efficiency of the existing infrastructure. A major obstacle is the inability to enforce socially optimal routes upon the commuters. The goal of this research project is to improve routing efficiency by leveraging heterogeneity in commuter preferences to help bridge the gap between the de facto user equilibrium solution and the utopic socially optimal solution.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC57
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67568/dot_67568_DS1.pdf
• File Type:
  [PDF - 152.00 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:da57963f11db2a3fd8c53d7fce699c7b9c2526a3f13f428ec26fab9986b58e6d68e02031b76fe2b5df2beafe2d1e1f274720d8a89c5a64efcba592a34a5d140a