An Optimal Adaptive Routing Algorithm for Large-Scale Stochastic Time-Dependent Networks

Details

• Creators:
  Gao, Song
• Corporate Creators:
  University Transportation Centers Program (U.S.) ; University of Massachusetts at Amherst
• Corporate Contributors:
  New England Transportation Consortium ; United States. Dept. of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  • Algorithms
  • Real time information
  • Route choice
  • Routes and routing
  • Stochastic processes
  • Time dependence
  • Highways
  • Planning and Forecasting
  • I72: Traffic and Transport Planning
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• TRIS Online Accession Number:
  01590495
• Edition:
  Final Report
• Corporate Publisher:
  New England University Transportation Center
• Abstract:
  The objective of this project is to develop an efficient algorithm and its computer implementation for the optimal adaptive routing problem that is practical in large-scale real-life networks, where a traveler could revise the route choice based upon en route traffic information. Existing adaptive routing algorithms are for explorative purpose and can only be applied to hypothetical and simplified networks. In this project, important changes will be made to make the algorithm practical in real-life networks, in three major areas: memory, running time, and realistic features. The algorithm will be implemented and tested on both randomly generated networks and a number of real-life large networks, including those from the Pioneer Valley Planning Commission (PVPC) of Massachusetts, and Stockholm, Sweden. The developed algorithm is an important building block for the route choice module of an advanced traffic prediction model, and is also the intelligent core of a route guidance system.
• Format:
  PDF
• Funding:
  UMAR24-19A
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/38992/dot_38992_DS1.pdf
• File Type:
  [PDF - 231.81 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:e2a3585a0673aec6326fbe655b04a76fc033d270ce8d4a964f1a73d87357e59c