Boundary Conditions Estimation on a Road Network Using Compressed Sensing

This report presents a new boundary condition estimation framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi equation, the authors pose the problem of estimating the boundary conditions of the system on a network, as a Mixed Integer Linear Program (MILP). The authors show that this framework can handle various types of traffic flow measurements, including floating car data or flow measurements. To regularize the solutions, the authors propose a compressed sensing approach in which the objective is to minimize the variations over time (in the L₁ norm sense) of the boundary flows of the network. The authors show that this additional requirement can be integrated in the original MILP formulation, and can be solved efficiently for small to medium scale problems.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 29p

Subject/Index Terms

Filing Info

  • Accession Number: 01603510
  • Record Type: Publication
  • Report/Paper Numbers: SWUTC/16/600451-00090-1, 476660-00090-1
  • Contract Numbers: DTRT12-G-UTC06
  • Created Date: May 31 2016 1:36PM