Section: New Results
Belief propagation inference for traffic prediction
Participants : Cyril Furtlehner, Jean-Marc Lasgouttes.
This work  deals with real-time prediction of traffic conditions in a setting where the only available information is floating car data (FCD) sent by probe vehicles. The main focus is on finding a good way to encode some coarse information (typically whether traffic on a segment is fluid or congested), and to decode it in the form of real-time traffic reconstruction and prediction. Our approach relies in particular on the belief propagation algorithm.
These studies have been done in particular in the framework of the projects Travesti and Pumas.
This year, the work about the theoretical aspects of encoding real valued variables into a binary Ising model has been under review for a Journal and has been largely revised in the process.