Section: New Results
Belief propagation inference for traffic prediction
Participant : Jean-Marc Lasgouttes.
This work , in collaboration with Cyril Furtlehner (TAO, Inria), 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.
The work about the theoretical aspects of encoding real valued variables into a binary Ising model has now been published .
Moreover, following an agreement signed with the city of Vienna (Austria) and the company SISTeMA ITS (Italy), we obtained access to large amounts of data. We are now working on assessing the performance of our techniques in real-world city networks.