Section: New Results

Belief propagation inference for traffic prediction

Participant : Jean-Marc Lasgouttes.

This work [36], [35], in collaboration with Cyril Furtlehner (TAU, Inria), deals with real-time prediction of traffic conditions in a urban setting with incomplete data. The main focus is on finding a good way to encode available information (flow, speed, counts,...) in a Markov Random Field, and to decode it in the form of real-time traffic reconstruction and prediction. Our approach relies in particular on the Gaussian belief propagation algorithm.

Through our collaboration with PTV Sistema, we obtained extensive results on large-scale datasets containing 250 to 2000 detectors. The results show very good ability to predict flow variables and a reasonably good performance on speed or occupancy variables. Some element of understanding of the observed performance are given by a careful analysis of the model, allowing to some extent to disentangle modelling bias from intrinsic noise of the traffic phenomena and its measurement process.

This year we worked on code optimization and submitted our work to Transportation Research: Part C.