This paper 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. Starting from the Ising model of statistical physics, we use a discretized space-time traffic description, on which we define and study an inference method based on the Belief Propagation (BP) algorithm. The idea is to encode into a graph the \emph{a priori} information derived from historical data (marginal probabilities of pairs of variables), and to use BP to estimate the actual state from the latest FCD. The behavior of the algorithm is illustrated by numerical studies on a simple simulated traffic network. The generalization to the superposition of many traffic patterns is discussed.