peer reviewed ; This paper presents a random forests (RF) based approach to estimate an origin-destination (O-D) matrix on the basis of a travel survey. The trips are predicted on a weekly basis to retain a maximum number of recorded trips for model calibration and validation. The flexibility of the procedure ensures an extension for further disaggregate estimates of O-D matrices. We adopt data stemming from the Belgium National Household Travel Survey as an input for estimating the O-D matrix, in contrast to conventional approaches that exploit traffic counts. Regarding the methodology, preliminary results indicate that the RF approach provides interesting approximations of the O-D traffic flows. The mix of "bagging" and "random subspace" principles included in the RF framework confines the risk of overfitting. Furthermore, the approach is capable of handling large dataset in terms of the number of features and the number of observations. © 2017 The Authors. Published by Elsevier B.V.