1. A multi-task learning method for predicting travel demand on an associated transportation network, comprising: collecting observation data corresponding to a plurality of observations of the associated transportation network; generating a set of time series corresponding to transportation network data of segments of the associated transportation network; forming a plurality of clusters of time series, each time series in the set thereof in one cluster; for each cluster, applying multi-task learning to generate a prediction model, including: determining a common prediction model for all series in a cluster thereof, and determining a specific prediction model for each series in the cluster thereof, wherein the prediction model generated via the multi-task leaminq application is a sum of the common prediction model and the specific prediction model for each cluster; and predicting travel demand on a selected segment of the associated transportation network corresponding to at least one of the set of time series in accordance with the generated prediction model, wherein at least one of the collecting, generating, forming, and predicting is performed by a computer processor.