Abstract: An enhanced temporal decision tree is described that allows a temporal feature to be tested at a node not merely at a given time instant but, rather, over a time window. Further, the recorded temporal data can be multi-source, with features recorded from multiple sources; multi-resolution, through subsampling; multi-scale, based on varying time windows; and multi-domain, through sampling in the frequency domain, for example. By allowing features to be tested over a time window, enhanced temporal decision trees can be used to classify any temporal data or time series.

Abstract: An enhanced temporal decision tree is described that allows a temporal feature to be tested at a node not merely at a given time instant but, rather, over a time window. Further, the recorded temporal data can be multi-source, with features recorded from multiple sources; multi-resolution, through subsampling; multi-scale, based on varying time windows; and multi-domain, through sampling in the frequency domain, for example. By allowing features to be tested over a time window, enhanced temporal decision trees can be used to classify any temporal data or time series.

Abstract: A rideshare system and associated methodology for recommending rideshares to prospective riders and drivers uses itineraries and personal affinities to make the recommendations. The rideshare system computes a cost value by comparing the search criteria submitted by a user to stored user profiles. Results of the comparison are displayed in order of highest match to lowest match, according to the cost values computed. Further, the system also adaptively updates the stored profiles according to rideshare history. Lastly, the system includes a real-time user localization unit to locate prospective riders and drivers in real time, making just in time or unplanned rideshares possible.