Abstract: A system and method are provided for predicting passenger loads at vehicle stops on a transportation network. The method includes providing a classifier which has been trained to predict passenger loads at vehicle stops on a transportation route, based on reconstructed passenger loads for vehicle stops on the route. Transaction data is acquired for passengers boarding at vehicle stops on the transportation route. Reconstructed passenger loads are computed for vehicle stops on the route based on the transaction data. With the trained classifier, a passenger load for at least one of the vehicle stops on the transportation route is predicted, based on the reconstructed passenger load for the vehicle stop.

Abstract: Methods, systems, and processor-readable media for the stochastic optimization of public transport schedules. A real-world collection of transit instances can be derived from a transport system and fed as input to a two-stage stochastic program. A schedule offset can be relaxed in the two-stage stochastic program to allow the two-stage stochastic program to operate according to the real-world collection of transit instances. An optimized transport schedule can then be derived from the two-stage stochastic program for use by the transport system based on the schedule offset and the real-world collection of transit instances.