Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.

Abstract: A travel coordination system provides information to providers to reduce the wait time between trips. A region is partitioned into zones and generates a score for each zone. A zone score can be generated by estimating the wait time for the zone, which may be determined by a model for the wait time. The model for the wait time may use factors that contribute to the wait time, such as the number of providers in a zone and the trip request rate. The zone score for each zone is displayed to the provider on a road map of the geographic region. The travel coordination system also provides routes that use zone scoring to reduce the wait time for receiving an assignment request during travel to the destination. The travel coordination system identifies a destination and generates candidate routes to the destination. A route is selected based on route scores.

Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.