Patents

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.

A travel coordination system provides suggestions to providers for where they should go to reduce the wait time between trips. A geographic region is broken down into zones and a score is generated for each zone. A zone score can be generated by determining the estimated wait time for the zone and generating a model for the wait time. A zone score can also be generated using a model for the wait time using factors that likely contribute to the wait time. The zone score for each zone is displayed to the provider on a road map of the geographic region along with the boundaries of each of the zones and the provider’s position within the region. The travel coordination system also suggests driving routes. The travel coordination system selects a target zone and generates candidate routes to the zone. A route is selected based on route scores.