Predicting and utilizing variability of travel times in mapping services
US10175054B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 10, 2015 |
| Grant date | Jan 8, 2019 |
| Priority date | — |
| Expiry date | May 22, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/047
- WIPO fieldMeasurement
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.