PREDICTING AND UTILIZING VARIABILITY OF TRAVEL TIMES IN MAPPING SERVICES

摘要

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.

主权项

1. A system for predicting variability of travel time for a trip and utilizing the predicted variability for route planning, the system comprising:
one or more processors; and memory storing instructions that are executable by the one or more processors, the memory including:
an input component to receive an origin, a destination, and a start time associated with the trip;a route generator to obtain candidate routes that run from the origin to the destination;a prediction component to predict, based at least in part on a machine learning model that includes latent variables that are associated with the trip, a probability distribution of travel time for individual ones of the candidate routes; andan output component to:
recommend one or more routes from the candidate routes based at least in part on a criterion that is based at least in part on the probability distribution; andprovide a measure of travel time for individual ones of the recommended one or more routes.