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Predicting and Exploiting Travel Time Variability in Mapping Services

A travel time and variability technology, applied in forecasting, data processing applications, computing, etc., can solve the problems of inaccurate travel time prediction, low average travel time, poor user experience, etc., to improve user experience and improve survival rate. , the effect of reducing the chance of being late and/or leaving too early

Active Publication Date: 2021-06-22
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, map services that suggest routes with low average travel time but high variability in travel time may result in poor user experience due to inaccuracies in travel time predictions

Method used

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  • Predicting and Exploiting Travel Time Variability in Mapping Services
  • Predicting and Exploiting Travel Time Variability in Mapping Services
  • Predicting and Exploiting Travel Time Variability in Mapping Services

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0138] Example One: A system for predicting variability in travel time for an itinerary and utilizing the predicted variability for route planning, the system comprising: one or more processors; and memory storing Instructions executable by a device, the memory includes: an input component for receiving an origin, a destination, and a start time associated with the trip; a route generator for obtaining a candidate route for traveling from the origin to the destination; predicting means for predicting a probability distribution of travel times for individual ones of the candidate routes based at least in part on a machine learning model including latent variables associated with the trip; and output means for: recommending one or more of the candidate routes, the criteria being based at least in part on a probability distribution; and providing a measure of travel time for individual ones of the one or more recommended routes.

example 2

[0139] Example 2: The system according to Example 1, further comprising a ranker configured to rank candidate routes according to the route that minimizes the criteria before the output component recommends one or more routes.

example 3

[0140] Example three: The system of any of the preceding examples (alone or in combination), wherein the criteria include at least one of: percentile of travel time, or time to arrival at destination by specified time probability of occurrence.

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Abstract

A system for predicting variability in travel time for a trip at a particular time may utilize a machine learning model that includes latent variables associated with the trip. A machine learning model may be trained from historical travel data based on location-based measurements reported from mobile devices. Once trained, machine learning models can be leveraged to predict variability in travel times. A process may include: receiving an origin, a destination, and a start time associated with a trip; obtaining candidate routes for travel from the origin to the destination; and predicting travel for individual ones of the candidate routes based at least in part on a machine learning model Probability distribution over time. One or more routes may be recommended based on the predicted probability distributions, and a measure of travel time for the recommended routes may be provided.

Description

Background technique [0001] Computer-driven mapping services help users locate points of interest (eg, particular buildings, addresses, etc.), and the like. Many map services also provide route planning applications that can suggest the fastest or most ideal routes from an origin to a destination, and sometimes even provide predicted travel times (eg, driving time, walking time, etc.) for these routes. These predicted travel times typically represent average (average) travel times that can be obtained from historical trip data. [0002] Although average travel times provide fairly accurate predictions of travel times, they are not completely accurate for predicting actual travel times. In other words, average travel time will never give perfectly accurate results all the time. At least for vehicle trips, this may be due in part to differences in driver habits / behaviour, unknown timing of traffic signals, and unobservable traffic, road, and / or weather conditions in travel tim...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G01C21/34
CPCG06Q10/047G01C21/34
Inventor D·伍德亚德E·J·霍维茨G·诺根P·B·科驰D·拉兹M·格尔德兹米特
Owner MICROSOFT TECH LICENSING LLC
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