Method for predicting travel times using autoregressive models
First Claim
1. A method for predicting a future travel time on a link, comprising:
- a training phase comprising the steps of;
collecting training travel times on the link for training seasonal intervals;
determining training inflows from the training travel times;
estimating a seasonal component of the training inflows;
subtracting the seasonal component from the training inflows to obtain training deviations from the training inflows;
determining statistics from the training deviations, wherein the seasonal components and the statistics form a model of traffic flow on the link; and
a prediction phase comprising the steps of;
collecting current travel times on the link for current seasonal intervals;
determine current inflows from the current travel times;
subtracting, for a most recent travel time from a most recent inflow to obtain a current deviation;
estimating, for a future time a predicted deviation using the statistics;
adding the seasonal component to the predicted deviation to obtain a predicted inflow; and
determining the future travel time from the predicted inflow.
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Abstract
Future travel times along links are predicted using training and prediction phases. During training, seasonal intervals, a seasonal component of the training inflows are learned. The seasonal component is subtracted from the training inflows to obtain training deviations from the training inflows to yield statistics, which along with the seasonal components form a model of traffic flow on the link. During prediction, current travel times on the link are collected for current seasonal intervals to determine current inflows. A most recent travel time is subtracted from a most recent inflow to obtain a current deviation. For a future time, a predicted deviation is estimated using the statistics. The seasonal component is added to the predicted deviation to obtain a predicted inflow from which the future travel time is predicted.
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Citations
10 Claims
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1. A method for predicting a future travel time on a link, comprising:
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a training phase comprising the steps of; collecting training travel times on the link for training seasonal intervals; determining training inflows from the training travel times; estimating a seasonal component of the training inflows; subtracting the seasonal component from the training inflows to obtain training deviations from the training inflows; determining statistics from the training deviations, wherein the seasonal components and the statistics form a model of traffic flow on the link; and a prediction phase comprising the steps of; collecting current travel times on the link for current seasonal intervals; determine current inflows from the current travel times; subtracting, for a most recent travel time from a most recent inflow to obtain a current deviation; estimating, for a future time a predicted deviation using the statistics; adding the seasonal component to the predicted deviation to obtain a predicted inflow; and determining the future travel time from the predicted inflow. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification