Traffic prediction using real-world transportation data
First Claim
1. A method comprising:
- receiving, by one or more computers, a request relating to traffic prediction, the request having an associated day and an associated time;
comparing, by the one or more computers, a first prediction error for a first traffic prediction model with a second prediction error for a second traffic prediction model, wherein the first traffic prediction model comprises a moving average model that exhibits increased prediction accuracy as a prediction time horizon is reduced, the second traffic prediction model comprises a historical average model that exhibits similar prediction accuracy across multiple prediction time horizons, and both the first prediction error and the second prediction error are calculated using a historical data set selected from previously recorded traffic data in accordance with the associated day and the associated time;
selecting, by the one or more computers, use of the first traffic prediction model when the first prediction error is less than the second prediction error for the associated day and the associated time;
selecting, by the one or more computers, use of the second traffic prediction model when the first prediction error is not less than the second prediction error for the associated day and the associated time; and
providing an output for use in traffic prediction by the one or more computers, wherein the output comes from applying the first traffic prediction model when the first prediction error is less than the second prediction error, and the output comes from applying the second traffic prediction model when the first prediction error is not less than the second prediction error.
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Accused Products
Abstract
Systems and techniques for enhancing accuracy of traffic prediction include a system of one or more computers operable to receive a request relating to traffic prediction, compare a first prediction error for a first (moving average) traffic prediction model with a second prediction error for a second (historical average) traffic prediction model, calculated using a historical data set selected from previously recorded traffic data in accordance with a day and time associated with the request, select use of the first model or the second model based on the comparison of prediction errors, and provide an output for use in traffic prediction, wherein the output comes from applying the first traffic prediction model when the first prediction error is less than the second prediction error, and the output comes from applying the second traffic prediction model when the first prediction error is not less than the second prediction error.
19 Citations
20 Claims
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1. A method comprising:
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receiving, by one or more computers, a request relating to traffic prediction, the request having an associated day and an associated time; comparing, by the one or more computers, a first prediction error for a first traffic prediction model with a second prediction error for a second traffic prediction model, wherein the first traffic prediction model comprises a moving average model that exhibits increased prediction accuracy as a prediction time horizon is reduced, the second traffic prediction model comprises a historical average model that exhibits similar prediction accuracy across multiple prediction time horizons, and both the first prediction error and the second prediction error are calculated using a historical data set selected from previously recorded traffic data in accordance with the associated day and the associated time; selecting, by the one or more computers, use of the first traffic prediction model when the first prediction error is less than the second prediction error for the associated day and the associated time; selecting, by the one or more computers, use of the second traffic prediction model when the first prediction error is not less than the second prediction error for the associated day and the associated time; and providing an output for use in traffic prediction by the one or more computers, wherein the output comes from applying the first traffic prediction model when the first prediction error is less than the second prediction error, and the output comes from applying the second traffic prediction model when the first prediction error is not less than the second prediction error. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a user interface device; and one or more computers operable to interact with the user interface device, the one or more computers comprising at least one processor and at least one memory device, and the one or more computers configured and arranged to perform operations comprising (i) receiving a request relating to traffic prediction, the request having an associated day and an associated time, (ii) determining how much to apply each of a first traffic prediction model and a second traffic prediction model based on previously recorded traffic data corresponding to the associated day and the associated time, wherein the first traffic prediction model comprises a moving average model that exhibits increased prediction accuracy as a prediction time horizon is reduced, and the second traffic prediction model comprises a historical average model that exhibits similar prediction accuracy across multiple prediction time horizons, and (iii) applying the first and second traffic prediction models in accordance with the determining to generate an output for use in relation to traffic prediction by the one or more computers; wherein the one or more computers are configured and arranged to perform operations comprising (i) receiving information regarding an event that has one or more attributes that are correlated with reduction in traffic flow on one or more roads of a road network approaching the event, (ii) calculating an influenced speed change and an influenced time shift, for a sensor associated with the road network, based on the information regarding the event including start time, location, direction, and severity of the event as compared with similar historical events, and (iii) using the influenced speed change and the influenced time shift in application of the first traffic prediction model. - View Dependent Claims (10, 11, 12)
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13. A system comprising:
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a user interface device; and one or more computers operable to interact with the user interface device, the one or more computers comprising at least one processor and at least one memory device, and the one or more computers configured and arranged to (i) receive a request relating to traffic prediction, the request having an associated day and an associated time, (ii) compare a first prediction error for a first traffic prediction model with a second prediction error for a second traffic prediction model, wherein the first traffic prediction model comprises a moving average model that exhibits increased prediction accuracy as a prediction time horizon is reduced, the second traffic prediction model comprises a historical average model that exhibits similar prediction accuracy across multiple prediction time horizons, and both the first prediction error and the second prediction error are calculated using a historical data set selected from previously recorded traffic data in accordance with the associated day and the associated time, (iii) select use of the first traffic prediction model when the first prediction error is less than the second prediction error for the associated day and the associated time, (iv) select use of the second traffic prediction model when the first prediction error is not less than the second prediction error for the associated day and the associated time, and (v) provide an output for use in traffic prediction by the one or more computers, wherein the output comes from applying the first traffic prediction model when the first prediction error is less than the second prediction error, and the output comes from applying the second traffic prediction model when the first prediction error is not less than the second prediction error. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification