Traffic Prediction Using Real-World Transportation Data
First Claim
1. A method comprising:
- receiving a request relating to traffic prediction, the request having an associated day and an associated time;
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
applying the first and second traffic prediction models in accordance with the determining to generate an output for use in relation to traffic prediction.
1 Assignment
0 Petitions
Accused Products
Abstract
Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data'"'"'s intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.
-
Citations
15 Claims
-
1. A method comprising:
-
receiving a request relating to traffic prediction, the request having an associated day and an associated time; 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 applying the first and second traffic prediction models in accordance with the determining to generate an output for use in relation to traffic prediction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A system comprising:
-
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. - View Dependent Claims (11, 12, 13, 14)
-
-
15-20. -20. (canceled)
Specification