An approach is provided for determining one or more real-time traffic data series associated with one or more road segments over one or more time intervals. The approach involves causing, at least in part, a determination of one or more differences of the real-time data series with one or more historical models. The approach also involves determining whether the one or more real-time difference exceeds a threshold difference. The approach involves causing, at least in part, a selection of one or more other historical models for the one or more road segments over the one or more time intervals based, at least in part, on a determination that the real-time difference exceeds the threshold difference.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: determining at least one first model and at least one second model, wherein the at least one first model and the at least one second model provide for predicting traffic information for one or more travel segments; determining at least one first confidence metric by evaluating the at least one first model against real-time traffic data associated with the one or more travel segments, wherein the real-time traffic data are collected over a predetermined previous time period; determining at least one second confidence metric by evaluating the at least one second model against the real-time traffic data; causing, at least in part, a selection of at least one champion model from among the at least one first model and the at least one second model based, at least in part, on the at least one first confidence metric and the at least one second confidence metric, wherein the least one champion model is used to predict the traffic information for the one or more travel segments based, at least in part, on the selection. 2. A method of claim 1, further comprising: causing, at least in part, a prediction of the traffic information for the one or more travel segments by decaying the at least one champion model from the real-time traffic data. 3. A method of claim 1, wherein the at least one first model is at least one default model. 4. A method of claim 3, wherein the at least one first model further was designated as at least one previous champion model for the predetermined previous time period, the method further comprising: causing, at least in part, a selection of the at least one second model over the at least one first model as the at least one champion model if the at least one second confidence metric greater than the at least one first confidence by at least one threshold value that is greater than if the at least one first model were not the at least one default model. 5. A method of claim 3, wherein the at least one default model is at least one noncongestion model. 6. A method of claim 1, wherein the at least one champion model is not at least one default model, the method further comprising: causing, at least in part, a reversion of the at least one champion model to that at least one default model over one or more subsequent epochs for predicting the traffic information for the one or more travel segments. 7. A method of claim 6, wherein the reversion of the at least one champion model comprises: causing, at least in part, a transitioning of the at least one champion model to the at least one default model via at least one transition model for predicting the traffic information. 8. A method of claim 1, further comprising: determining that one or more future epochs have been designated as one or more congestion periods; and causing, at least in part, a restriction of the selection of the at least one champion model to one or more congestion-based models for predicting the traffic information for the one or more future epochs. 9. A method of claim 8, wherein the selection of the at least one champion model is further restricted to one or more default models for predicting the traffic information for the one or more future epochs. 10. A method of claim 1, further comprising: causing, at least in part, an updating of the selection of the at least one champion model periodically, continuously, on demand, or a combination thereof. 11. An apparatus comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine at least one first model and at least one second model, wherein the at least one first model and the at least one second model provide for predicting traffic information for one or more travel segments; determine at least one first confidence metric by evaluating the at least one first model against real-time traffic data associated with the one or more travel segments, wherein the real-time traffic data are collected over a predetermined previous time period; determine at least one second confidence metric by evaluating the at least one second model against the real-time traffic data; cause, at least in part, a selection of at least one champion model from among the at least one first model and the at least one second model based, at least in part, on the at least one first confidence metric and the at least one second confidence metric, wherein the least one champion model is used to predict the traffic information for the one or more travel segments based, at least in part, on the selection. 12. An apparatus of claim 11, wherein the apparatus is further caused to: cause, at least in part, a prediction of the traffic information for the one or more travel segments by decaying the at least one champion model from the real-time traffic data. 13. An apparatus of claim 11, wherein the at least one first model is at least one default model. 14. An apparatus of claim 13, wherein the at least one first model further was designated as at least one previous champion model for the predetermined previous time period, the apparatus further comprising: cause, at least in part, a selection of the at least one second model over the at least one first model as the at least one champion model if the at least one second confidence metric greater than the at least one first confidence by at least one threshold value that is greater than if the at least one first model were not the at least one default model. 15. An apparatus of claim 13, wherein the at least one default model is at least one noncongestion model. 16. An apparatus of claim 11, wherein the at least one champion model is not at least one default model, the apparatus further comprising: cause, at least in part, a reversion of the at least one champion model to that at least one default model over one or more subsequent epochs for predicting the traffic information for the one or more travel segments. 17. An apparatus of claim 11, wherein the reversion of the at least one champion model comprises: cause, at least in part, a transitioning of the at least one champion model to the at least one default model via at least one transition model for predicting the traffic information. 18. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps: determining at least one first model and at least one second model, wherein the at least one first model and the at least one second model provide for predicting traffic information for one or more travel segments; determining at least one first confidence metric by evaluating the at least one first model against real-time traffic data associated with the one or more travel segments, wherein the real-time traffic data are collected over a predetermined previous time period; determining at least one second confidence metric by evaluating the at least one second model against the real-time traffic data; causing, at least in part, a selection of at least one champion model from among the at least one first model and the at least one second model based, at least in part, on the at least one first confidence metric and the at least one second confidence metric, wherein the least one champion model is used to predict the traffic information for the one or more travel segments based, at least in part, on the selection. 19. A non-transitory computer-readable storage medium of claim 18, further comprising: causing, at least in part, a prediction of the traffic information for the one or more travel segments by decaying the at least one champion model from the real-time traffic data. 20. A non-transitory computer-readable storage medium of claim 18, wherein the at least one first model is at least one default model. 21-48. (canceled)