Travel time forecasting from clustered time series via optimal filtering fusion


This talk summarizes the problem of travel time forecasting within a highway. Several measurements are captured describing travel times for multiple origin-destination (OD) pairs. A network model is then proposed to infer travel time between origin and destination based on a reduced number of states. The forecast strategy is based on current day and historical data. Historical data is organized into several clusters. For each cluster, a predictor is designed based on the Kalman filtering strategy. Then these predictions are fused, in a best linear unbiased estimation sense, in order to get the best prediction.

Oct 16, 2015 11:00 AM — 12:00 PM
460 Portola Plaza, Los Angeles, CA 90095
Click on the Video button above to view the presentation done at that time. My talk is at the end of the list.