Travel time forecasting from clustered time series via optimal filtering fusion

Abstract

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.

Date
Oct 16, 2015 11:00 AM — 12:00 PM
Location
Los Angeles, USA
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