日時 : 2025年5月27日（火）16:00 ~ 17:30
場所 : 東北大学情報科学研究科棟2階大講義室
講演者 : Jeng-Min Chiou 教授 (Distinguished Professor & Director, Institute of Statistics and Data Science, National Taiwan University)
タイトル : Functional Regression with Derivatives for Traffic Prediction
概要 : To predict traffic congestion within a short time frame, we propose a generalized functional linear regression model using traffic speed trajectories and their first two derivatives as functional predictors. As the derivatives are not directly observed, they are estimated and represented, along with the trajectories, via the Karhunen–Loève expansion. The model captures both integrated and individual predictor effects, with weight parameters indicating their relative importance. An application to freeway data shows that incorporating trajectory derivatives improves prediction accuracy.
