Print Email Facebook Twitter Efficient real time OD matrix estimation based on principal component analysis Title Efficient real time OD matrix estimation based on principal component analysis Author Djukic, T. Flötteröd, G. Van Lint, H. Hoogendoorn, S.P. Faculty Civil Engineering and Geosciences Department Transport & Planning Date 2012-10-31 Abstract In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy. Next, we define a new transformed set of variables (demand principal components) that is used to represent the OD demand in lower dimensional space. We use these new variables as state variable in a novel reduced state space model for real time estimation of OD demand. Through an example we demonstrate the quality improvement of OD estimates using this new formulation and a so-called ‘colored’ Kalman filter over the standard Kalman filter approach for OD estimation, when correlated measurement noise is accounted due to reduction of variables in state vector. To reference this document use: http://resolver.tudelft.nl/uuid:57d0cff6-9a0e-4f32-aa43-1c7a94f0ae40 Publisher TRAIL research school Source Trail-beta congress 2012, “Mobility & Logistics - Science meets Practice”, October 30-31, 2012, De Kuip, Rotterdam Part of collection Institutional Repository Document type conference paper Rights (c) 2012 Djukic, T.TRAIL Research School Files PDF 292131.pdf 1.49 MB Close viewer /islandora/object/uuid%3A57d0cff6-9a0e-4f32-aa43-1c7a94f0ae40/datastream/OBJ/view