Femke van Wageningen-Kessels

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Freeway traffic state estimation and prediction are central components in real-time traffic management and information applications. Model-based traffic state estimators consist of a dynamic model for the state variables (e.g., a first- or second-order macroscopic traffic flow model), a set of observation equations relating sensor observations to the system(More)
be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates than in Eulerian (space-time) coordinates. This paper investigates the opportunities of the Lagrangian form for traffic state estimation in freeway networks. We propose a new model-based state estimator where the discretized Lagrangian model is used as(More)
We propose and analyze a generic multi-class kinematic wave traffic flow model: Fastlane. The model takes into account heterogeneity among driver-vehicle units with respect to speed and space occupancy: long vehicles with large headways (e.g. trucks) take more space than short vehicles with short headways (e.g. passenger cars). Moreover, and this is what(More)
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