Short-term traffic prediction in road traffic control centres

Efficient traffic state estimation based on a localized deterministic Ensemble Kalman Filter

Master thesis (2015)

Authors

Contributors

J.W.C. Van Lint (mentor)
Y. Yuan (mentor)
H. Taale (mentor)
J.H. Baggen (mentor)
Department
Transport, Infrastructure & Logistics (Delft University of Technology) (TU Delft)
Copyright: © 2015 Scholten, F.J.M.
Traffic state estimation Dynamic traffic management Ensemble Kalman Filter Traffic state prediction
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Published Date

17-06-2015

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Faculty

Delft University of Technology

Department

Transport, Infrastructure & Logistics

Abstract

Traffic state estimations and predictions are essential parts for dynamic traffic management applications. This thesis consists of two parts: firstly the requirements for application of a traffic prediction tool in a road traffic management center are analysed, and a design is made for a tool to function in non-recurrent conditions and cope with the control measures applied by the traffic operators. The second part consists of building a prototype of an integrated estimation and prediction tool. Instead of the more standard Extended Kalman Filter, an Ensemble Kalman Filter (EnKF( is used for estimation. This EnKF is further refined by a deterministic and localised variant. From the extensive simulation experiment, it is shown that the EnKF is fast enough for real-time estimation and the accuracy is good enough in a controlled setting.