Adaptive Kalman Filtering for Multi-Step ahead Traffic Flow Prediction

Luis Ramon Leon Ojeda 1, * Alain Y. Kibangou 1 Carlos Canudas de Wit 1, *
* Corresponding author
1 NECS - Networked Controlled Systems
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.
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Luis Ramon Leon Ojeda, Alain Y. Kibangou, Carlos Canudas de Wit. Adaptive Kalman Filtering for Multi-Step ahead Traffic Flow Prediction. American Control Conference (ACC 2013), Jun 2013, Washington, DC, United States. ⟨hal-00842684⟩

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