Adaptive Kalman filtering for multi-step ahead traffic flow prediction

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...

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Bibliographic Details
Published in2013 American Control Conference pp. 4724 - 4729
Main Authors Ojeda, Luis Leon, Kibangou, Alain Y., de Wit, Carlos Canudas
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Adaptation models
Computational modeling
Forecasting
Kalman filters
Noise
Predictive models
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Summary: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.
ISBN:1479901776
9781479901777
ISSN:0743-1619
DOI:10.1109/ACC.2013.6580568