27 January 1998 Kalman filter approach to traffic modeling and prediction
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Proceedings Volume 3207, Intelligent Transportation Systems; (1998) https://doi.org/10.1117/12.300860
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States
Abstract
The objective of our work has been to develop and integrate prediction, control and optimization modules for use in highway traffic management. This is accomplished through the use of the Semantic Control paradigm, implementing a hybrid prediction/routing/control system, to model both macro-level as well a micro level. This paper addresses the design and operation of a Kalman filter that processes traffic sensor data in order to model and predict highway traffic volume. This data was given in the form of hourly traffic flow, and has been fit using a cubic spline method to allow observations at various time intervals. THe filter is augmented via the Method of Sage and Husa to identify the parameters of the system noise on-line, and to determine the dynamics of the traffic process iteratively to aid in the prediction of the future traffic. The results show a good ability to predict traffic flow at the sensors for several time periods in the future, as well as some noise rejection capabilities.
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Gregory J. Grindey, Gregory J. Grindey, S. Massoud Amin, S. Massoud Amin, Ervin Y. Rodin, Ervin Y. Rodin, Asdrubal Garcia-Ortiz, Asdrubal Garcia-Ortiz, } "Kalman filter approach to traffic modeling and prediction", Proc. SPIE 3207, Intelligent Transportation Systems, (27 January 1998); doi: 10.1117/12.300860; https://doi.org/10.1117/12.300860
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