Dynamic prediction of traffic volume through Kalman filtering theory

Iwao Okutani and Yorgos J. Stephanedes

Transportation Research Part B: Methodological, 1984, vol. 18, issue 1, 1-11

Abstract: Two models employing Kalman filtering theory are proposed for predicting short-term traffic volume. Prediction parameters are improved using the most recent prediction error and better volume prediction on a link is achieved by taking into account data from a number of links. Based on data collected from a street network in Nagoya City, average prediction error is found to be less than 9% and maximum error less than 30%. The new models perform substantially (up to 80%) better than UTCS-2.

Date: 1984
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