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dc.contributor.authorChan, Kit Yan
dc.contributor.authorDillon, Tharam
dc.contributor.authorSingh, Jaipal
dc.contributor.authorChang, Elizabeth
dc.identifier.citationChan, Kit Yan and Dillon, Tharam S. and Singh, Jaipal and Chang, Elizabeth. 2012. Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm. IEEE Transactions on Intelligent Transportation Systems. 13 (2): pp. 644-654.

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.

dc.publisherIEEE Intelligent Transportation Systems Society
dc.subjectneural networks (NNs)
dc.subjectExponential smoothing method
dc.subjectshort-term - traffic flow forecasting
dc.subjectLevenberg-Marquardt (LM) algorithm
dc.titleNeural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Intelligent Transportation Systems

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curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access

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