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dc.contributor.authorChiou, Yu-Chiunen_US
dc.contributor.authorLan, Lawrence W.en_US
dc.contributor.authorTseng, Chun-Mingen_US
dc.date.accessioned2014-12-08T15:36:47Z-
dc.date.available2014-12-08T15:36:47Z-
dc.date.issued2014-10-02en_US
dc.identifier.issn1547-2450en_US
dc.identifier.urihttp://dx.doi.org/10.1080/15472450.2013.806764en_US
dc.identifier.urihttp://hdl.handle.net/11536/25159-
dc.description.abstractIn this study, a novel method is proposed to predict the traffic features in a long freeway corridor with a number of time steps ahead. The proposed method, on the basis of rolling self-structured traffic patterns, utilizes the growing hierarchical self-organizing map model to partition the unlabeled traffic patterns into an appropriate number of clusters and then develops the genetic programming model for each cluster to predict its corresponding traffic features. For demonstration, the proposed method is tested against a 110-km freeway stretch, on which 48 time steps of 5-min traffic flows are predicted (i.e., a 4-h prediction). The prediction accuracy of the proposed method is compared with other models (ARIMA, SARIMA, and naive models) and the results support the superiority of the proposed method. Further analyses indicate that applications of the proposed method to larger scale freeway networks require sufficient lengths of observation to acquire enough traffic patterns for training and validation in order to achieve higher prediction accuracy.en_US
dc.language.isoen_USen_US
dc.subjectRolling Self-Structured Traffic Patternsen_US
dc.subjectTraffic Predictionen_US
dc.subjectGrowing Hierarchical Self-Organizing Mapen_US
dc.subjectGenetic Programmingen_US
dc.titleA Novel Method to Predict Traffic Features Based on Rolling Self-Structured Traffic Patternsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/15472450.2013.806764en_US
dc.identifier.journalJOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMSen_US
dc.citation.volume18en_US
dc.citation.issue4en_US
dc.citation.spage352en_US
dc.citation.epage366en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000342502300004-
dc.citation.woscount2-
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