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dc.contributor.authorLan, Lawrence W.en_US
dc.contributor.authorLin, Feng-Yuen_US
dc.contributor.authorHuang, Yi-Sanen_US
dc.date.accessioned2014-12-08T15:25:45Z-
dc.date.available2014-12-08T15:25:45Z-
dc.date.issued2004en_US
dc.identifier.isbn978-988-97563-6-9en_US
dc.identifier.urihttp://hdl.handle.net/11536/18174-
dc.description.abstractThis paper develops a confined space fuzzy proportion (CSFP) model to predict the short-term traffic flow dynamics. The rationale for prediction reasoning is based on the "fuzzy proportion" inference from similar historical trajectories that are within a "confined space" defined by a spatial threshold in the reconstructed state space. To perform the inference, we assign to each trajectory with unequal weight that is proportional to the relative distance to the latest (present) data vector. The United States I-35 Freeway one-minute flow data are used to construct and validate the proposed model. We employ Theil inequality coefficient (U) and its decomposed proportions to evaluate the prediction performance and to analyze the sources of prediction errors. The empirical results have demonstrated extremely high predictive capability of this CSFP model. The small bias and variance proportions of U statistic further indicate that the prediction model can successfully capture the trends and conspicuous fluctuations of one-minute traffic flow variations.en_US
dc.language.isoen_USen_US
dc.subjectChaotic time seriesen_US
dc.subjectPredictionen_US
dc.subjectShort-term traffic flowen_US
dc.subjectSimilarity reasoningen_US
dc.titleCONFINED SPACE FUZZY PROPORTION MODEL FOR SHORT-TERM TRAFFIC FLOW PREDICTIONen_US
dc.typeProceedings Paperen_US
dc.identifier.journalTRANSPORTMETRICA: ADVANCED METHODS FOR TRANSPORTATION STUDIESen_US
dc.citation.spage370en_US
dc.citation.epage379en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000261140900038-
Appears in Collections:Conferences Paper