標題: CONFINED SPACE FUZZY PROPORTION MODEL FOR SHORT-TERM TRAFFIC FLOW PREDICTION
作者: Lan, Lawrence W.
Lin, Feng-Yu
Huang, Yi-San
運輸與物流管理系 註:原交通所+運管所
Department of Transportation and Logistics Management
關鍵字: Chaotic time series;Prediction;Short-term traffic flow;Similarity reasoning
公開日期: 2004
摘要: This 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.
URI: http://hdl.handle.net/11536/18174
ISBN: 978-988-97563-6-9
期刊: TRANSPORTMETRICA: ADVANCED METHODS FOR TRANSPORTATION STUDIES
起始頁: 370
結束頁: 379
Appears in Collections:Conferences Paper