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Prediction of Divided Traffic Demands Based on Knowledge Discovery at Expressway Toll Plaza
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 Title & Authors
Prediction of Divided Traffic Demands Based on Knowledge Discovery at Expressway Toll Plaza
Ahn, Byeong-Tak; Yoon, Byoung-Jo;
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 Abstract
The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.
 Keywords
K-nearest neighbors;Non-parametric approach;Multivariate in-and-out model;Forecasting divided traffic demands;
 Language
Korean
 Cited by
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