Browsing by Author "Tseng, Chun-Ming"

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A Novel Method to Predict Traffic Features Based on Rolling Self-Structured Traffic Patterns
(2014-10-02)
Chiou, Yu-Chiun
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Lan, Lawrence W.
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Tseng, Chun-Ming
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交大名義發表
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運輸與物流管理系註：原交通所+運管所
In 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.
ESTIMATION OF FREEWAY DYNAMIC ORIGIN-DESTINATION MATRICES: A NOVEL APPROACH
(2010)
Chiou, Yu-Chiun
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Lan, Lawrence W.
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Tseng, Chun-Ming
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This paper proposes a novel approach to estimate medium-to-long term freeway dynamic origin-destination (O-D) matrices. The proposed approach includes a two-stage prediction model with an integrated algorithm. The two-stage prediction model uses K-means algorithm to extract clusters of traffic patterns and then employs genetic programming to predict the traffic in each cluster. The integrated algorithm combines cell transmission model with extended Kalman filtering to estimate the arrival distributions and the O-D proportions. To demonstrate the applicability of the proposed approach, a field study of on-ramp traffic patterns on a freeway is examined. The results show that the proposed approach can accurately predict the traffic and satisfactorily estimate the O-D proportions along a freeway.