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Title of Journal: J Mar Sci Technol

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Abbravation: Journal of Marine Science and Technology

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Springer Japan

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DOI

10.1001/archpsyc.60.1.13

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1437-8213

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Shortterm prediction of vehicle waiting queue at

Authors: Weibin Zhang Yajie Zou Jinjun Tang John Ash Yinhai Wang
Publish Date: 2016/05/10
Volume: 21, Issue: 4, Pages: 729-741
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Abstract

Ferry service plays an important role in several cities with waterfront areas Transportation authorities often need to forecast volumes of vehicular traffic in queues waiting to board ships at ferry terminals to ensure sufficient capacity and establish schedules that meet demand Several previous studies have developed models for longterm vehicle queue length prediction at ferry terminals using terminal operation data Few studies however have been undertaken for shortterm vehicular queue length prediction In this study machine learning methods including the artificial neural network ANN and support vector machine SVM are applied to predict vehicle waiting queue lengths at ferry terminals Through time series analysis the existence of a periodic queuelength pattern is established Hence methodologies used in this study take into account periodic features of vehicle queue data at terminals for prediction To further consider the cyclical characteristics of vehicle queue data at ferry terminals a prediction approach is proposed to decompose vehicle waiting queue length into two components a periodic part and a dynamic part A trigonometric regression function is introduced to capture the periodic component and the dynamic part is modeled by SVM and ANN models Moreover an assembly technique for combining SVM and ANN models is proposed to aggregate multiple prediction models and in turn achieve better results than could be attained from a lone predictive method The prediction results suggest that for multistep ahead vehicle queue length prediction at ferry terminals the ensemble model outperforms the separate prediction models and the hybrid models especially as prediction step size increases This research has important practical significance to both traffic service management interests and the travelers in cities along waterfront areas


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