Journal of Marine Science and Technology

, Volume 21, Issue 4, pp 729–741 | Cite as

Short-term prediction of vehicle waiting queue at ferry terminal based on machine learning method

  • Weibin Zhang
  • Yajie Zou
  • Jinjun Tang
  • John Ash
  • Yinhai Wang
Original article


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 long-term vehicle queue length prediction at ferry terminals using terminal operation data. Few studies, however, have been undertaken for short-term 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 queue-length 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 multi-step 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.


Ferry traffic Traffic prediction Machine learning Periodic analysis Algorithm composition 



This research is sponsored jointly by Fundamental Research Funds for the Central Universities of China (No. 2015KJ013), Shanghai Sailing Program (No. 16YF1411900) and University of Washington.


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Copyright information

© JASNAOE 2016

Authors and Affiliations

  • Weibin Zhang
    • 1
  • Yajie Zou
    • 2
  • Jinjun Tang
    • 3
  • John Ash
    • 1
  • Yinhai Wang
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Key Laboratory of Road and Traffic Engineering of Ministry of EducationTongji UniversityShanghaiChina
  3. 3.School of Transportation Science and EngineeringHarbin Institute of TechnologyHarbinChina

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