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.
This is a preview of subscription content, log in to check access.
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.
Guo J, Huang W, Williams BM (2014) Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res Part C 43:50–64CrossRefGoogle Scholar
Vlahogianni EI, Karlaftis MG, Golias JC (2014) Short-term traffic forecasting: where we are and where we’re going. Transp Res Part C 43:3–19CrossRefGoogle Scholar
Merrick JRW, van Dorp JR, Blackford JP, Shaw GL, Harrald J, Mazzuchi TA (2003) A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model. Reliab Eng Syst Saf 81:119–132CrossRefGoogle Scholar
Xie Y, Huynh N (2010) Kernel-based machine learning models for predicting daily truck volume at seaport terminals. J Transp Eng 136:1145–1152CrossRefGoogle Scholar
Zou Y, Zhang Y, Zhu X (2014) Constructing a bivariate distribution for freeway speed and headway data. Transp A Transp Sci 10(3):255–272Google Scholar
Zou Y, Zhu X, Zhang Y, Zeng X (2014) A space–time diurnal method for short-term freeway travel time prediction. Transp Res Part C Emerg Technol 43:33–49CrossRefGoogle Scholar
Hofleitner A, Herring R, Bayen A (2012) Arterial travel time forecast with streaming data: a hybrid approach of flow modeling and machine learning. Transp Res Part B 46:1097–1122CrossRefGoogle Scholar
Dimitriou L, Tsekeris T, Stathopoulos A (2008) Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transp Res Part C 16:554–573CrossRefGoogle Scholar
Wang J, Deng W, Guo Y (2014) New Bayesian combination method for short-term traffic flow forecasting. Transp Res Part C 43:79–94CrossRefGoogle Scholar
Deardorf RG (1999) Washington state ferries 20-year long-range plan. Transp Res Rec 1677:93–104CrossRefGoogle Scholar
Zhang Y, Zhang Y, Haghani A (2014) A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Transp Res Part C 43:65–78CrossRefGoogle Scholar
Moretti F, Pizzuti S, Panzieri S, Annunziato M (2015) Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing 167:3–7CrossRefGoogle Scholar
Breiman L (1999) Combining predictors, combining artificial neural nets—ensemble and modular multi-net systems. Springer, Berlin, pp 31–50zbMATHGoogle Scholar
Xia J, Chen M, Huang W (2011) A multistep corridor travel-time prediction method using presence-type vehicle detector data. J Intell Transp Syst 15(2):104–113CrossRefGoogle Scholar
Chandra SR, Al-Deek H (2009) Predictions of freeway traffic speeds and volumes using vector autoregressive models. J Intell Transp Syst 13:53–72CrossRefGoogle Scholar
Zou Y, Hua X, Zhang Y, Wang Y (2015) Hybrid short-term freeway speed prediction methods based on periodic analysis. Can J Civ Eng 42(8):570–582CrossRefGoogle Scholar
Zhang W, Goerlandt F, Montewka J, Kujala P (2015) A method for detecting possible near miss ship collisions from AIS data. Ocean Eng 107:60–69CrossRefGoogle Scholar
Gneiting T, Larson K, Westrick K, Genton MG, Aldrich E (2006) Calibrated probabilistic forecasting at the Stateline wind energy center: the regime-switching space-time method. J Am Stat Assoc 101(475):968–979MathSciNetCrossRefzbMATHGoogle Scholar
Piuri V (2003) Neural networks for instrumentation, measurement and related industrial applications. IOS, AmsterdamGoogle Scholar