Abstract
Vessel traffic flow forecasting is of significant importance for the water transport safety, especially in the multi-bridge water areas. An improved Kalman model combining regression analysis and Kalman filtering is proposed for short-term vessel traffic flow forecasting between Wuhan Yangtze River Bridge (hereafter WYRB) and the Second Wuhan Yangtze River Bridge (hereafter SWYRB). Given the vessel traffic flow of WYRB is positively correlated with that of SWYRB, its regression coefficient is obtained as well as the regression predictions. The predictions are further used to replace the state transition equation of Kalman filtering. The prediction results of the improved Kalman model demonstrate better agreements with field observations, and hence, illustrate good capability of the proposed method in the short-term traffic flow forecasting. The discrepancy between the model predictions and field observations is generally attributed to the inherent deficiency of Kalman filtering method and the errors resulted from automatic identification system (AIS) data (e.g. missed AIS data). The proposed method can provide a support for the real-time and accurate basis for the ship traffic planning management.
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Acknowledgements
This study is supported by the National Natural Science Foundation of China (No. 51479155) and the Fundamental Research Funds for the Central Universities, and Fujian Province Natural Science Foundation (No. 2015J05108), and Fuzhou Science and Technology Planning Project (No. 2016S117), and Young Teachers Education Research Project of Department of Education of Fujian Province (No. JK2017038, JAT170439), and the 2017th Outstanding Young Scientist Training Program of Colleges in Fujian Province, Fujian College’s Research Base of Humanities and Social Science for Internet Innovation Research Center (Minjiang University) (No. IIRC20170104), Yingcai project of CUMT (YC2017001), UOW Vice-Chancellor’s Postdoctoral Research Fellowship and PAPD. The authors would like to express their thanks to Wuhan Maritime Bureau for providing AIS data.
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He, W., Zhong, C., Sotelo, M.A. et al. Short-term vessel traffic flow forecasting by using an improved Kalman model. Cluster Comput 22 (Suppl 4), 7907–7916 (2019). https://doi.org/10.1007/s10586-017-1491-2
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DOI: https://doi.org/10.1007/s10586-017-1491-2