基于“21世纪海上丝绸之路”AIS数据的船舶交通流预测

doi:10.12052/gdutxb.200076

摘要/Abstract

摘要： 为探究“21世纪海上丝绸之路”船舶交通流规律，基于2018年“21世纪海上丝绸之路”AIS（Automatic Identification System，船舶自动识别系统）数据，利用时间序列模型分别对货船、油轮和货船-油轮这3种情形下的船舶交通流进行了研究。结果显示，船舶交通流变化规律可以用ARIMA模型（Auto-regressive Intergrated Moving Average Model，即差分自回归移动平均模型），拟合并预测；货船、油轮和货船-油轮这3种情形有相同的最优选择模型ARIMA（1，1，2）。“21世纪海上丝绸之路”船舶交通流可由前两个时间周期内的交通流数据拟合预测，并且ARIMA（1，1，2）模型对单一船型交通流的预测效果优于对混合船型交通流的预测。

关键词: AIS数据, 时间序列模型, 船舶交通流

Abstract: This paper aims to explore the law of vessel traffic flow on the “21st Century Maritime Silk Road”. Based on the Automatic Identification System data of the “21st Century Maritime Silk Road” in 2018, the time series model is used to analyze the vessel traffic flow of three situations of cargo ships, tankers and cargo ships-tankers. The research shows that: 1) The changes in vessel traffic flow can be fitted and predicted by using the differential autoregressive moving average model (ARIMA). 2) Three situations have the same optimal selection model ARIMA (1, 1, 2). The results show that the current vessel traffic flow of “21st Century Maritime Silk Road” can be predicted by the traffic flow in the last two time periods. Three situations all have relatively accurate prediction results, and the prediction effect of the ARIMA (1, 1, 2) model on the single ship traffic flow is better than that of the mixed ship traffic flow.

Key words: AIS (Automatic Identification System) data, time series model, vessel traffic flow

中图分类号:

U692

李振福, 段伟, 李肇坤, 邓昭. 基于“21世纪海上丝绸之路”AIS数据的船舶交通流预测[J]. 广东工业大学学报, 2020, 37(06): 1-8.

Li Zhen-fu, Duan Wei, Li Zhao-kun, Deng Zhao. Vessel Traffic Flow Forecast Based on AIS Data of “21st Century Maritime Silk Road”[J]. Journal of Guangdong University of Technology, 2020, 37(06): 1-8.

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