Research on inland waterway congestion prediction method based on traffic wave theory
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摘要: 为实现对航道拥塞度的预测，基于交通波理论提出一种考虑最大排队长度的拥塞度预测方法。模型基于船舶自动识别系统（AIS）数据提取交通流特征参数，结合船舶在不同水域的航行行为差异，提出航路特征区域划分方法。在此基础上，选取交通波理论中排队长度作为拥塞度评价指标，提出基于高斯过程回归的最大排队长度预测方法，实现对航道拥塞程度的预测。针对长江流域裕溪河段开展案例研究，结果表明：该航段2020年7月最大排队长度理论值为0.98 km,建立回归模型的Adjusted R2为0.88,预测最大排队长度1.34 km,与理论值误差0.37 km。该模型具有较高的可解释性，能实现对航道拥塞度的预测，本研究可为海事监管服务水平提升提供理论依据。Abstract: Existing studies indicate that the longer the queue length of vessels, the greater the channel saturation. To predict channel congestion, this study proposes a congestion prediction method that considers the maximum queue length based on the fundamental principles of traffic wave theory. The model utilizes Automatic Identification System（AIS） data to extract traffic flow characteristic parameters and, considering the differences in navigation behavior among ships in different waters, proposes a method for dividing the channel into characteristic areas. The queue length in traffic wave theory is selected as the evaluation index for congestion, and a method for predicting the maximum queue length based on Gaussian process regression is proposed to achieve the prediction of waterway congestion levels. A case study is conducted in the Yuxi River section of the Yangtze River Basin. The results show that the theoretical value of the maximum queue length in this section in July 2020 is 0.98 km, and the Adjusted R2 index of the established regression model is 0.88, predicting a maximum queue length of 1.34 km with an error of 0.37 km compared to the theoretical value. The research results demonstrate that the proposed model has a high degree of interpretability and can effectively predict the maximum queue length, thereby enabling the prediction of channel congestion. This study provides a theoretical basis for improving the level of maritime supervision services.
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