考虑周期性波动因素的船舶交通流量预测模型

为提高船舶交通流量预测精度，综合考虑季节、气候等因素，通过分析历史流量数据，在线性增长模型的基础上构建了考虑周期性波动因素的船舶交通流量预测改进模型，并运用贝叶斯估计和预测方法求解模型，提出了基于时序数据预测船舶交通流量的预测方法.实例验证表明，较传统线性增长模型，新模型更符合交通流量的实际情况，月流量预测结果的平均绝对误差下降了3.56%，标准差下降了3.79%.因此，新的预测方法用于船舶交通流量预测是有效的.
In order to improve prediction accuracy of ship traffic flow, an improved model was developed to predict ship traffic flow based on linear growth model in consideration of all periodic fluctuation factors, such as season, climate, and so on, then the Bayesian estimation and prediction were used to solve the new model, and ship traffic flow was predicted by using the time series data of ship traffic flow. Results show that the proposed model more accords with the actual situation of traffic flow comparing with the linear growth model, and the mean absolute error of monthly ship flow decreases 3.56%, and the standard deviation decreases 3.79%, therefore, it is effective to predict ship traffic flow.