基于遗传算法的集装箱铁水联运运量组合预测

Combined Forecasting of Container Rail-Water Intermodal Transport Volume Based on Genetic Algorithm

集装箱铁水联运是多式联运的重要形式之一,有利于充分发挥铁路运输在内贸运输和外贸集疏运方面大运量、低能耗的优势,是实现高效物流的有效途径。根据铁水联运运量数据小样本和不稳定的特点,将三次指数平滑方法、多元线性回归方法、BP神经网络方法组合,并通过遗传算法为单一预测方法赋权,建立了组合预测模型。以2011~~2020年铁水联运相关统计数据对模型进行实证分析,结果证明组合模型能在一定程度上提高预测精度和稳定性。

Container Rail-Water intermodal transport is one of the important forms of multimodal transport.It is conducive to give full play to the advantages of railway transportation in terms of large volume and low energy consumption in domestic and foreign trade transportation. It is also an effective way to achieve efficient logistics. The traffic volume data of rail-water intermodal transportation has the characteristics of small sample and instability. Therefore, this paper combines the cubic exponential smoothing method, multiple linear regression method and BP neural network method to establish a combination forecasting model. The weight of combination forecasting model is assigned by genetic algorithm. This paper makes an empirical analysis based on the statistics of rail-water intermodal transportation in 2011-2020. And the results show that the combination forecasting model can improve the accuracy and stability to a certain extent.