分拣中心货量调控是物流运营的关键环节,准确预测分拣中心货量对物流行业的发展具有重要意义。本文以湘西自治州为例,选取年GDP、人均GDP等8个指标作为分拣中心货量的度量,根据相关性分析,确定对货量影响显著的指标,并利用LSTM对显著指...分拣中心货量调控是物流运营的关键环节,准确预测分拣中心货量对物流行业的发展具有重要意义。本文以湘西自治州为例,选取年GDP、人均GDP等8个指标作为分拣中心货量的度量,根据相关性分析,确定对货量影响显著的指标,并利用LSTM对显著指标进行预测,接着基于RBF对2024~2028年分拣中心的货量进行预测,最后进行精度检验,预测精度对比分析表明,通过指标预测的方式相对直接预测货量具有更高的预测精度,更适合用于分拣中心货量的预测。The regulation of cargo volume in sorting centers is a key link in logistics operation. Accurate prediction of cargo volume in sorting centers is of great significance for the development of the logistics industry. Taking Xiangxi Autonomous Prefecture as an example, this paper selects eight indicators such as annual GDP and per capita GDP as the measurement of cargo volume in sorting centers. According to correlation analysis, the indicators that have significant impacts on cargo volume are determined. LSTM is used to predict the significant indicators. Then, based on RBF, the cargo volume of sorting centers from 2024 to 2028 is predicted. Finally, accuracy inspection is carried out. The comparison and analysis of prediction accuracy shows that the method of predicting through indicators has higher prediction accuracy than directly predicting cargo volume and is more suitable for predicting cargo volume in sorting centers.展开更多
文摘分拣中心货量调控是物流运营的关键环节,准确预测分拣中心货量对物流行业的发展具有重要意义。本文以湘西自治州为例,选取年GDP、人均GDP等8个指标作为分拣中心货量的度量,根据相关性分析,确定对货量影响显著的指标,并利用LSTM对显著指标进行预测,接着基于RBF对2024~2028年分拣中心的货量进行预测,最后进行精度检验,预测精度对比分析表明,通过指标预测的方式相对直接预测货量具有更高的预测精度,更适合用于分拣中心货量的预测。The regulation of cargo volume in sorting centers is a key link in logistics operation. Accurate prediction of cargo volume in sorting centers is of great significance for the development of the logistics industry. Taking Xiangxi Autonomous Prefecture as an example, this paper selects eight indicators such as annual GDP and per capita GDP as the measurement of cargo volume in sorting centers. According to correlation analysis, the indicators that have significant impacts on cargo volume are determined. LSTM is used to predict the significant indicators. Then, based on RBF, the cargo volume of sorting centers from 2024 to 2028 is predicted. Finally, accuracy inspection is carried out. The comparison and analysis of prediction accuracy shows that the method of predicting through indicators has higher prediction accuracy than directly predicting cargo volume and is more suitable for predicting cargo volume in sorting centers.
