To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

中文翻译：

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基于MIC特征选择和考虑换乘客流的ST-LightGBM轨道交通车站短期客流预测

针对目前地铁客流短期预测方法存在的影响因素不明确、精度不高、时空复杂度高等问题，在考虑换乘客流后，提出了一种基于ST-LightGBM的地铁客流预测方法。 . 首先，以历史数据为训练集，将问题转化为数据驱动的多输入单输出回归预测问题，将地铁客流短期预测问题形式化，找出问题难点. 其次，基于客流的空间转移和空间相似性，从旅客出行数据中提取可能影响地铁站客流的候选时空特征。第三，我们使用最大信息系数（MIC）特征选择算法来选择具有显着影响的特征作为输入。最后，建立了基于光梯度提升机（LightGBM）模型的地铁客流短期预测模型。考虑到中转客流，该方法时空成本低，准确率高。在厦门市连坂地铁站数据集上的实验结果表明，该方法获得了比SARIMA、SVR和BP网络更高的预测精度。