地铁乘客出站走行时间的预测是城市交通运行和管理的重要依据，对其进行准确预测有助于缓解地铁拥堵、优化地铁服务和提高乘客满意度。为了准确预测地铁乘客出站走行时间，首先，基于视频分析软件从监控视频中提取了乘客出站时的走行时间和若干特征变量。其次，为了筛选出对走行时间有显著影响的因素，采用相关性分析和最优尺度回归模型进行影响因素分析，并使用遗传算法进行最优特征组合的提取。最终，将提取出的特征作为输入向量，使用极端梯度提升模型（extreme gradient boosting, XGBoost）进行走行时间的预测，并以平均绝对误差等作为评价指标。实验结果表明，本文提出的方法在地铁乘客出站行为预测方面具有较好的效果，平均绝对误差为1.55 s，低于未优化的极端梯度提升模型（1.87 s）、支持向量机（2.03 s）和随机森林（1.96 s）等模型。

The prediction of subway passengers' walking time during exit is an important basis for urban traffic operation and management. Accurate prediction can help alleviate subway congestion, optimize subway services, and improve passenger satisfaction. Firstly, video analysis software was used to extract the walking time and several characteristic variables of passengers during exit from surveillance videos. Secondly, in order to screen out the factors that have a significant impact on walking time, correlation analysis and optimal scaling regression model were used for factor analysis, and genetic algorithm was used to extract the optimal feature combination. Finally, the extracted features were used as input vectors and the Extreme Gradient Boosting model was used to predict walking time, with mean absolute error as the evaluation index. The experimental results show that the method proposed in this paper has good effect in predicting the behavior of subway passengers during exit, with a mean absolute error of 1.55 s, lower than the unoptimized Extreme Gradient Boosting model (1.87 s), support vector machine (2.03 s) and random forest (1.96 s) models.