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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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Petrochemical Universities / Shiyou Huagong Gaodeng Xuexiao Xuebao is the property of Journal Editorial Department Of Liaoning Shihua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

腹壁浅动脉（superficial inferior epigastric artery, SIEA）皮瓣是近年来应用于头颈, 四肢缺损修复及乳房重建的较新 型的穿支皮瓣, 由于皮瓣面积充足, 术中无需切开腹直肌鞘, 减少了术后供区并发症, 且供区瘢痕较隐蔽, 可同时取得良好的美 容效果, 获得了许多学者的关注. 现就该皮瓣的解剖, 术前检测, 临床应用及限制因素等方面作一综述. [ABSTRACT FROM AUTHOR]