基于遗传算法优化XGBoost模型的地铁乘客出站走行时间预测
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

U293.1+3

基金项目:

浙江省’尖兵’’领雁’研发攻关计划(2022C01105);陕西省自然科学基金(2023-JC-YB-588);陕西省社会科学基金(2022F021)


GUO Kai-xuan1, XIAO Mei2*, LIU Yu3, ZHANG Hao4(School of Transportation Engineering, Chang’an University, Shaanxi 710064, China;
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    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秒,低于未优化的极端梯度提升模型(1.87秒)、支持向量机(2.03秒)和随机森林(1.96秒)等模型。

    Abstract:

    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 seconds, lower than the unoptimized Extreme Gradient Boosting model (1.87 seconds), support vector machine (2.03 seconds) and random forest (1.96 seconds) models.

    参考文献
    相似文献
    引证文献
引用本文

郭凯旋,肖梅,刘宇,等. 基于遗传算法优化XGBoost模型的地铁乘客出站走行时间预测[J]. 科学技术与工程, 2024, 24(18): 7851-7858.
Guo Kaixuan, Xiao Mei, Liu Yu, et al. GUO Kai-xuan1, XIAO Mei2*, LIU Yu3, ZHANG Hao4(School of Transportation Engineering, Chang’an University, Shaanxi 710064, China;[J]. Science Technology and Engineering,2024,24(18):7851-7858.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-07-03
  • 最后修改日期:2024-04-03
  • 录用日期:2023-11-14
  • 在线发布日期: 2024-07-05
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注
《科学技术与工程》入选维普《中文科技期刊数据库》自然科学类期刊月度下载排行榜TOP10