交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 86-91.DOI: 10.16097/j.cnki.1009-6744.2025.01.009

• 智能交通系统与信息技术 • 上一篇    下一篇

数据驱动的城轨车站行人仿真方法研究

韩烽凡1,刘爽*2a,朱亚迪2b,杨烨3,王曦4   

  1. 1. 天津市交通科学研究院,天津300074;2.北京交通大学,a.交通运输学院,b.土木建筑工程学院,北京100044;3. 北京车网科技发展有限公司,北京100176;4.北京城建交通设计研究院有限公司,北京100037
  • 收稿日期:2024-11-26 修回日期:2024-12-12 接受日期:2024-12-14 出版日期:2025-02-25 发布日期:2025-02-21
  • 作者简介:韩烽凡(1998—),男,河北定州人,助理工程师。
  • 基金资助:
    国家自然科学基金(52202385)。

Data-driven Pedestrian Simulation Method for Urban Rail Transit Station

HAN Fengfan1, LIU Shuang*2a, ZHU Yadi2b, YANG Ye3, WANG Xi4   

  1. 1. Tianjin Transportation Research Institute, Tianjin 300074, China; 2a. School of Traffic and Transportation, 2b. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; 3. Beijing Chewang Technology Development Co Ltd, Beijing 100176, China; 4. Beijing Urban Construction Transport Planning & Institute Co Ltd, Beijing 100037, China
  • Received:2024-11-26 Revised:2024-12-12 Accepted:2024-12-14 Online:2025-02-25 Published:2025-02-21
  • Supported by:
    National Natural Science Foundation of China (52202385)。

摘要: 为解决传统的城市轨道交通车站行人仿真中个体运动轨迹与真实行走轨迹差异较大的问题,同时提高行人仿真结果与真实轨迹的匹配度,本文基于循环神经网络模型,融合传统社会力模型中行人之间以及行人与障碍物之间的交互机制和视角机制,构建了一种基于数据驱动的行人仿真方法。同时,利用残差网络结构考虑城市轨道交通车站内复杂场景对行人行走轨迹的影响,利用条件变分自编码器融合局部路径终点预测,进一步提高仿真结果的准确度。采用国际公共数据集,以及中国某城市地铁车站视频监控数据进行模型训练,验证了方法的有效性。仿真结果表明:本文提出的仿真方法相较于传统社会力方法以及其他研究方法的平均位移误差和终点位移误差至少减少了11.9%和10.2%;结合场景信息和终点信息修正,进一步减少了平均位移误差和终点位移误差28.1%和25.9%,验证了本文方法的有效性。

关键词: 交通工程, 行人仿真模型, 深度学习, 乘客行为, 城市轨道交通

Abstract: To address the significant deviations observed between simulated and actual pedestrian trajectories in urban rail transit stations with traditional pedestrian simulation models, this study proposes a data-driven pedestrian simulation method based on a recurrent neural network (RNN). The approach integrates interaction mechanisms between pedestrians and obstacles, perspective mechanisms, and destination attraction mechanisms derived from the traditional social force model to enhance the alignment between simulation results and real trajectories. Additionally, a residual network structure is employed to account for the impact of complex station environments on pedestrian movement. A conditional variational autoencoder (CVAE) is used to incorporate local path endpoint predictions, further improving the accuracy of simulation outcomes. The proposed method is validated by using several public datasets and video surveillance data from subway stations in a Chinese city. Simulation results demonstrate that, compared with the traditional social force model and other methods, the proposed approach reduces the average displacement error and endpoint displacement error by at least 11.9% and 10.2%, respectively. Moreover, the incorporation of scene information and endpoint information corrections decreases the up two errors by an additional 28.1% and 25.9%, respectively, confirming the effectiveness of the proposed method.

Key words: traffic engineering, pedestrian simulation method, deep learning, passenger walking behavior, urban rail transit

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