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<p>Modern campuses integrate various urban infrastructures such as residential, transportation, commercial, and service facilities. Research on personnel behavior within campuses provides valuable reference for campus management, resource allocation, and spatial planning. The integration of multi-source data enables a more comprehensive and nuanced analysis of the interplay between human behavior and spatial distribution, offering deeper and more objective insights into these phenomena. This study focuses on the Wuxi campus of Southeast University, combining Wi-Fi probe localization technology with a custom-developed physical environment sensor system. Based on the pre-experiment monitoring of pedestrian flows, high-traffic public spaces were selected for analysis. Over a period of 14&#xa0;days, 28.87 million raw localization data points and 340,000 raw environmental data points were collected. To ensure the accuracy and reliability of the data, rigorous data cleaning procedures were employed, including the time restrictions, anomalous data, drift, redundant and ping-pong effects. Both the localization data and the physical environment data were subsequently visualized, allowing for the detailed examination of temporal and spatial behavioral characteristics of campus occupants and the corresponding environmental attributes of public spaces. By fitting the two heterogeneous datasets, the study revealed a correlation between the spatial distribution of pedestrian stopping behavior and physical environmental factors. Additionally, a convolutional autoencoder neural network was used to extract both 2D and 3D features of pedestrian trajectories, yielding four typical spatiotemporal patterns of pedestrian movement. These findings provide a robust foundation for improving the physical environment of the campus and informing crowd management strategies, while also offering critical insights for future campus planning and the optimization of spatial resources.</p>

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Analysis of campus crowd behaviour based on location data and physical environment data: a case study of Southeast University Wuxi Campus

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月26日

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  • Ye Tang,
  • Junqiang Sun,
  • Guangjin Wang,
  • Wenjin Hong,
  • Li Li

摘要

Modern campuses integrate various urban infrastructures such as residential, transportation, commercial, and service facilities. Research on personnel behavior within campuses provides valuable reference for campus management, resource allocation, and spatial planning. The integration of multi-source data enables a more comprehensive and nuanced analysis of the interplay between human behavior and spatial distribution, offering deeper and more objective insights into these phenomena. This study focuses on the Wuxi campus of Southeast University, combining Wi-Fi probe localization technology with a custom-developed physical environment sensor system. Based on the pre-experiment monitoring of pedestrian flows, high-traffic public spaces were selected for analysis. Over a period of 14 days, 28.87 million raw localization data points and 340,000 raw environmental data points were collected. To ensure the accuracy and reliability of the data, rigorous data cleaning procedures were employed, including the time restrictions, anomalous data, drift, redundant and ping-pong effects. Both the localization data and the physical environment data were subsequently visualized, allowing for the detailed examination of temporal and spatial behavioral characteristics of campus occupants and the corresponding environmental attributes of public spaces. By fitting the two heterogeneous datasets, the study revealed a correlation between the spatial distribution of pedestrian stopping behavior and physical environmental factors. Additionally, a convolutional autoencoder neural network was used to extract both 2D and 3D features of pedestrian trajectories, yielding four typical spatiotemporal patterns of pedestrian movement. These findings provide a robust foundation for improving the physical environment of the campus and informing crowd management strategies, while also offering critical insights for future campus planning and the optimization of spatial resources.