Analysis of Campus Crowd Behavior Based on Location Data and Physical Environment Data: a Case Study of Southeast University Wuxi Campus

EasyChair Preprint 15914

Abstract

The study on the behavior of on-campus individuals provides valuable insights for campus management, resource allocation, and planning layout. The application of multi-source data offers more objective and in-depth opportunities for exploring behavioral phenomena. Focusing on the Wuxi campus of Southeast University, this research utilized Wi-Fi probe positioning technology combined with a physical environment sensor system to comprehensively collect 28.87 million positioning data points and 340,000 environmental data points over a period of 14 days. After cleaning redundant, missing, abnormal, drifting, and ping-pong data, both types of data underwent visual analysis, and their correlations were studied. Additionally, trajectory feature extraction was conducted using a convolutional autoencoder neural network. The study ultimately revealed the temporal distribution of pedestrian flow, the spatial distribution of stopping behavior, and the spatiotemporal characteristics of pedestrian trajectories. This provides a reliable basis for guiding crowd behavior by improving specific cam-pus areas and the physical environment.

Keyphrases: Characterization, Wi-Fi probes, data correlation, data visualization, physical environment sensors

@booklet{EasyChair:15914,
  author    = {Ye Tang and Junqiang Sun and Guangjin Wang and Wenjin Hong and Li Li},
  title     = {Analysis of Campus Crowd Behavior Based on Location Data and Physical Environment Data: a Case Study of Southeast University Wuxi Campus},
  howpublished = {EasyChair Preprint 15914},
  year      = {EasyChair, 2025}}