用WiFi数据预测校园人流，不依赖用户行为假设。
Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
- 构建图卷积与LSTM结合模型，从WiFi日志学人流模式。
- 相比MLP和线性回归，预测准确率显著提升。
- 适合智慧校园、城市交通规划人员参考。
我们提出一种基于图神经网络的集成架构，用于在动态时间分辨率下预测校园建筑内人员密度及建筑间移动情况。该模型通过融合Wi-Fi日志与建筑使用时序信息，直接从数据中学习交通流模式，无需假设个体行为偏好，同时保障隐私。将问题建模为图结构，节点代表建筑，边代表通行路径，采用新型图卷积加LSTM神经网络（GCLSTM）捕捉复杂时空模式。我们描述了建模方法、模型估计过程、可解释性分析，并评估了所提模型性能。基于多伦多都会大学真实采集的Wi-Fi日志进行实验，结果表明，集成式GCLSTM模型显著优于传统行人流量估计器，如多层感知机（MLP）和线性回归。
原文摘要 · Abstract (English)
We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure represented by a set of nodes (representing buildings), connected through a route of edges or links using a novel Graph Convolution plus LSTM Neural Network (GCLSTM) which has shown remarkable success in modelling complex patterns. We describe the formulation, model estimation, interpretability and examine the relative performance of our proposed model. We also present an illustrative architecture of the models and apply on real-world WiFi logs collected at the Toronto Metropolitan University campus. The results of the experiments show that the integrated GCLSTM models significantly outperform traditional pedestrian flow estimators like the Multi Layer Perceptron (MLP) and Linear Regression.
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