使用WiFi信号检测移动模式

2018 IEEE International Smart Cities Conference (ISC2) Pub Date : 2018-09-01 DOI:10.1109/ISC2.2018.8656903

Arash Kalatian, B. Farooq

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引用次数: 5

摘要

我们利用智能手机的Wi-Fi通信来预测他们的移动模式，即步行、骑自行车和开车。Wi-Fi传感器被部署在多伦多市中心街道上的四个战略位置，形成一个闭环。深度神经网络(多层感知器)与三种基于决策树的分类器(决策树，袋装决策树和随机森林)的发展。结果表明，多层感知器的预测精度最高，对迁移模式的预测准确率为86.52%。

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Mobility Mode Detection Using WiFi Signals

We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree-based classifiers (Decision Tree, Bagged Decision Tree and Random Forest) are developed. Results show that the best prediction accuracy is achieved by Multilayer Perceptron, with 86.52% correct predictions of mobility modes.

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2018 IEEE International Smart Cities Conference (ISC2)

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