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针对传统基于真实MAC地址的人流OD监测方法面临的隐私保护与数据失效问题，提出一种基于Wi-Fi探测请求信息元素的匿名人流OD识别方法，通过分析探测请求中HT Capabilities和VHT Capabilities等字段的稳定特征，设计改进的DBSCAN聚类算法，实现随机MAC设备的精准区分。针对跨区域轨迹匹配问题，提出基于信息元素分组筛选与距离矩阵计算的OD关联策略。结果表明，在开放设施中，设备聚类精度达到97.87%,OD匹配精度达到81.8%,验证了方法的有效性。与现有技术相比，该方法在保护用户隐私的同时，兼顾了高精度与低计算复杂度，为公共安全、交通规划及应急管理提供了新思路。
Abstract：To address the privacy protection and data invalidation issues faced by traditional Origin-Destination(OD) crowd flow monitoring methods based on real MAC addresses, this paper proposes an anonymous OD crowd flow identification method based on Wi-Fi probe request information elements. By analyzing stable features in fields such as HT Capabilities and VHT Capabilities within probe requests, we design an enhanced DBSCAN clustering algorithm to accurately distinguish randomized MAC devices. To address cross-region trajectory matching, we introduce an OD association strategy leveraging information element group filtering and distance matrix computation. Experimental results in a public facility environment demonstrate a device clustering accuracy of 97.87% and an OD matching accuracy of 81.8%, validating the method's effectiveness. Compared to existing techniques, our approach achieves high precision and low computational complexity while preserving user privacy, offering novel insights for public safety, transportation planning, and emergency management.
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基本信息:
DOI：10.13291/j.cnki.djdxac.2026.03.004
中图分类号:TN92;U491.12
引用信息:
[1]朱成娟,赵星吉,陈爱同.随机MAC机制下基于Wi-Fi探测请求的人流OD识别方法[J].大连交通大学学报,2026,47(03):36-44.DOI:10.13291/j.cnki.djdxac.2026.03.004.
基金信息:
国家自然科学基金面上项目(72171033); 辽宁省教育厅科学研究计划项目(JDL2019037)
2025-03-12
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