基于轨迹数据的用户身份匹配方法研究综述

A review of user identity matching methods based on trajectory data

随着城市交通精细化治理要求的不断提高,传统的交通调查难以适应快速变化的居民出行模式。新技术和新商业模式的出现与发展使得出行者轨迹以大规模、高频率和低成本的方式被记录。在不侵犯用户隐私的前提下,融合多源匿名数据构建出行者完整的多模式出行链,可有效补充现有交通调查方法。从轨迹相似性和数据特征出发,整理了不同类型的用户身份匹配方法,并讨论了未来的研究的方向。

With the continuous improvement of urban traffic fine-grained governance requirements,traditional traffic surveys struggle to adapt to residents'rapidly changing travel patterns.The emergence and development of new technologies and business models have enabled traveler trajectories to be recorded in a large-scale,highfrequency and low-cost manner.Fusing multiple sources of anonymized data to construct a complete multi-modal travel chain of travelers without violating users'privacy,which can effectively complement existing traffic survey methods.This paper explores various user identitymatching methods based on trajectory similarity and data characteristics,and discusses the directions for future research.