文章编号： 1672-5328（2024）06-0126-02

刘宇航
(同济大学交通学院，上海201800)

摘要： 选取来自国际学术期刊的论文，以概述形式对城市交通理论方法、实证分析等学术研究成果进 行总结性介绍，旨在增强城市交通业界和学界对国际学术动向和研究热点的关注，促进学术交流。 《TRANSIT：使用网络信令数据进行大规模细粒度人类移动轨迹推断》一文基于网络信令数据设计了 人类移动轨迹扩展框架TRANSIT。依托密度聚类技术、移动行为模式以及高采样率信令数据，该框 架能够重建大规模、高精度的人类移动轨迹。基于真实GPS轨迹数据的验证表明，其在移动轨迹重建 方面优于现有先进方法。信令数据经TRANSIT框架处理后，支持出行分担率计算、通勤线路识别、 城市吸引力分析及城市流动性研究。

关键词： 移动轨迹扩展框架；网络信令数据；移动轨迹

中图分类号： U491

文献标识码：A

Academic Dynamics on Inferring Human Mobility Trajectories Using Network Signaling Data

LIU Yuhang
(College of Transportation Engineering, Tongji University, Shanghai 201800, China)

Abstract： A review of selected papers from international academic journals is presented to summarize research findings, theoretical approaches, and empirical analyses of urban transportation. The aim is to enhance the communication between industrial and academic fields of urban transportation, highlight international research focuses, and promote academic exchange. The paper TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data proposed a framework called TRANSIT for extending and reconstructing large- scale and high- precision human mobility trajectories based on network signal data by leveraging density clustering technology, mobile behavior patterns, and high-sampling-rate signaling data. Validated by real-world GPS trajectory datasets, the proposed method performs better than existing modeling frameworks. Signaling data, after being processed by the TRANSIT framework, supports the analysis of travel mode shares, commuting routes, urban attractiveness and mobility.

Keywords： TRANSIT; network signaling data; mobility trajectory