使用网络信令数据进行人类移动轨迹推断研究动态
Academic Dynamics on Inferring Human Mobility Trajectories Using Network Signaling Data
刘宇航1
作者信息
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.
Abstract
A review of selected papers from international academic journals is presented to summarize re-search findings,theoretical approaches,and empirical analyses of urban transportation.The aim is to en-hance the communication between industrial and academic fields of urban transportation,highlight interna-tional research focuses,and promote academic exchange.The paper TRANSIT:Fine-Grained Human Mo-bility 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.
关键词
移动轨迹扩展框架/网络信令数据/移动轨迹Key words
TRANSIT/network signaling data/mobility trajectory引用本文复制引用
出版年
2024
NETL
NSTL
万方数据