用图嵌入技术可视化城市人流动态，提升分析效率与精度。
Graph Network Modeling Techniques for Visualizing Human Mobility Patterns
- 将人流数据转化为图结构，并嵌入连续空间以简化匹配与建模。
- 实验显示图匹配后误差平均降低约40%。
- 适合城市规划、交通研究等需分析人流模式的场景。
城市尺度的人类移动分析需要模型来捕捉复杂的人流特征，这些特征受周边兴趣点可达性、地区社会经济因素及本地交通选择的影响。本文将人类移动及其流动关系表示为图结构。尽管基于图的方法在移动性分析中仍处于初期阶段，但其面临多重挑战：高质量高时空分辨率流动数据不足、大规模移动数据转为网络结构时计算资源有限，以及图模型固有的可扩展性问题。本研究提出一种将图嵌入连续空间的方法，缓解了快速图匹配、图时间序列建模及移动动态可视化等问题。通过实验表明，从出租车轨迹采集的移动数据可成功转换为网络结构，并揭示流动模式变化，用于下游任务时，相比未匹配图，平均误差降低约40%。
原文摘要 · Abstract (English)
Human mobility analysis at urban-scale requires models to represent the complex nature of human movements, which in turn are affected by accessibility to nearby points of interest, underlying socioeconomic factors of a place, and local transport choices for people living in a geographic region. In this work, we represent human mobility and the associated flow of movements as a grapyh. Graph-based approaches for mobility analysis are still in their early stages of adoption and are actively being researched. The challenges of graph-based mobility analysis are multifaceted - the lack of sufficiently high-quality data to represent flows at high spatial and teporal resolution whereas, limited computational resources to translate large voluments of mobility data into a network structure, and scaling issues inherent in graph models etc. The current study develops a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics. Through experiments, we demonstrate how mobility data collected from taxicab trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。