摘要
【目的】人类出行流量生成是指在缺乏历史出行流量数据的情况下,基于出行起终点特征估算两点之间出行流量的技术。该技术在城市规划、交通管理和商业布局等领域具有重要应用价值。然而,经典活动模型难以捕捉影响出行的复杂因素,而基于深度学习的模型虽精度较高,但可解释性不足,限制了实际应用。【方法】本文提出一种基于解耦表征学习的人类出行流量生成建模方法,通过构建位置、非位置及残差因素3类编码器,采用互信息最小化策略将影响出行流量生成的因素解耦为3类独立潜码,结合注意力机制构建出行流量综合表征以实现出行流量的高精度生成。【结果】在2020年纽约州与宾夕法尼亚州人口流动数据集上,将本方法与GM、RM、RF、GNN、DG和SI-GCN 3组不同类别的6种流量生成模型进行对比。实验结果显示,本方法在“通勤者公共部分”指标上达到0.773和0.727,在流量生成精度和跨区域泛化能力方面均优于其他对比方法。通过消融实验与无监督解耦指标验证了解耦表征模块可以有效分离影响出行流量生成的三类潜在因素,并通过SHAP分析和注意力权重量化三类流量生成因素对流量生成的贡献。【结论】相较于经典活动模型和深度学习模型,本方法在提高流量生成精度的同时,进一步提升模型的可解释性,为优化出行流量生成模型提供了新的视角。
[Objective]Human mobility flow generation refers to the technology of estimating mobility flow between two locations based on the characteristics of the origin and destination,in the absence of historical mobility flow data.This technology has significant application value in fields such as urban planning,traffic management,and commercial layout.However,classical activity models struggle to capture the complex factors influencing mobility behavior,while deep learning-based models offer higher accuracy but lack interpretability,limiting their practical utility.[Methods]This paper proposes a human mobility flow generation method based on disentangled representation learning.By constructing separate encoders for location-related,non-location-related,and residual factors,and using a mutual information minimization strategy,the model decouples the influencing factors into three independent latent codes.A comprehensive representation of travel flow is then formed by integrating an attention mechanism,enabling high-precision flow generation.[Results]Comparative and decoupling analysis experiments using 2020 population mobility data from New York and Pennsylvania demonstrate that,compared with existing methods,the proposed approach improves the"commuter public part"indicator by 4.47%and 3.71%,respectively.It also outperforms baseline models in terms of flow generation accuracy and cross-regional generalization ability.Ablation studies and verification using unsupervised disengtangled indicators show that the disentangled representation module effectively separates the three latent factors influencing mobility flow generation.The relative contributions of each factor are quantified using the SHAP method and attention weights.[Conclusions]Compared with classical activity models and standard deep learning models,the proposed method not only improves the accuracy of mobility flow generation but also enhances model interpretability,offering a new perspective for optimizing mobility flow generation models.
作者
朱雨昂
张彤
王志鹏
ZHU Yuang;ZHANG Tong;WANG Zhipeng(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China)
出处
《地球信息科学学报》
北大核心
2026年第1期138-153,共16页
Journal of Geo-information Science
基金
国家自然科学基金项目(42371470)。
关键词
人类活动建模
流量生成
深度学习
解耦表征学习
编码器
位置编码
互信息
注意力机制
modeling of human activities
mobility flow generation
deep learning
disentangled representation learning
encoder
location code
mutual information
attention mechanism
