电子学报2025，Vol.53Issue(10)：3551-3565,15.DOI:10.12263/DZXB.20250307
融合多源城市环境信息的知识图谱驱动轨迹生成模型
Urban Trajectory Generation via Knowledge Graph-Enhanced Multi-Source Context Fusion
摘要
Abstract
Mobility trajectory data of individuals,vehicles,and other objects in urban environments contains rich information about residents'activities,which is highly valuable for urban planning,traffic management,and epidemic spread analysis.However,privacy protection and commercial confidentiality significantly restrict the sharing and utilization of trajectory data.Generating synthetic trajectories that preserve the characteristics of real trajectories to replace real ones for release and application has become a preferred solution to overcome these limitations.Recently,deep learning-based trajectory generation research has attracted considerable attention from both academia and industry,with various trajectory models based on generative adversarial networks,diffusion models,and others being successively proposed.Nevertheless,existing trajectory generation models suffer from two major limitations:first,they fail to effectively capture global spatial dependencies in human mobility patterns;second,they inadequately model the influence of urban environments on trajectory generation,leading to deviations between generated trajectories and real-world scenarios.To address this,this paper proposes a knowledge graph-driven trajectory generation model integrating multi-source urban environmental information,named urban trajectory generation via knowledge graph-enhanced multi-source context fusion(KG-TrajGen).The model integrates key multi-source urban environmental data,including road network topology,points of interest(POI),and functional zone classifications,to construct a foundational road knowledge graph(RKG)and an environment-semantics-enhanced road knowledge graph(E-RKG).A relational graph convolutional network is employed to learn basic road segment embeddings from the RKG,simultaneously capturing both local and global spatial dependencies among roads.Additionally,a structure-aware knowledge graph embedding method is used to extract urban environmental knowledge from the E-RKG,endowing the model with environmental awareness and enriching the road segment embedding features.Subsequently,a Transformer decoder model learns human activity pattern features from historical trajectory data to obtain trajectory history-enhanced road segment embeddings.Finally,by effectively fusing the knowledge graph-enhanced and historical trajectory-enhanced road segment embeddings,the model generates environment-aware,fine-grained trajectories in an autoregressive manner.Experiments on two open source real world trajectory datasets demonstrate that KG-TrajGen significantly outperforms base-line methods in terms of statistical feature error,frequent pattern feature error,and trajectory error metrics.Moreover,the generated trajectories perform better than those from baseline methods in downstream trajectory analysis tasks such as traffic flow prediction,fully validating the effectiveness of the KG-TrajGen model.The code for KG-TrajGen is available at https://github.com/trajgen/KG-TrajGen.关键词
知识图谱/路网/轨迹生成/隐私保护/城市环境/TransformerKey words
knowledge graph/road network/trajectory generation/privacy protection/urban environment/Transformer分类
信息技术与安全科学引用本文复制引用
李康,于娟,韩建民,邱晟,杨琼..融合多源城市环境信息的知识图谱驱动轨迹生成模型[J].电子学报,2025,53(10):3551-3565,15.基金项目
教育部人文社会科学研究项目(No.22YJCZH215) （No.22YJCZH215）
浙江省高校重大人文社科攻关计划项目(No.2023QN150) （No.2023QN150）
国家自然科学基金(No.61702148,No.61672648) Humanities and Social Sciences Project of the Ministry of Education of China(No.22YJCZH215) （No.61702148,No.61672648）
Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions(No.2023QN150) （No.2023QN150）
National Natural Science Foundation of China(No.61702148,No.61672648) （No.61702148,No.61672648）
