Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency

doi:10.1109/TMC.2024.3501280

	Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency
	Wang, Chenxing 1; Zhao, Fang 1; Luo, Haiyong 2; Fang, Yuchen 1; Zhang, Haichao 1; Xiong, Haoyu 1
	2025-04-01
发表期刊	IEEE TRANSACTIONS ON MOBILE COMPUTING
ISSN	1536-1233
卷号	24 期号:4 页码:2832-2846
摘要	We focus on the transportation-aware trajectory recovery problem, which is distinct from the conventional vehicle-based trajectory recovery, facing three major challenges: heterogeneity, personalization and efficiency. For the heterogeneity, the velocity of the mobile object is intrinsically correlated with the specific transportation mode, containing inherent heterogeneity. For the personalization, the trajectory data is complicated by substantial variations in users, which are different in personalized behaviors. For the efficiency, previous works mostly employ sequence-to-sequence framework which limits their efficiency due to the auto-regressive inference pattern. To address these challenges, we design a novel efficient and effective multi-modal deep model, coined as PTrajRec, for transportation-aware trajectory recovery. Specifically, we initially embed location, behavior, and transportation mode modalities in distinct channels, which not only reflect spatio-temporal information encapsulated in location sequences but also introduce the heterogeneity and personalization characteristics associated with mode and behavior sequences. For further modeling these modalities, we employ the auto-correlation mechanism to learn periodic dependencies on the temporal dimension and the graph attention mechanism to learn road network dependencies on the spatial dimension. At last, we propose a dual-view constraint mechanism to assist the efficient trajectory recovery framework and design three auxiliary tasks to address the inherent heterogeneity and efficiency design. Extensive experimental results on two real-world datasets demonstrate the superiority of our proposed method compared to state-of-the-art baselines with reduced computation cost and excellent performance.
关键词	Trajectory Roads Transportation Global Positioning System Feature extraction Mobile computing Data mining Uncertainty Public transportation Social networking (online) Location-based social networks spatio-temporal data mining transportation mode-aware trajectory recovery
DOI	10.1109/TMC.2024.3501280
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62261042] ; Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation ; Fengtai Rail Transit Frontier Research Joint Fund[L221003] ; Beijing Natural Science Foundation[4232035] ; Beijing Natural Science Foundation[4222034] ; China Postdoctoral Science Foundation[2024M750200] ; BUPT Excellent Ph.D. Students Foundation[CX2022132]
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:001439561800043
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40675
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Fang; Luo, Haiyong
作者单位	1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Chenxing,Zhao, Fang,Luo, Haiyong,et al. Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2025,24(4):2832-2846.
APA	Wang, Chenxing,Zhao, Fang,Luo, Haiyong,Fang, Yuchen,Zhang, Haichao,&Xiong, Haoyu.(2025).Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency.IEEE TRANSACTIONS ON MOBILE COMPUTING,24(4),2832-2846.
MLA	Wang, Chenxing,et al."Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency".IEEE TRANSACTIONS ON MOBILE COMPUTING 24.4(2025):2832-2846.