Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

• URL: http://arxiv.org/abs/2111.03465v2
• Date: Wed, 10 Nov 2021 08:46:48 GMT
• Title: Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
• Authors: Huandong Wang, Qiaohong Yu, Yu Liu, Depeng Jin, Yong Li
• Abstract summary: We focus on modeling users' mobility patterns based on knowledge graph techniques. We propose a new type of knowledge graph, i.e., massively-temporal urban knowledge graph (STKG) We show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.
• Score: 20.682466719664838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge'' extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

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