论文标题
基于知识图的低可预测性个人的潜在目的地发现
Potential destination discovery for low predictability individuals based on knowledge graph
论文作者
论文摘要
旅行者可能会前往他们从未去过的地点，我们称之为他们的潜在目的地。尤其是在非常有限的观察结果下，旅行者倾向于显示随机运动模式，并且通常具有大量潜在目的地，这使得他们难以处理移动性预测（例如，目的地预测）。在本文中，我们开发了一个新的基于知识图的框架（PDPFKG），以通过考虑旅行之间的关联关系来潜在的目的地发现低可预测性旅行者。我们首先构建了一个旅行知识图（TKG），以通过实体（例如旅行者，目的地和时间信息）及其关系对旅行方案进行建模，我们在其中介绍了私人关系的概念以降低复杂性。然后，实现了修改的知识图嵌入算法以优化整体图表。基于Trip知识图嵌入模型（TKGEM），可以通过计算三元组的距离来获得个人未来未观察到的目的地的可能排名。经验。 PDPFKG使用来自中国Xuancheng City配备基于视频的车辆检测系统的138个交叉口的匿名车辆数据集进行了测试。结果表明，（i）所提出的方法显着超过了基线方法，并且（ii）结果在选择潜在目的地时表现出与旅行者行为的强烈一致性。最后，我们对方法论的创新点进行了全面的讨论。
Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with traveler behavior in choosing potential destinations. Finally, we provide a comprehensive discussion of the innovative points of the methodology.
