Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the "when$\rightarrow$what$\rightarrow$where", a.k.a. "time$\rightarrow$activity$\rightarrow$location" decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers' destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show a 33.4% performance improvement of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.
翻译:模拟人类流动有助于理解人们如何获得资源,如何在城市中相互实际接触,从而有助于城市规划、流行病控制和基于地点的广告等各种应用。下一个地点预测是个人流动模型中的一项决定性任务,通常被视为序列模型,通过Markov或RNN的方法加以解决。然而,现有的模型很少注意个人旅行决定的逻辑和人口集体行为的可复制性。为此,我们提议为下一个地点预测提供一个业余和受空间限制的长时短期学习者(CSLSL),利用基于多任务学习的因果结构,明确模拟“当美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元”的序列模型。但“时间/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元/美元”的决定逻辑。我们提出一个受空间限制的损失功能,作为辅助任务,以确保旅行目的地的预测和实际空间分布的一致性。此外,CSLSLSL采用名为长和短期的因果结构的模块,明确学习CLSL4 Broal-lialalalalalalalalalalalalssstrubalsxxxx。