Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.

翻译：准确的活动位置预测是许多移动应用的关键组成部分，尤其对于开发个性化、可持续的交通系统至关重要。尽管深度学习模型被广泛采用，但下一位置预测模型仍缺乏对移动相关时空上下文的全面讨论与整合。本文利用多头自注意力（MHSA）神经网络，从历史位置访问记录、访问时间与活动时长，以及周边土地利用功能中学习位置转移模式，从而推断个体的下一位置。具体而言，我们采用兴趣点数据和潜在狄利克雷分配方法，在不同空间尺度上表征位置的土地利用上下文，生成时空特征的嵌入向量，并通过MHSA网络学习预测下一位置。基于两个大规模全球导航卫星系统（GNSS）追踪数据集的实验表明，所提模型优于其他先进预测模型，并揭示了不同时空上下文对模型性能的贡献。此外，我们发现基于群体数据训练的模型相较于个体级模型，能以更少的参数实现更高的预测性能——这得益于对集体移动模式的学习。研究还表明，近期（最近一小时）及一周前的移动行为对当前预测影响最大，证明仅学习历史移动的子集即可获得准确的位置预测结果。我们认为所提模型对上下文感知的移动预测至关重要，所得见解有助于理解位置预测模型，并推动其在移动应用中的部署。