Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions. In this study, we introduce a causal intervention framework to assess the impact of mobility-related factors on neural networks designed for next location prediction -- a task focusing on predicting the immediate next location of an individual. To achieve this, we employ individual mobility models to generate synthetic location visit sequences and control behavior dynamics by intervening in their data generation process. We evaluate the interventional location sequences using mobility metrics and input them into well-trained networks to analyze performance variations. The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thus facilitating the simulation of diverse spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. The gained insights hold significant value for the real-world application of mobility prediction networks, and the framework is expected to promote the use of causal inference for enhancing the interpretability and robustness of neural networks in mobility applications.

翻译：深度神经网络在移动性预测任务中日益普及，但其复杂的内部运作机制给可解释性带来挑战，特别是在理解移动性行为的各个方面如何影响预测结果方面。本研究引入了一个因果干预框架，用于评估与移动性相关的因素对面向下一位置预测（即预测个体即将到达的下一个位置的任务）的神经网络的影响。为此，我们利用个体移动模型合成位置访问序列，并通过干预其数据生成过程来控制行为动态。我们使用移动性指标评估干预后的位置序列，并将其输入训练好的网络以分析性能变化。结果表明，该方法能有效生成具有不同移动性行为的位置序列，从而模拟多样化的时空变化。这些变化导致下一位置预测网络的性能波动，揭示了关键移动性行为因素的影响，包括位置转换的序列模式、探索新位置的倾向性，以及群体和个体层面的位置选择偏好。所得见解对移动性预测网络的实际应用具有重要价值，且该框架有望推动因果推断在提升移动性应用中神经网络的可解释性和鲁棒性方面的应用。