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
通过提出计算模型和学习框架，我们提出了一个Causal and Spatial-constrained Long and Short-term Learner（CSLSL）模型，该模型可以隐式地建模个体的出行决策，并在下一个地点预测中显示出传递不变性及其群体一致性。通过空间约束和多任务学习，该模型引入了时间、活动和位置之间的因果关系来实现准确度的提高。