Revealing behavioral impact on mobility prediction networks through causal interventions

arXiv:2311.11749v2 Announce Type: replace-cross
Abstract: 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. This study introduces 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 …

abstract arxiv behavior causal challenges cs.lg cs.si framework impact interpretability mobility networks neural networks physics.soc-ph prediction predictions study tasks through type