Revealing behavioral impact on mobility prediction networks through causal interventions
arxiv(2023)
摘要
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 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, thereby facilitating the simulation of diverse yet
realistic 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.
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