arXiv Machine Learning By Fred Shone, Tim Hillel

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach

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arXiv:2512. 04223v2 Announce Type: replace Abstract: Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging.

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arXiv Machine Learning
Sep 22

Modelling daily activity patterns from mobile phone location data via deep representation learning

The paper introduces the Activity Chain Encoder (ACE), a self‑supervised deep learning model that transforms passively collected mobile phone location data into daily activity representations. ACE integrates pre‑trained urban embeddings, visit timing, and duration, using a Transformer to capture the sequential structure of stays, and is trained via masked activity modelling and contrastive learning without explicit activity labels. The resulting user‑level profiles are clustered and interpreted with temporal‑functional patterns and Census demographics, revealing six distinct weekday activity‑pattern groups in London that differ in daily rhythms, urban contexts, and demographic characteristics.

By Xinglei Wang, Junyuan Liu, Guangsheng Dong, Zichao Zeng, Stephen Law, James Haworth, Tao Cheng