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

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

Read the original on arXiv Machine 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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