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This study presents a flexible white box method for mining daily activity chains from large-scale mobile phone location data, addressing challenges such as the lack of semantic information and the complexity of existing machine learning models. The proposed method integrates spatial and temporal features to infer activity purposes, demonstrating reasonable outcomes that align with travel survey patterns. Additionally, the research explores the impact of data conditions on model performance, enhancing the pr

Author

Sybert van

Language

EN