Multi-day activity pattern recognition based on semantic embeddings of activity chains

Understanding residents' multi-day activity patterns is crucial for urban transportation planning. However, traditional travel surveys have limitations in observing the recurring regularity of residents' activities over multiple days, resulting in limited insights into long-term activity patterns. This study aims to provide a general and practical analytic framework for multi-day activity pattern recognition by fusing mobile phone data, travel survey data, and built environment data. First, a rule-based model is proposed to infer home/work activities, while a machine learning model is proposed to infer non-home/work activities of mobile phone users. Then, their multi-day activity chains are reconstructed with sequences of activity units which are labeled by inferred activity types. To measure the differences and similarities among these activity chains, this study develops a word embedding model to represent activity units and a sentence embedding model to represent activity chains in numerical vector space. Finally, regular multi-day activity patterns of mobile phone users can be recognized based on the clustering of vectorized activity chains. A case study in Shanghai is conducted to validate the proposed analytic framework for understanding the multi-day activity patterns of residents. The results indicate the effective recognition of 39 representative patterns of 7-day activity chains in Shanghai, reflecting the various lifestyles of residents. In summary, the main contribution of this study is to transform the problem of multi-day activity pattern recognition into similarity measurement of sentence vectors of activity chains by employing techniques from the field of natural language processing.