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In transport modeling and prediction, trip purposes play an important role. The most particular case is activity-based modeling, whereby mobility choices (e.g. mode, path, and departure time) are made in order to carry out specific activities. A current challenge, however, lies on getting appropriate data that relates observed trips with their purpose. Recently, a Smartphone-based travel survey (the Future Mobility Survey, FMS) was conducted in Singapore that collected location data from 793 participants. Each FMS user was required to collect data for at least 14 days and validate at least 5 of them. This dataset presents diverse opportunities in terms of developing machine learning models for the future versions of FMS, where the validation process is intelligent and easy to use (e.g. having pre-filled activities associated to the user traces). This paper proposes a learning model that, given a stop location, identifies the most likely activity associated to it. Our data often contains errors or noise due to limited functionality of physical sensors in a dense area, and human mistakes in the validation process. To alleviate this effect, we generate heterogeneous features by different spatial quantization techniques and apply ensemble learning for a good generalization performance.
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Stored only when both papers are in the corpus — 25 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.