Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

• The present study proposed a rigorous methodology to impute the sequence of activities elicited form smart-card data using a continuous hidden Markov model (CHMM).
• The proposed model requires neither labeled data for training nor subsequent measurements such as prompted-recall surveys.
• The present study showed the power of unsupervised machine-learning models.
• Self-clustered activities and transition probabilities between them were fully validated by observed data.

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.