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The introduction of electronic payment systems for public transport in many cities all over the world enabled collection of detailed and comprehensive data records of public transport journeys.Processing and mining of these data opens new opportunities in transport modelling and travel behaviour research.In particular its potential for identification and analysis of activities, which cause regular travels, represents a highly interesting research question.In this paper, opportunities for detection of primary activities, as home and work activities, and their locations based on records of smart card fare payment system for public transport are investigated.Thereby the city-state of Singapore is used as a case-study.In order to gain information about country specific activity characteristics and allow accurate model calibration, data from household travel survey is used.In particular, two major activity detection models are considered: a simple rule-based model using only activity duration for detection of work activities and more involved discrete choice modelling approach using additional variables as activity start-time and land-use information, and distinguishing between home, work and other activities.In particular, the benefit resulting from including land-use information into the discrete choice model is evaluated and models with and without it are compared.The developed models are applied to the full record of public transport journeys in Singapore and outcomes are compared against each other.In conclusion, challenges associated with analysis of longer time periods consisting out of journey data for several days are discussed.
Chakirov et al. (Sun,) studied this question.