Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore


Yi Zhu

Document Type

Journal Article

Publication Date


Subject Area

place - urban, place - asia, planning - surveys, planning - travel demand management, technology - passenger information, technology - intelligent transport systems, ridership - modelling


Activity inference, Transit smart card data, Spatio-temporal correspondence, Urban sensing, Activity landscape


Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders’ activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new ‘‘big data’’ such as transit smart card data and “small data” such as traditional travel surveys to create better insights of urban travel demand and activity dynamics.


Permission to publish the abstract has been given by SpringerLink, copyright remains with them.