Enriching Archived Smart Card Transaction Data for Transit Demand Modeling

Document Type

Journal Article

Publication Date


Subject Area

planning - route design, land use - planning, ridership - demand, mode - bus, mode - mass transit


Transit demand modeling, Transit, Spatiotemporal analysis, Smart cards, Route (Itinerary), Public transit, Planning, Origin and destination, O&D, Mass transit, Local transit, Linked trips, Itinerary, Integrated circuit cards, Dynamic models, Demand, Data enrichment, Contactless fare cards, Chip cards, Boarding and alighting, Arrival time (Bus transit), Archived data


Transaction data from public transit smart cards represent a continuous stream of detailed travel information for transit demand modeling. Although certain aspects of information are incomplete in unprocessed data, efforts are devoted to deriving a more comprehensive understanding of the system and its users from partial information through data enrichment processes, with a long-term goal of establishing a dynamic model of demand. On the basis of previous work, methods are proposed to estimate the arrival time of bus runs at the stop level by using temporal constraints and to identify linked trips by using spatial–temporal concepts. These enrichments lead to the reconstruction of individual itineraries, the analysis of transfer activity, and the synthesis of vehicle load profiles. The latter provide planners with a detailed spatial–temporal progression of each run, origin and destination stops for each individual transaction, and boarding and alighting activity at each stop. The study draws on more than 37,000 smart card boarding transactions of an average weekday from a midsize transit agency. Results suggest that linked trips represent slightly above 10% of the total number of transactions in the network and the smart card system overestimates the proportion of linked trips by nearly 40%. The outcome is promising and lays a foundation to further enrich the itineraries by associating the boarding and alighting stops with trip generators, deriving trip purposes, and performing multiday analysis.