Big Data in Transport Geography: Estimating the quality of Smart Card Data

The emergence of new tracking technologies and Big Data has caused a transformation of the transport geography field in recent years. One new datatype, which is starting to play a significant role in public transport, is smart card data. Despite the growing focus on smart card data, there is a need for studies that explicitly compare the quality of this new type of data to traditional data sources.

With the current focus on Big Data in the transport field, public transport planners are increasingly looking towards smart card data to analyze and optimize flows of passengers. However, in many cases it is not all public transport passengers in a city, region or country with a smart card system that uses the system, and in such cases, it is important to know what biases smart card data has in relation to giving a complete view upon passenger flows.

This paper therefore analyses the quality and biases of smart card data in Denmark, where public transport passengers may use a smart card, may pay with cash for individual trips or may hold a season ticket for a certain route. By analyzing smart card data collected in Denmark in relation to data on sales of cash tickets, sales of season tickets, manual annual passenger counts and continuous automated door counts in busses and trains, and travel survey data, this article estimates quality and biases of using smart card data to illuminate the complete flow of public transport passengers in Denmark.