What big data do not tell us

Abstract

With the introduction of the chipcardfor public transport (OV-chipkaart) as a digital ticketing system in the Dutch public transport (PT), a new data source for PT was created. The data registered by this smartcard contain every transaction (check-ins and check-outs) with according time and location. In 2013, the OV-chipkaart system operator Trans Link Systems registered 1.9 billion transactions, which were allstored in the central back office(Trans Link Systems, 2014). This Big Data1source contains interesting information for strategic transport models using a pivot point method(Pelletier, Trépanier, & Morency, 2011). This method uses origin-destination (OD) matrices to describe the current situation, on which growth factors are applied to determine forecast matrices. The OV-chipkaart replaced the National Ticketing System (NVB),with the according WROOV studies. The NVB contained themain ticket types and allowed travellers to travel with all PT operators in the country. The WROOV studies consisted of surveys that were used to allocate the revenues of the NVB to the operators and governments.The WROOV surveys were the primary data source of travel behaviour regarding bus and light rail until 2009, the year the OV-chipkaart was introduced.The data availability on travel behaviour was one of the reasons for PT operators to introduce the OV-chipkaart(Bergmans, Bottenberg, & Hilferink, 2012).In contradiction to WROOV data, however, the OV-chipkaart data havehardlybeen used in strategic planning. This is caused byseveral issues: the availability of OV-chipkaart data is deficient and, moreover, the OV-chipkaart data do not contain all required information(Bagchi & White, 2005).