Short-Term Prediction of Ridership on Public Transport with Smart Card Data

Abstract

Public transport operators are collecting massive amounts of data from smart card systems. In the Netherlands, every passenger checks in and checks out; this system creates detailed records of demand patterns. In buses and trams, users check in and check out in the vehicle; this factor provides good information on route choice. Options for analyzing smart card data and performing what-if analyses with transport planning software were explored. On the basis of big data, this new generation of transport demand models added to the existing range of transport demand models and approaches. The goal was to provide public transport operators with a simple (easy-to-build) model to perform what-if analyses. The data were converted to passengers per line and an origin–destination matrix between stops. This matrix was assigned to the network to repro-duce the measured passenger flows, and then what-if analysis became possible. With fixed demand, line changes could be investigated. With the introduction of an elastic demand model, changes in the level of service realistically affected passenger numbers. This method was applied to a case study in The Hague, Netherlands. Smart card data were imported into a transport model and connected with the network. The tool proved to be valuable to operators, who gained insights into the effects of small changes.