The superstation representation of metro networks for overcoming data availability issues of station-to-station origin destination pairs – An application on the London underground

Information on metro passengers’ route choices is important to public transport operators. Recently, smart cards have been introduced in many metropolises, which, however, do not reveal explicitly the actual routes of the cardholders. Research literature shows that a finite mixture model (FMM) may provide an efficient approach to inferring the route choices; still, it can be rendered ineffective when the data sample size is small. To overcome this issue, we introduce the concept of ‘superstations’, which is a group of stations from/to which passengers have similar route choice patterns. From that, a larger data sample can be available for a superstation-to-superstation origin destination pair, so as to regain a functional FMM and enhance its fitness for inferring the passengers’ route choices. We test the proposed methodology on the London Underground. Results show that the FMM applied on the superstation representation presents a better performance than using a simple station-to-station representation.