Exploring Non-Routine Trips Through Smartcard Transaction Analysis
Keywords: Public transport, Smartcard, Spatio-temporal clustering, Demand-Responsive Transport
Abstract. Public transportation (PT) studies often overlook non-routine trips, focusing on commuting trips. However, recent research reveals that occasional trips comprise a significant portion of public transportation trips. Furthermore, traveler preferences for non-routine trips essentially differ from their preferences for regular commuting. We investigate non-routine trips based on a database of 63 million records of PT boardings made in Israel during June 2019. The behavioral patterns of PT users are revealed by clustering their boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority and constitute less than 15% of Israeli riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of a day, and land use composition around the trip origin.