Public transportation (PT) studies often neglect non-routine trips focusing predominantly on commuting. However, recent research revealed that occasional trips make up a substantial portion of public transport journeys, and traveler preferences for non-routine trips diverge from their preferences for regular commuting. We study non-routine trips based on a database of 63 million smartcard (SC) records of PT boardings made in Israel during June 2019. The characteristics of these trips are revealed by clustering PT users’ 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 in Israel and constitute less than 15% of the riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified as 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 day, and land use composition around the location of trip origin. We discuss the potential applications of our approach in the landscape of diverse flexible PT.
- Demand-responsive transport
- Public transport
- Transportation network company
- Trip clustering