Personalized predictive public transport crowding information with automated data sources

The paper proposes a methodology for providing personalized, predictive in-vehicle crowding information to public transport travellers via mobile applications or at-stop displays. Three crowding metrics are considered: (1) the probability of getting a seat on boarding, (2) the expected travel time standing, and (3) the excess perceived travel time compared to uncrowded conditions. The methodology combines prediction models of passenger loads and alighting counts based on lasso regularized regression and multivariate PLS regression, a probabilistic seat allocation model and a bias correction step in order to predict the crowding metrics. Depending on data availability, the prediction method can use a combination of historical passenger counts, real-time vehicle locations and real-time passenger counts. We evaluate the prediction methodology in a real-world case study for a bus line in Stockholm, Sweden. The results indicate that personalized, predictive crowding information that is robust to varying data availability can be provided sufficiently early to be useful to travellers. The methodology is of value for agencies and operators in order to increase the attractiveness and capacity utilization of public transport.