A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport - Minhyuck Lee; Inwoo Jeon; Chulmin Jun (Applied Sciences, 2022)
In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is added to the public transport network, passengers choose the fastest route to their destination. The proposed methodology is applied to actual lines (bus and subway lines) in Seoul, the capital of South Korea, and it is validated through the observed traffic volume of those lines recorded in the smart card data. The experiments are conducted using smart card data, with more than 100 million trips stored, extracted from about 1 million passengers who have check-in records in the catchment area of the new lines. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data.