# A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport
###### Abstract  
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.  
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methodology was developed as short-term transport planning software to perform visualization and what-if analysis, and it was applied to a case study in The Hague, the Netherlands [21]. [PERSON] et al., (2015) proposed short-term bus passenger demand prediction using a time-series model based on smart card data [22]. [PERSON] and [PERSON] (2017) showed how short-term demand can be accurately modeled with a neural network [23]. [PERSON] et al., (2021) proposed a data-based approach that exploits AFC to predict the demand for trains when special events, such as sport games and concerts, occur [24].  
However, although various studies dealing with short-term public transport demand forecasting have been conducted [25, 26], empirical studies that predict the number of passengers on actual new lines and that compare this number with observations are insufficient. Most of the related models were verified based on scenarios in which network changes were not considered. Updating a public transport network, such as adding new lines, can cause a large dispersion of overall demand. Therefore, it is very important to model the short-term ridership forecasting of new lines in order to obtain validation scenarios that reflect similar conditions. In addition, a model considering the trade-off relationship or relative weights between variables with different units, such as time, price, and comfort, or based on an SP survey has limitations in describing real-world situations.  
In this study, a methodology that predicts ridership on new public transport lines using smart card data is proposed. The proposed methodology assumes that, when a new line is added to a public transport network, passengers choose the fastest route to their destination [27]. In the updated network, passengers' new routes are computed using the optimal path finding algorithm. The path finding algorithm receives the card usage records of passengers before the network is updated and assigns individual passengers to the optimal path on the updated network. That is, the proposed methodology predicts the number of passengers on a new line based on the deterministic approach. Above all, this study is different from related studies in that it predicts the ridership for actual new lines in Seoul city and compares them with the observations from the smart card data.