# A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport
## 4 Prediction  
In this study, the number of passengers was predicted for two lines in Seoul city using the proposed methodology. These two lines started operating in August 2018 and October 2017. For this reason, the public transport network and smart card data for September 2017, a period for which there are no usage records for both lines, were used for the experiment. The public transport network includes approximately 11,000 nodes, 620 lines, and schedules for 80,000 vehicles. The smart card data include about 100 million trips recorded over 10 days. Figures 5 and 6 present maps of the route of bus No. 1167 and Ui-Sinseol subway, respectively, corresponding to the new lines. In the maps, the red point is the node through which the new line passes, and the green point is the node within the catchment area. Passengers on both lines are selected from passengers with a check-in history at nodes within the catchment area. About 1 million candidates were extracted from the smart card data applied in the experiment. Figure 4: Example of optimal route allocation after adding a new line: (**a**) before; (**b**) after. Figure 5: Map of route of bus No. 1167. The proposed methodology assumes that passengers will choose the route that reaches their destination the fastest, and it calculates the optimal route using the RAPTOR algorithm. To support this logic, the algorithm must be able to predict the route that the passengers actually select. Therefore, an experiment comparing the routes used by the candidates recorded in the smart card data for September 2017 with the routes determined by the algorithm was performed first. Figure 7 shows the matching rate relative to the number of transfers on the route. For 483,193 candidates who did not transfer, the algorithm produced the same routes for 404,380 passengers, which corresponded to approximately 84%. For candidates who made a single transfer, the route calculated by the algorithm matched the actual route by about 88%. Although the matching rate decreased with an increase in the number of transfers, the overall matching rate reached approximately 70%. The ridership numbers on bus No. 1167 and the Ui-Sineol subway line predicted using September 2017 data were validated using smart card data from April 2019. Figure 8 shows the daily average number of passengers per node for the two lines. Figure 8 shows the observed demand and predicted demand for bus No. 1167, and (b) shows the results for the Ui-Sineol subway line. The prediction of ridership for both lines was highly accurate. Figure 6: Map of Ui-Sineol subway line. Figure 7: Matching rate by number of transfers on the route. The observed daily average number of passengers for bus No. 1167 was 1431, and the predicted value using the proposed methodology was 1439. The number of passengers for each node was also similar to the observed value. The largest difference appeared at node No. 8502298, which is located immediately next to a subway station. The observed daily average number of passengers for Ui-Sinseol subway line was 38,036, and the predicted value using the proposed methodology was 37,559. The number of passengers per subway station was underestimated by about 480 compared to the observed value. The largest difference appeared at node No. 4713, which is a complex transfer station with several subway lines. Figure 9 shows the average number of passengers per hour for each line. The prediction results for ridership per hour on the two lines showed similar patterns to those of the observed values. In particular, the expected values of the subway line showed a slight difference from the observed values, with an average error of 10%. The commuting  
Figure 8: Daily average number of passengers per node: (**a**) bus No. 1167; (**b**) Ui-Sinseol subway line. patterns in which the number of passengers increases during peak hours in the morning and evening were also found in both the predicted and observed values.