# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 5 Conclusions  
Since 2000, Taiwan has been implementing the AFC system, also called the smartcard system, in the transportation system. The widespread use of smartcards helps passengers greatly reduce their transaction time and helps companies collect a large amount of information. Although there is a smartcard system, serious traffic jams still occur. Therefore, a good passenger flow forecast could be used to reduce traffic congestion, increase passenger convenience, and assist enterprises with formulating route planning, resetting timetables, and constructing other policies. In addition, a good passenger flow forecast can help cities reduce excessive energy consumption and carbon emissions and improve urban ecosystems to achieve sustainable development.  
In order to achieve better prediction performance, we carried out the following steps:  
1. We proposed an integrated-weight time-series forecast model to forecast passenger flow. We used real smartcard data to verify that the proposed model has good predictive capabilities, rather than using simulated data to show the research results. The experiments showed that the proposed model performed better than the listed models for each route for different time series, as shown in Tables 6-8.
2. In terms of the verification data, we focused on the top three routes with the most passengers out of the 17 routes--Route 1, which showed the largest fluctuations; Route 7, which has the largest number of passengers; and Route 52, which has the least number of passengers of the top three routes--as shown in Figure 2.
3. In terms of attribute screening, this study used smartcard data and time attributes as well as 15 external weather attributes. In addition, as the number of passengers varies with time, this is a time-series forecasting problem; hence, seasonal trends had to be considered. Therefore, we added the number of lags to the forecast of passenger flow.
4. As shown in Tables 6-8, we found that the data for each route could be partitioned by time (weeks, weekdays, and holidays) to improve the forecast result. Based on the key attributes shown in Table 9 and the lag periods of the top three models shown in  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Route 1** & **Route 7** & **Route 52** \\ \hline Full attributes & 199.882 & 93.682 & 79.963 \\ Removal of meteorology attributes & 231.714 & 144.949 & 113.959 \\ Removal of first key attribute & 228.594 & 136.121 & 121.006 \\ Removal of second key attribute & 228.066 & 140.402 & 116.717 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of sensitivity analysis based on RMSE.  
Table 10, the number of lags affected forecast results. Furthermore, most of the top three models are in the LSTM family, which presents a better forecast.  
In terms of future work, we have two suggestions to further enhance this topic by making the results less conservative and improving the forecasting performance: (1) other attributes could be used in these forecast models, and (2) other methods (such as deep learning algorithms) could be applied to this topic.  
Conceptualization, C.-H.C. and M.-C.T.; methodology, C.-H.C.; software, Y.-C.C.; validation, C.-H.C. and M.-C.T.; formal analysis, M.-C.T.; investigation, Y.-C.C.; resources, C.-H.C.; data curation, M.-C.T.; writing--original draft preparation, Y.-C.C.; writing--review and editing, C.-H.C.; visualization, M.-C.T.; supervision, C.-H.C.; project administration, M.-C.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledge partial support from Ministry of Science and Technology, Taiwan (Grant No. MOST-2221-E-224-037-).  
The authors declare no conflict of interest.