# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 3 Proposed Model
\begin{table}
\begin{tabular}{l l l} \hline
**Type** & **Attribute** & **Description** \\ \hline \multirow{7}{*}{Smartcard} & Month & “1” denotes Jan, “2” means Feb, \(\ldots\), “12” represents Dec \\ \cline{2-3}  & Day & “1” denotes the first day for each month, “2” means the second day for each month, \(\ldots\), “31” denotes the last day for each month. \\ \cline{2-3}  & Week & “1” denotes Monday, “2” means Tuesday, \(\ldots\), “7” represents Sunday. \\ \cline{2-3}  & Bus line & The attribute was applied to visualize the heat map. \\ \cline{2-3}  & Bus station & The attribute was applied to visualize the heat map. \\ \cline{2-3}  & Station passengers & The attribute represents the passengers boarding at each bus station, which was applied to calculate the number of passengers at all stations for each day \\ \cline{2-3}  & Passengers & Number of passengers on the bus line for each day \\ \hline \multirow{7}{*}{Tmax} & Temp & Average temperature, degrees Celsius, \({}^{\circ}\)C \\ \cline{2-3}  & Tmax & Maximum temperature, degrees Celsius, \({}^{\circ}\)C \\ \cline{2-3}  & Tmin & Minimum temperature, degrees Celsius, \({}^{\circ}\)C \\ \cline{2-3}  & RH & Relative humidity, percent \% \\ \cline{2-3}  & RH\_min & Minimum relative humidity, percent \% \\ \cline{2-3}  & WS & The wind speed was taken as the average value 10 min before the observation point, meters per second (m/s). \\ \cline{2-3}  & WS\_max & The maximum wind speed was taken as the maximum instantaneous wind speed within 1 h before the observation point, meters per second (m/s). \\ \cline{2-3}  & Precp & The precipitation was taken as the total rainfall in a day, milliliters per day. \\ \cline{2-3}  & Precp\_hr & Total number of rainy hours in a day, number of hours \\ \cline{2-3}  & Precp\_10 max & Maximum precipitation within ten minutes of the day, milliliters per ten minutes. \\ \cline{2-3}  & Precp\_hrmax & Maximum precipitation within an hour of the day, milliliters per hour. \\ \cline{2-3}  & SunS & Sunshine hours, number of hours \\ \cline{2-3}  & SunS\_rate & The sunshine rate is a percentage ratio of the recorded bright sunshine duration and daylight duration in a day, percent \%. \\ \cline{2-3}  & GlobalRad & Global radiation refers to a value used to measure the solar radiation energy for a given time and area, megajoules per square meter and per day, MJ/m\({}^{2}\). \\ \cline{2-3}  & UVImax & The maximum ultraviolet index refers to the international measurement standard for the solar ultraviolet (UV) radiation intensity at a certain place on a certain day; the index value from 0 to 11+ is divided into five levels. \\ \hline \multirow{7}{*}{Lag period} & Lag 1 & A Lag 1 autocorrelation is the correlation between values that are one time period apart. \\ \cline{2-3}  & Lag 2 & \\ \cline{2-3}  & Lag 3 & \\ \cline{2-3}  & Lag 4 & a Lag k autocorrelation is a correlation between values that are \(k\) time periods apart, where \(k\) = 2, 3, 4, 5, 6, 7. \\ \cline{2-3}  & Lag 6 & \\ \cline{2-3}  & Lag 7 & \\ \hline \end{tabular}
\end{table} 
Table 2: Attributes used in this study. Step 4: Establishment of an integrated-weight time-series forecasting model  
After extracting/adding the attributes of Steps 2 and 3, all attributes are listed in Table 2. This study proposed an integrated-weight time-series forecasting model to improve forecasting performance. That is, this step applied the four intelligent forecast methods to train these parameters of multi-variables and lag periods based on different data series and forecast passenger numbers. Further, we input the collected data with time order to train their parameters by SVR, MLP, RBF network, and LSTM; hence, these models are called intelligent time-series models.