# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 3 Materials and Methods
### Multi-Source Data Fusion Based on a Spatio-Temporal Grid
#### 3.4.3 Multi-Source Data Fusion  
Taxi GPS data include taxi ID, recorded time, geographical location (longitude and latitude), driving speed, driving direction, passenger load and other information. A large number of taxi trajectory data with high accuracy, wide coverage and rich content have become an important data source for studying the travel rules of urban residents [16]. The common key taxi trajectory data structures are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field Name** & **Field Type** & **Description** \\ \hline fishnet\_id & int (11) & Grid id number \\ POINT\_X & double & Grid center point \(x\)-axis coordinate \\ POINT\_Y & double & Grid center point \(y\)-axis coordinate/\({}^{\circ}\) \\ Longitude\_min & decimal (10, 6) & The longitude of the grid/\({}^{\circ}\) \\ Longitude\_max & decimal (10, 6) & The longitude of the grid/\({}^{\circ}\) \\ Latitude\_min & decimal (10, 6) & The latitude of the grid/\({}^{\circ}\) \\ Latitude\_max & decimal (10, 6) & The latitude of the grid/\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Map grid information data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fishnet\_id** & **Longitude\_min** & **Longitude\_max** & **Latitude\_min** & **Latitude\_max** \\ \hline
1 & 114.232525 & 114.234807 & 30.53251 & 30.534308 \\
2 & 114.232974 & 114.235256 & 30.53251 & 30.534308 \\
3 & 114.233424 & 114.235706 & 30.53251 & 30.534308 \\
4 & 114.233873 & 114.236155 & 30.53251 & 30.534308 \\
5 & 114.234322 & 114.236604 & 30.53251 & 30.534308 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Map grid information table data segment.  
Figure 2: Mapping grid structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field Name** & **Field Type** & **Description** \\ \hline T\_TargetID & varchar (255) & Data id number \\ T\_UTCT & decimal (15) & Timestamp(Unix) \\ T\_Longitude & decimal (10, 6) & Longitude/\({}^{\circ}\) \\ T\_Latitude & decimal (10, 6) & Latitude/\({}^{\circ}\) \\ T\_Speed & decimal (10, 6) & Driving speed(km/h) \\ T\_Heading & decimal (10, 6) & Driving direction/\({}^{\circ}\) \\ T\_Status & decimal (10) & Driving status \\ \hline \hline \end{tabular}
\end{table}
Table 4: Common taxi trajectory data table.  
Figure 3: Multi-source data fusion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T\_TargetID** & **T\_UTCT** & **T\_Longitude** & **T\_Latitude** & **T\_Speed** & **T\_Heading** & **T\_Status** \\ \hline MMC8000 GPSANDASYN051113-23416 & 1392235225 & 114.301990 & 30.606773 & 8.705277 & 28.17 & 262,144 \\ MMC8000 GPSNADASYN051113-24802 & 1392235226 & 114.285118 & 30.585786 & 8.391806 & 43.75 & 262,144 \\ MMC8000 GPANDASYN051113-22877 & 1392235226 & 114.279390 & 30.577443 & 0 & 0 & 0 \\ MMC8000 GPSNADASYN051113-22877 & 139223525 & 114.279390 & 30.577443 & 0 & 0 & 262,144 \\ MMC8000 GPSNADAS  
residents' work and rest time, urban functional areas, and traffic accessibility to form a multi-data space-time grid shown in Figure 4.