## 5 System Implementation and Field Testing
### Testing Results
#### 5.2.2 Personalized Dynamic Scheduling  
We considered four attractions, naemly Racing Cars, Spinning Tea Cups, Merry-Go-Round, and Mountain Adventures, to test the function of personalized dynamic scheduling, as shown in Figure 5. Assume that the location (i.e., GPS coordinates) of the tourist is (N 24.86284\({}^{\circ}\), E 120.99045\({}^{\circ}\)). In addition, three attractions, i.e., Racing Cars, Spinning Tea Cups, and Merry-Go-Round, are assumed to be added into My Play List. The parameters of these attractions for testing are listed in Table 1.  
* Closest First strategy Suppose that the personalized dynamic scheduling function with the Closest First strategy was activated at 12:07, and the queue lengths of three attractions were all 20 visitors. According to the result of Google Maps Directions API, we obtained the distances between our location and Racing Cars, Spinning Tea Cups, and Merry-Go-Round as 450 m, 220 m, and 55 m, respectively. Obviously, Merry-Go-Round is the closest attraction and should be recommended. In our experiments, the moving time of the tourist is 1 min because the walking time of tourists are assumed to be constant, and the queue length of the attraction is assumed to be 32 visitors. Thus, we obtained the waiting time and the recommended session time as 72 min and 13:20, respectively. As shown in Figure 7, the result verifies that the personalized dynamic scheduling function actually recommended the closest attraction (Merry-Go-Round in this experiment) when we selected the \"Closest First\" strategy. The result also confirms that the recommended session time, moving time, and personalized waiting time are all correctly calculated. * Shortest Waiting Time First strategy  
Suppose that we activated the personalized dynamic scheduling function with the Shortest Waiting Time First strategy at 12:10, and the queue lengths of three attractions were all 20 visitors. To determine the recommended next attraction, we calculated the recommended session time, moving time, and waiting time, as listed in Table 2. Note that this strategy prefers the attraction having the shortest personalized waiting time. Thus, Racing Car should be recommended because it had the shortest personalized waiting time (65 min). Figure 8 illustrates the testing result, which verifies that the personalized dynamic scheduling function actually recommended the attraction with the shortest personalized waiting time (Racing Cars in this experiment) when we considered the \"Shortest Waiting Time First\" strategy. Moreover, the recommended session time, moving time, and personalized waiting time were all correctly determined. * Hottest First strategy  
Suppose that the personalized dynamic scheduling function with strategy \"Hottest First\" was activated at 12:15. The queue lengths of all attractions were assumed to be 20 visitors. According to the database in the central subsystem, we obtained that Spinning Tea Cups should be recommended because it had the largest visit count. Moreover, we determined the recommended session time, moving time, and waiting time as 13:20, 3 min, and 62 min, respectively. Figure 9 shows the testing result, which validates that the personalized dynamic scheduling function actually recommended the hottest attraction (Spinning Tea Cups in this experiment) when we selected the \"Hottest First\" strategy. The result also validates that the recommended session time, moving time, and personalized waiting time were all correctly calculated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attraction Name** & **Next Session Time** & \
\begin{tabular}{c} **Personalized Waiting Time** \\ **(Min)** \\ \end{tabular} &
\begin{tabular}{c} **Recommended Session** \\ **Time** \\ \end{tabular} \\ \hline Racing Cars & 12:20 & 65 & 13:20 \\ Spinning Tree Cups & 12:20 & 67 & 13:20 \\ Merry-Go-Round & 12:20 & 69 & 13:20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculation results for the Shortest Waiting Time First strategy. Figure 8: Experimental results of the Shortest Waiting Time First strategy.