# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 4 The Analysis of Experiments
### Training Results Analysis  
The simulation time of 3600 s is an iteration. Six indexes are selected as traffic efficiency indexes, namely, the cumulative travel time of all vehicles, cumulative waiting time of all vehicles, cumulative travel time of all vehicles, cumulative waiting time of all buses, cumulative waiting time of all buses, cumulative waiting time of all passengers, and cumulative travel time of all passengers. Figure 6 shows the curve of the prediction error and the number of iterations of the neural network. Figure 7 shows the curve of the six traffic efficiency indexes and the number of iterations. The results show that with the increase in the number of iterations, the prediction error and traffic efficiency index of the neural network shows a downward trend. When the number of iterations reaches 330, the prediction error and six traffic efficiency indexes of the neural network converge, indicating the effectiveness of the method. Through calculation, comparing the first 300 iterations with the last 300 iterations, the cumulative travel time of all vehicles was reduced by 20%, the cumulative waiting time of all vehicles was reduced by 53%, the cumulative travel time of all buses was reduced by 16%, the cumulative waiting time of all buses was reduced by 46%, the cumulative waiting time of all passengers was reduced by 19%, and the cumulative travel time of all passengers reduced by 51%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Direction** & **Bus ID** & **Busload** & **Direction** & **Bus ID** & **Busload** & **Direction** & **Bus ID** & **Busload** & **Direction** & **Bus ID** & **Busload** \\ \hline NE: L & 1 & 33 & SW: S & 5 & 7 & SE: S & 10 & 28 & SE: R & 5 & 32 \\ NE: L & 2 & 30 & SW: S & 6 & 20 & SE: S & 11 & 10 & NW: L & 1 & 13 \\ NE: L & 3 & 27 & SW: S & 7 & 11 & SE: S & 12 & 20 & NW: L & 2 & 6 \\ NE:S & 1 & 14 & SW: S & 8 & 7 & SE: S & 13 & 14 & NW: S & 1 & 12 \\ NE: S & 2 & 27 & SW: S & 9 & 28 & SE: S & 14 & 18 & NW: S & 2 & 7 \\ NE: S & 3 & 29 & SW: S & 10 & 26 & SE: S & 15 & 9 & NW: S & 3 & 14 \\ NE:S & 4 & 26 & SW: S & 11 & 32 & SE: S & 16 & 11 & NW: S & 4 & 31 \\ NE: S & 5 & 10 & SW: R & 1 & 19 & SE: S & 17 & 7 & NW: S & 5 & 29 \\ NE:S & 6 & 19 & SW: R & 2 & 20 & SE: S & 18 & 26 & NW: S & 6 & 11 \\ NE: R & 1 & 32 & SE: L & 1 & 17 & SE: S & 19 & 16 & NW: S & 7 & 9 \\ NE: R & 2 & 26 & SE: L & 2 & 11 & SE: S & 20 & 6 & NW: S & 8 & 33 \\ SW: L & 1 & 33 & SE: L & 3 & 34 & SE: S & 21 & 20 & NW: S & 9 & 31 \\ SW: L & 2 & 7 & SE: L & 4 & 24 & SE: S & 22 & 6 & NW: S & 10 & 13 \\ SW: L & 3 & 9 & SE: L & 5 & 17 & SE: S & 23 & 31 & NW: S & 11 & 14 \\ SW: L & 4 & 9 & SE: S & 1 & 34 & SE: S & 24 & 12 & NW: S & 12 & 20 \\ SW: L & 5 & 19 & SE: S & 2 & 11 & SE: S & 25 & 16 & NW: S & 13 & 12 \\ SW: L & 6 & 6 & SE: S & 3 & 22 & SE: S & 26 & 6 & NW: S & 14 & 5 \\ SW: L & 7 & 30 & SE: S & 4 & 32 & SE: S & 27 & 10 & NW: S & 15 & 29 \\ SW: L & 8 & 5 & SE: S & 5 & 7 & SE: S & 28 & 10 & NW: S & 16 & 26 \\ SW: S & 1 & 13 & SE: S & 6 & 6 & SE: R & 1 & 30 & NW: S & 17 & 13 \\ SW: S & 2 & 31 & SE: S & 7 & 7 & SE: R & 2 & 27 & NW: R & 1 & 24 \\ SW: S & 3 & 8 & SE: S & 8 & 8 & SE: R & 3 & 7 & NW: R & 2 & 18 \\ SW: S & 4 & 32 & SE: S & 9 & 10 & SE: R & 4 & 34 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intersection busload statistics.  
Figure 6: Training error curve.