# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 4 The Analysis of Experiments
### Comparison of Intelligent Agent Operation Effect  
To verify the effect, the proposed method was compared with a fixed-time control and an actuated control. In the fixed-time control, as shown in Figure 5, the signal planning uses the maximum green time as the duration time of each phase. In the full actuated control, the signal phase, the minimum green time, and the maximum green time are also shown in Figure 5. The green light time of phase changes without changing the sequence of phases. The unit green light extension time is taken as 6 s, and the inductive loop detector is set at the range of 30 m from the stop line. When the bus or other vehicle is detected within a preset time interval of 6 s, the phase will be extended until the maximum green time. When no vehicle is detected within the preset time interval of 6 s, the phase will be changed. In the experiment, 10 groups of random seeds were added to ensure the fairness of the experiment by simulating 10 groups of different departure rules under the specified traffic flow conditions, and the average value of 10 groups of result data was taken to  
Figure 7: Traffic efficiency index curve under the training: (**a**) cumulative travel time of all vehicles; (**b**) cumulative waiting time of all vehicles; (**c**) cumulative travel time of buses; (**d**) cumulative waiting time of buses; (**e**) cumulative travel time of passengers; (**f**) cumulative waiting time of passengers.  
compare the operation effect of three methods (as shown in Figure 8). The results of the proposed method show that the average cumulative travel time of all vehicles is 10.2% less than that of a fixed-time control and 3.4% less than that of an actuated control. The average cumulative waiting time of all vehicles is reduced by 35.9% compared with a fixed-time control and 14.3% with an actuated control. The average cumulative travel time of buses is 9.7% less than a fixed-time control and 6.7% less than an actuated control. The average cumulative waiting time of buses is 36.9% less than a fixed-time control and 27.2% less than an actuated control. The average cumulative waiting time of all passengers is reduced by 9.9% compared with a fixed-time control and 3.9% with an actuated control. The average cumulative travel time of all passengers is reduced by 35.9% compared with a fixed-time control and 16.7% with an actuated control.  
Figure 8: Effect comparison between three methods: fixed-time control method, actuated control method and DQN method: fixed-time control method, actuated: (**a**) cumulative travel time of all vehicles; (**b**) cumulative waiting time of all vehicles; (**c**) cumulative travel time of all buses; (**d**) cumulative waiting time of all buses; (**e**) cumulative travel time of all passengers; (**f**) cumulative waiting time of all passengers.  
We designed \"high\", \"shoulder\", and \"low\" traffic demand scenarios to verify the adaptability of our algorithm. The traffic flow of \"shoulder\" traffic demand is shown in Table 1, the traffic flow of \"high\" and \"low\" traffic demand increases and decreases by 10% proportionally, respectively on the basis of \"shoulder\" traffic demand. In \"high\", \"shoulder\", and \"low\" traffic demand, the DQN control method proposed in this paper is superior to the fixed-time control method and actuate control method. As shown in Table 3, the average travel time of all vehicles can be reduced by 1.6-10.6%; the average waiting time of all vehicles can be reduced by 12.1-38.3%; the average travel time of buses can be reduced by 5.5-11.9%; the average cumulative waiting time of buses can be reduced by 35.9-37.0%; the average waiting time of passengers can be reduced by 2.4-10.8%; and the average travel time of passengers can be reduced by 15.4-50.7%.