# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 4 The Analysis of Experiments  
### Construction of Simulation Test Environment  
The simulation experimental intersection is Luotian/Xinhu intersection in Shenzhen, Guangdong Province, China. It is surrounded by Bao'an Stadium and large residential areas, with dense bus lines passing through intersections. The intersection is shown in Figure 4.  
As shown in Figure 5, the current existing traffic signal plan of this intersection is a fixed-time signal plan. Due to its proximity to the nearby Bao'an Stadium and large residential areas, the intersection has a large number of pedestrians. So, the traffic signal plan of the intersection should not only consider motor vehicles but also consider cross-street pedestrians. Compared to the control strategy for straight-ahead directions in one phase, and left turns (from opposites approach) in another phase, the current existing traffic signal planning (as shown in Figure 5) has the following advantages: (1) the waiting time for pedestrians to cross the street can be reduced; (2) the waiting time of pedestrians on the central safety island can also be reduced. In Figure 5, the double-arrow dotted line indicates the direction of pedestrians crossing, and pedestrian twice crossing the street, and the single-arrow solid line indicates the direction of motor vehicles passing. In a fixed-time control, the maximum green time of each phase is used as the actual green light time of each phase. This paper still follows the current existing traffic signal planning. On the one hand, the current traffic signal planning is the optimal planning under the long-term practice of professional signal timing social service teams. On the other hand, compared with the current traffic signal planning for a fixed-time control, only the pedestrians in 41P and 31P will increase the waiting time of 31-38 s in the central safety island (1 m \(\times\) 6 m). It would not significantly increase the risk of pedestrians staying on the central safety island. According to the definition of the traffic state space of the intelligent agent in 2.1, and considering that the maximum vehicle queue length at Luotian/Xinhu intersection in the real world is less than 100 m, to order to reduce the representation dimension of the state space and considering the redundant design, so only describe the traffic state within a range of 180 m in Luotian/Xinhu intersection, and the state vector S can be represented by 1 \(\times\) 262 vector. And the state parameters, such as bus location and busload, queuing vehicles, the information of signal timing, can be obtained in the bus networking environment. Figure 4: SUMO simulation environment of Luotian–Xinhu intersection. The bus location can be obtained based on the positioning information; the busload can be obtained by counting the on-board camera in the bus; the number of queuing vehicles can be obtained by radar vehicle detectors; and the signal timing state, such as the current phase green time, can be obtained by reading the information from the traffic signal controller. The experimental traffic data at the intersection were obtained from the manual survey data from 12:00 to 13:00 on 22 November 2021, as shown in Table 1. The busload data were based on the manual statistics of the bus video, as shown in Table 2. In Tables 1 and 2, column \"Direction\" is defined as \"entrance direction: turn direction\". A, L, S, and R indicate turn around, turn left, go straight, and turn right, respectively. The algorithm was built on the Windows 10 operating system with i7-9700 CPU and 16.0 GB memory, implemented on python 3.8.5. The neural network was built by TensorFlow 2.8.0, the traffic operation simulation environment was SUMO 1.8.0. The simulation time is 3600 s for an iteration and there are 600 iterations in total. The deep neural network adopts a three-layer structure, with 262 nodes in the input layer, 200 nodes in the hidden layer, and 4 nodes in the output layer. The neural network parameters are updated every 10 decision steps after 100 decision steps.