## 6 Conclusions  
This paper addresses the critical issue of delays faced by private vehicles due to the prioritization of self-driving buses at arterial intersections. To tackle this challenge, a strategy optimization method utilizing the powerful DQN algorithm is proposed. The method not only achieves real-time signal timing for self-driving buses but also effectively mitigates potential delays for private vehicles caused by TSP. To optimize the algorithm model efficiently, an environment model based on real road network data is utilized during agent training. Through careful design of the state space, action space, and reward function, the DQN model is trained to learn the optimal control strategy. An innovative approach is introduced to enhance the allocation of vehicle road rights in the optimization algorithm model, incorporating an equalization reward component within the reward-and-punishment function. The value of the reward function is optimized through a deep  
Figure 8: Comparisons of the average intersection queuing length under different scenarios.  
Figure 7: Comparisons of the intersection delay under different scenarios.  
neural network, a well-defined loss function, and an experience-playback buffer area. The experimental results showcase the remarkable effectiveness of this method. Additionally, performing extensive hyperparameter tuning can help optimize the performance of the DQN algorithm, including as to learning rate, batch size, and discount factor, ensuring the identification of the best parameter settings. By implementing the optimized TSP method through DQN control for self-driving, substantial enhancements in TSP effectiveness and overall road network flow are achieved. These significant outcomes contribute valuable insights to the realm of intelligent control for urban public transportation, paving the way for future advancements in efficient traffic management and sustainable mobility solutions.  
In this study, the DQN algorithm is utilized for real-time signal priority of self-driving buses, and its effectiveness is validated in simplified scenarios. However, only one reinforcement-learning algorithm is investigated. In the future, more learning algorithms can be studied and compared to improve performance. Additionally, the impacts of different traffic-flow distributions on the model remain unexplored. Further experiments with diverse traffic data could help optimize and generalize the model.  
Conceptualization, H.L. and X.Z.; methodology, H.L. and S.L.; software, S.L.; validation, S.L. and H.L.; formal analysis, S.L.; investigation, S.L. and H.L.; resources, H.L. and S.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, H.L., S.L. and X.Z.; visualization, S.L.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tackle Key Problems in Science and Technology Project of Henan Province (222102240052), the Foundation for High-Level Talents of Henan University of Technology (2018 BS029), the Doctoral Foundation of Henan University of Technology for [PERSON] (31400348), and Research Funds for [PERSON], Chief Expert of Traffic Engineering (21410003).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.