# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 5 Conclusions  
This paper proposes a deep Q-learning control method for multi-flow bus signal priority at intersections in the bus networking environment. By taking into account the needs of social vehicles and pedestrians crossing the street, it provides priority access for each traffic flow of buses at intersections. Based on the core objective of bus priority, the proposed method optimizes the design of state, action and reward functions, building a bus signal priority deep Q-learning simulation platform through the COM interface of the SUMO simulation platform and Python programming software. As shown in the simulation experiment, compared with a fixed-time control and an actuated control, the waiting time of buses decreased by 36.9% and 27.2%, respectively, and the average waiting time of all vehicles decreased by 35.9% and 16.7%, respectively.  
Although the proposed method has achieved some achievement in addressing the signal control problem considering bus priority, it is still less successful and needs further development. Moreover, there is still a risk that bus priority control can lead to increased emissions and thus be in conflict with sustainability. Therefore, with careful consideration of sustainability, further work will be focused on researching bus priority control as well as trunk and regional bus priority control.  
Conceptualization, W.S.; methodology, W.S.; software, L.Z.; validation, L.Z.; formal analysis, R.D.; investigation, R.D.; resources, H.W.; data curation, H.W.; writing--original draft preparation, W.S.; writing--review and editing, J.W.; visualization, H.W.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by [the Guangdong Basic and Applied Basic Research Foundation] under grant number [2022A1515111039]. The APC was funded by [the Guangdong Basic and Applied Basic Research Foundation].  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Traffic Demand**} & \multicolumn{2}{c}{**All Buses**} & \multicolumn{2}{c}{**All Vehicles**} & \multicolumn{2}{c}{**All Passengers**} \\  & & **Travel Time (s)** & **Waiting Time (s)** & **Travel Time (s)** & **Waiting Time (s)** & **Travel Time (s)** & **Waiting Time (s)** \\ \hline \multirow{2}{*}{Fixed-time control} & high & 14,066 & 3317 & 224,430 & 61,708 & 619,513 & 184,239 \\  & shoulder & 11,553 & 3119 & 200,843 & 54,830 & 591,270 & 159,345 \\  & low & 10,808 & 2814 & 178,793 & 51,723 & 534,785 & 134,763 \\  & high & 13,877 & 2901 & 212,796 & 46,766 & 582,302 & 148,243 \\ Actuated control & shoulder & 11,197 & 2705 & 186,728 & 41,010 & 554,194 & 122,269 \\  & low & 10,566 & 2559 & 163,089 & 38,001 & 488,560 & 90,876 \\  & high & 12,388 & 2155 & 200,460 & 41,118 & 565,513 & 125,478 \\ DQN & shoulder & 10,429 & 1966 & 180,316 & 35,140 & 532,584 & 102,009 \\  & low & 9984 & 1774 & 160,398 & 31,916 & 476,914 & 66,435 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect comparison in three traffic demand.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.