# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 7 Conclusions  
This study introduces a novel carbon benefits-based signal control method within a connected vehicle (CV) environment. The proposed method uniquely integrates a carbon incentive mechanism with signal control to enhance vehicle compliance with speed guidance, aiming to reduce carbon emissions at signalized intersections. By optimizing signal timing and vehicle speed guidance in a coordinated manner, the method effectively minimizes vehicle speed fluctuations, significantly contributing to emission reduction.  
The performance of the proposed method was evaluated using a microscopic simulation platform, demonstrating its effectiveness across various traffic demand scenarios and different penetration rates of connected and automated vehicles (CBCVs). The results indicate that the proposed method achieves substantial reductions in carbon emissions, stop frequencies, and vehicle delays compared to both fixed-timing and adaptive signal control methods. Specifically, carbon emissions were reduced by 5.24% to 17.60%, stop frequencies decreased by 14.8% to 75.4%, and delays were reduced by 22.82% to 52.62%. Under non-uniform traffic conditions, the emission reductions ranged from 8.92% to 14.51% compared to fixed-timing control and from 4.34% to 9.20% compared to adaptive control (when CR is zero in the proposed method).  
Furthermore, the proposed method provides a comprehensive framework for calculating carbon emission reductions and corresponding economic incentives for vehicles adhering to speed guidance. According to the carbon incentive framework, the average reward per CBCV ranges between RMB 0.0015 and 0.0165. Correspondingly, the rewards provided to CBCVs for each ton of carbon emission reduction at the intersection range from RMB 7.5 to 31.5. While the carbon rewards based solely on emission reductions may be modest for individual vehicles, they offer a new perspective. Traffic managers can provide additional rewards to encourage vehicle participation in carbon-incentive traffic control methods. These additional rewards can be proportionally distributed based on actual emission reductions, potentially achieving better incentive effects.  
Future research could focus on both theoretical and practical advancements in this study. On the theoretical side, it is important to examine how the proposed incentive amounts influence drivers' motivation to participate. Additionally, integrating CIM with broader traffic management systems, such as multi-intersection signal control or route-based traffic control, could further enhance its environmental and operational benefits. On the practical side, the priority should be to establish an accurate carbon emission pricing mechanism that aligns with both government policies and consumer demands.  
Conceptualization, Z.K., L.A., X.Y. and J.L.; methodology, Z.K.; software, Z.K. and J.L.; validation, Z.K. and L.A.; formal analysis, Z.K.; investigation, Z.K.; resources, X.Y.; data curation, Z.K. and L.A.; writing--original draft preparation, Z.K.; writing--review and editing, Z.K. and L.A.; visualization, Z.K. and L.A.; supervision, X.Y.; project administration, L.A.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Zhengzhou Major Science and Technology Project, grant number 2021 KJZX0060, and the National Natural Science Foundation of China, grant number No. 52072264.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.