journal article·2024-08-29Open access

A Carbon Benefits-Based Signal Control Method in a Connected Environment

Zhen Kang, Lianhua An, Xiaoguang Yang, Jintao Lai

Applied Sciences· Volume 14· Issue 17

Abstract

This study proposes an innovative carbon benefits-based signal control method for connected vehicle (CV) environments, aiming to reduce carbon emissions at urban intersections. By integrating a Carbon Inclusion Mechanism (CIM), the proposed approach offers carbon rewards to vehicles adhering to speed guidance. The method exhibits the following features: (i) higher ceiling of carbon emissions reduction at signal control intersection; (ii) higher compliance rate (CR) of vehicles by taking advantage of carbon economic incentives; (iii) a method for calculating carbon emissions reduction at the intersection. To validate the effectiveness, performance evaluations of emissions, stop frequencies, and delays were conducted through microscopic simulation. Sensitivity analysis encompassed various traffic demands, different CRs of carbon-benefit connected vehicles (CBCVs), and unbalanced traffic demand. The results demonstrated that the proposed method excels in reducing traffic emissions, stop frequencies, and delays. 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%. By utilizing connected vehicle technology and CIM, this study contributes to sustainable urban traffic management, laying a foundation for future research and the practical implementation of emission reduction strategies.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Vehicle emissions and performance · Traffic control and management · Wireless Sensor Networks for Data Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China

Funding

Zhengzhou Major Science and Technology Project · National Natural Science Foundation of China

References (40)

Show source-provided references
1. Agency (IEA) (2023). CO2 Emissions in 2022, International Energy Agency.
2. Lents, J., and Nikkila, N. (2004). IVE Model User Manual (Version 1.1.1), International Sustainable Systems Research Center (ISSRC), EUA.
3. Traffic and emission simulation in China based on statistical methodology10.1016/j.atmosenv.2010.10.051 ↗
4. Eco-Speed Harmonization with Partially Connected and Automated Traffic at an Isolated Intersection10.1155/2023/9948462 ↗
5. Vehicle State and Bias Estimation Based on Unscented Kalman Filter with Vehicle Hybrid Kinematics and Dynamics Models10.1007/s42154-023-00230-7 ↗
6. A real-time adaptive signal control in a connected vehicle environment10.1016/j.trc.2015.01.007 ↗
7. An improved adaptive signal control method for isolated signalized intersection based on dynamic programming10.1109/MITS.2016.2605318 ↗
8. An adaptive signal control scheme to prevent intersection traffic blockage
9. A dynamic optimization method for adaptive signal control in a connected vehicle environment10.1080/15472450.2019.1643723 ↗
10. Simulation-based optimization method for arterial signal control considering traffic safety and efficiency under uncertainties10.1111/mice.12876 ↗
11. Adaptive Traffic Light Control with Deep Reinforcement Learning: An Evaluation of Traffic Flow and Energy Consumption10.1109/TITS.2023.3305548 ↗
12. Eco-oriented signal control of intersections with vehicle type considerations using integrated estimation of driving behavior10.1016/j.jclepro.2022.133986 ↗
13. A speed guidance model accounting for the driver’s bounded rationality at a signalized intersection10.1016/j.physa.2017.01.025 ↗
14. Field implementation and testing of an automated eco-cooperative adaptive cruise control system in the vicinity of signalized intersections10.1016/j.trd.2018.11.019 ↗
15. Physics-augmented data-enabled predictive control for eco-driving of mixed traffic considering diverse human behaviors10.1109/TCST.2024.3361393 ↗
16. Optimal-Control-Based Eco-Driving Solution for Connected Battery Electric Vehicle on a Signalized Route10.1007/s42154-023-00255-y ↗
17. Dynamic eco-driving speed guidance at signalized intersections: Multi vehicle driving simulator based experimental study10.1155/2018/6031764 ↗
18. Consensus and optimal speed advisory model for mixed traffic at an isolated signalized intersection10.1016/j.physa.2019.121789 ↗
19. Green light optimal speed advisory system designed for electric vehicles considering queuing effect and driver’s speed tracking error10.1109/ACCESS.2020.3037105 ↗
20. Real-time implementation comparison of urban eco-driving controls10.1109/TCST.2023.3304910 ↗
21. Traffic signal timing and trajectory optimization in a mixed autonomy traffic stream10.1109/TITS.2021.3058193 ↗
22. Traffic signal control by leveraging cooperative adaptive cruise control (CACC) vehicle platooning capabilities10.1016/j.trc.2019.05.027 ↗
23. Reducing gasoline consumption in mixed connected automated vehicles environment: A joint optimization framework for traffic signals and vehicle trajectory10.1016/j.jclepro.2020.121836 ↗
24. Optimizing signalized intersections performance under conventional and automated vehicles traffic10.1109/TITS.2019.2921025 ↗
25. Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream10.1016/j.trc.2020.102659 ↗
26. DRL-TP3: A learning and control framework for signalized intersections with mixed connected automated traffic10.1016/j.trc.2021.103416 ↗
27. Eco-approach at an isolated actuated signalized intersection: Aware of the passing time window10.1016/j.jclepro.2023.140493 ↗
28. A generic approach to eco-driving of connected automated vehicles in mixed urban traffic and heterogeneous power conditions10.1109/TITS.2023.3286441 ↗
29. Cluster-wise cooperative eco-approach and departure application for connected and automated vehicles along signalized arterials10.1109/TIV.2018.2873912 ↗
30. Hill, C.J., and Garrett, J.K. (2011). AASHTO Connected Vehicle Infrastructure Deployment Analysis, Joint Program Office for Intelligent Transportation Systems.
31. Climate change 2022: Mitigation of climate change
32. How financial inclusion affects the collaborative reduction of pollutant and carbon emissions: The case of China10.1016/j.eneco.2022.105847 ↗
33. Achieving low-carbon urban passenger transport in China: Insights from the heterogeneous rebound effect10.1016/j.eneco.2019.06.009 ↗
34. Carbon footprint of urban public transport systems in Indian cities10.1016/j.cstp.2019.01.005 ↗
35. Paths for Carbon Peak and Carbon Neutrality in Transport Sector in China
36. Ghosh, A. (2020). Possibilities and challenges for the inclusion of the electric vehicle (EV) to reduce the carbon footprint in the transport sector: A review. Energies, 13.10.3390/en13102602 ↗
37. Does China’s carbon inclusion policy promote household carbon emissions reduction? Theoretical mechanisms and empirical evidence10.1016/j.eneco.2024.107462 ↗
38. Carbon Inclusion for Green Travel and Practices in Beijing
39. Signal timing at an isolated intersection under mixed traffic environment with self-organizing connected and automated vehicles10.1111/mice.12961 ↗
40. A generic simulation platform for cooperative adaptive cruise control under partially connected and automated environment10.1016/j.trc.2020.102874 ↗