Page 108 - Read Online

Yu et al. Carbon Footprints 2025, 4, 17 https://dx.doi.org/10.20517/cf.2025.12 Page 23 of 23

power distributions. Sci. Total. Environ. 2015, 514, 108-18. DOI
78. Liu, B.; Li, F.; Hou, Y.; Antonio, B. S.; Ma, X. Unveiling built environment impacts on traffic CO emissions using Geo-CNN
2
weighted regression. Transp. Res. D. Transp. Environ. 2024, 132, 104266. DOI
79. Xiao, Z.; Liu, B.; Wu, L.; et al. Exploring spatio-temporal carbon emission across passenger car trajectory data. IEEE. Trans. Intell.
Transport. Syst. 2025, 26, 1812-25. DOI
80. Xu, Z.; Kang, Y.; Cao, Y.; Li, Z. Spatiotemporal graph convolution multifusion network for urban vehicle emission prediction. IEEE.
Trans. Neural. Netw. Learn. Syst. 2021, 32, 3342-54. DOI
81. Chen, Z.; Zou, T.; Xu, Z.; Zhang, Y.; Chen, N. SAGE-GSAN: A graph-based method for estimating urban taxi CO emissions using
street view images. J. Clean. Prod. 2024, 474, 143543. DOI
82. Alfaseeh, L.; Tu, R.; Farooq, B.; Hatzopoulou, M. Greenhouse gas emission prediction on road network using deep sequence
learning. Transp. Res. D. Transp. Environ. 2020, 88, 102593. DOI
83. Tian, J.; Zeng, G.; Zhao, J.; Zhu, X.; Zhang, Z. A data-driven modeling method of virtual synchronous generator based on LSTM
neural network. IEEE. Trans. Ind. Inf. 2024, 20, 5428-39. DOI
84. Cini, A.; Marisca, I.; Bianchi, F. M.; Alippi, C. Scalable spatiotemporal graph neural networks. Proc. Aaai. Conf. Artif. Intell. 2023,
37, 7218-26. DOI
85. Xia, Y.; Liao, C.; Chen, X.; et al. Future reductions of China’s transport emissions impacted by changing driving behaviour. Nat.
Sustain. 2023, 6, 1228-36. DOI
86. Qiu, J. D.; Xu, X.; Qu, X. M.; Ji, Y. X. Carbon emission accounting method for mobile sources in urban road traffic. Urban. Transp.
China. 2023, 21, 77-86. (in Chinese) DOI
87. Yu, Z.; Li, W.; Liu, Y.; et al. Quantification and management of urban traffic emissions based on individual vehicle data. J. Clean.
Prod. 2021, 328, 129386. DOI
88. Wen, Y.; Wu, R.; Zhou, Z.; et al. A data-driven method of traffic emissions mapping with land use random forest models. Appl.
Energy. 2022, 305, 117916. DOI
89. Zhang, S.; Shi, J.; Huang, Y.; Shen, H.; He, K.; Chen, H. Investigating the effect of dynamic traffic distribution on network-wide
traffic emissions: an empirical study in Ningbo, China. PLoS. One. 2024, 19, e0305481. DOI PubMed PMC
90. Dong, Y.; Guo, X.; Wang, M.; Xu, J. Modeling carbon intensity of electric vehicles in the well-to-wheels phase under different traffic
flow conditions. iScience 2024, 27, 111070. DOI PubMed PMC
91. Peng, Z.; Ji, H.; Easa, S. M.; Wang, C.; Wang, Y.; Pan, H. Analyzing spatiotemporal truck emission pattern using explainable
machine learning: a case study in Xi’an, China. Transp. Res. D. Transp. Environ. 2024, 137, 104489. DOI
92. Zhang, Y.; Zhou, Y.; Fujita, H. Distributed multi-agent reinforcement learning for cooperative low-carbon control of traffic network
flow using cloud-based parallel optimization. IEEE. Trans. Intell. Transp. Syst. 2024, 25, 20715-28. DOI
93. Zhang, G.; Chang, F.; Jin, J.; Yang, F.; Huang, H. Multi-objective deep reinforcement learning approach for adaptive traffic signal
control system with concurrent optimization of safety, efficiency, and decarbonization at intersections. Accid. Anal. Prev. 2024, 199,
107451. DOI
94. Guo, Y.; Ma, J.; Xiong, C.; Li, X.; Zhou, F.; Hao, W. Joint optimization of vehicle trajectories and intersection controllers with
connected automated vehicles: Combined dynamic programming and shooting heuristic approach. Transp. Res. C. Emerg. Technol.
2019, 98, 54-72. DOI
95. Jiang, Q.; Yueshuai, H. B.; Ma, J. Connected automated vehicle impacts in Southern California part-II: VMT, emissions, and equity.
Transp. Res. D. Transp. Environ. 2022, 109, 103381. DOI
96. Chang, X.; Wu, J.; Sun, H.; Yan, X. A smart predict-then-optimize method for dynamic green bike relocation in the free-floating
system. Transp. Res. C. Emerg. Technol. 2023, 153, 104220. DOI
97. Molloy, J.; Tchervenkov, C.; Axhausen, K. W. Estimating the external costs of travel on GPS tracks. Transp. Res. D. Transp.
Environ. 2021, 95, 102842. DOI
98. Xiao, M.; Chen, L.; Feng, H.; Peng, Z.; Long, Q. Sustainable and robust route planning scheme for smart city public transport based
on multi-objective optimization: digital twin model. Sustain. Energy. Technol. Assess. 2024, 65, 103787. DOI
99. Li, Y.; Li, W. X.; Wei, Y. A. Research progress and prospects of carbon emission trading in road transportation. J. Tongji. Univ. (Nat.
Sci. Ed). 2018, 46, 465-71. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2018&filename=
TJDZ201804008 (accessed 2025-06-09). (in Chinese)
100. Teng, W.; Shi, C.; Yu, Y.; Li, Q.; Yang, J. Uncovering the spatiotemporal patterns of traffic-related CO emission and carbon
2
neutrality based on car-hailing trajectory data. J. Cleaner. Prod. 2024, 467, 142925. DOI
101. Sun, Y.; Liu, S.; Li, L. Grey correlation analysis of transportation carbon emissions under the background of carbon peak and carbon
neutrality. Energies 2022, 15, 3064. DOI
102. Li, C.; Chen, Y. Evaluation and comparative analysis of the mixed traffic flow on urban roads considering the green cost. Sustain.
Energy. Technol. Assess. 2023, 57, 103292. DOI