A systematic review of urban road traffic CO₂ emission models

Abstract

With rapid urbanization and increasing mobility demand, urban traffic systems face intensifying congestion, resulting in elevated CO₂ emissions. This paper provides a systematic review of the current status of models estimating CO₂ emissions from urban road traffic, considering their applicability across various traffic management scenarios. Urban road traffic CO₂ emission models can generally be categorized into two main types. Traditional models typically estimate emissions based on average speed, traffic conditions, or vehicle operation modes, whereas data-driven models leverage techniques such as machine learning and deep learning to capture complex emission patterns. The review proposes a set of model selection criteria, namely data availability, computational complexity, interpretability, and transferability. Based on a comparative evaluation of these criteria, the study finds that there is no one-size-fits-all model so far. Instead, model suitability depends heavily on local data infrastructure and specific application needs. Therefore, future work needs to enhance model localization and personalization to improve estimation accuracy, while the integration of spatiotemporal data-driven modeling approaches is likely to become a research hotspot in upcoming studies.

Keywords

Urban road traffic / CO₂ emission models / data-driven modeling / time-series analysis / spatiotemporal modeling / carbon emissions

References

[1] Yang XG,Wang YZ.Review of urban transportation system reconstruction and research in the transformation and development of automobiles.Transp Res2022;8:2-20(in Chinese)

[2] Yang XG.Liu LW. Key issues and research progress of urban road traffic health checks.Chin J Highw Transp2024;37:314-42(in Chinese)

[3] Liu Y.Decouple transport CO₂ emissions from China’s economic expansion: a temporal-spatial analysis.Transp Res Part D Transp Environ2020;79:102225

[4] Mishina Y.Are potential reductions in CO₂ emissions via hybrid electric vehicles actualized in real traffic?.Transp Res Part D Transp Environ2017;50:372-84

[5] Shang W,Yu Q.Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data.Appl Energy2023;352:121859

[6] Shang W,Ochieng W.Driving forces of CO₂ emissions from the transport, storage and postal sectors: a pathway to achieving carbon neutrality.Appl Energy2024;365:123226

[7] Zhong H,Liu C,Ke R.Models for predicting vehicle emissions: a comprehensive review.Sci Total Environ2024;923:171324

[8] Zhou M,Wang W.A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing.Transp Res Part D Transp Environ2016;49:203-18

[9] Grote M,Preston J.Including congestion effects in urban road traffic CO₂ emissions modelling: do local government authorities have the right options?.Transp Res Part D Transp Environ2016;43:95-106

[10] Pati D.How to write a systematic review of the literature.Health Environ Res Des J2018;11:15-30

[11] Onan A,Bulut H.Ensemble of keyword extraction methods and classifiers in text classification.Expert Syst Appl2016;57:232-47

[12] Campos R,Pasquali A,Nunes C.YAKE! Keyword extraction from single documents using multiple local features.Inf Sci2020;509:257-89

[13] Gilardi F,Kubli M.ChatGPT outperforms crowd workers for text-annotation tasks.Proc Natl Acad Sci U S A2023;120:e2305016120 PMCID:PMC10372638

[14] DeepSeek-AI ,Yang D.DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning.arXiv2025;arXiv:2501.12948Available online: https://arxiv.org/abs/2501.12948 (accessed 22 January 2025).

[15] Noroozi M,Shah A.An AI-assisted systematic literature review of the impact of vehicle automation on energy consumption.IEEE Trans Intell Veh2023;8:3572-92

[16] Moore AT,Poole RW.The role of VMT reduction in meeting climate change policy goals.Transp Res Part A Policy Pract2010;44:565-74

[17] Matthaios VN,Koutrakis P.In-vehicle exposure to NO₂ and PM_2.5: a comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure.Sci Total Environ2023;900:165537

[18] Veratti G,Teggi S.Description and validation of vehicular emissions from road traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows.Geosci Model Dev2024;17:6465-87

[19] Grote M,Preston J.A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data.Transp Res Part D Transp Environ2018;63:809-25

[20] Logan KG,Hastings A.electric and hydrogen buses: shifting from conventionally fuelled cars in the UK.Transp Res Part D Transp Environ2020;85:102350

[21] Zhang Z,Gao K,Liu Y.Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure.Appl Energy2025;377:124548

[22] Jin H,Teng J.Impact of traffic conditions on bus carbon emissions.Urban Transp China2015;13:66-71(in Chinese)

[23] Lyu P,Liu Y.Review of the studies on emission evaluation approaches for operating vehicles.J Traffic Transp Eng (Engl Ed)2021;8:493-509

[24] Shang W,Xiang Q.The impact of deep reinforcement learning-based traffic signal control on emission reduction in urban road networks empowered by cooperative vehicle-infrastructure systems.Appl Energy2025;390:125884

[25] Zhang H,Guo X,Zhang C.Three-dimensional road network empowered traffic emission estimation via trajectory data.J Clean Prod2024;475:143669

[26] Du B,Geng Y,Xu H.Testing and evaluation of cold-start emissions in a real driving emissions test.Transp Res Part D Transp Environ2020;86:102447

[27] Yu C,Qin Z,Yuan Q.Traffic volume and road network structure: revealing transportation-related factors on PM2.5 concentrations.Transp Res Part D Transp Environ2023;124:103935

[28] Jiang Z,Niu H.Investigating the impact of high-altitude on vehicle carbon emissions: a comprehensive on-road driving study.Sci Total Environ2024;918:170671

[29] Smit R,Boulter P.Validation of road vehicle and traffic emission models - a review and meta-analysis.Atmos Environ2010;44:2943-53

[30] Ehmer M.A comparative study of white box, black box and grey box testing techniques.Int J Adv Comput Sci Appl2012;3

[31] Zhang YX,Zhu YS.Review of research on motor vehicle fuel consumption models.Comput Eng Appl2021;57:14-26https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2022&filename=jsgg202124002 (accessed 2025-06-09). (in Chinese)

[32] Grassi YS,Díaz MF.Vehicular fleet characterisation and assessment of the on-road mobile source emission inventory of a Latin American intermediate city.Sci Total Environ2021;792:148255

[33] Ntziachristos L. EMEP/EEA air pollutant emission inventory guidebook 2023. European Environmental Agency. https://copert.emisia.com/wp-content/uploads/2024/07/1.A.3.b.i-iv-Road-transport-2024.pdf (accessed 2025-03-13).

[34] California Air Resources Board. EMFAC2021 Volume III Technical Document, Version 1.0.1 April 2021. https://ww2.arb.ca.gov/sites/default/files/2021-08/emfac2021_technical_documentation_april2021.pdf (accessed 2025-03-13).

[35] United States Environmental Protection Agency. Mobile source emission factors research. https://www.epa.gov/moves/mobile-source-emission-factors-research (accessed 2025-03-13).

[36] HBEFA-handbook emission factors for road transport. https://www.hbefa.net/ (accessed 2025-03-13).

[37] Keller M,Notter B. Handbook emission factors for road transport 3.1/3.2/3.3: quick reference. https://www.hbefa.net/ (accessed 2025-03-13).

[38] Lu H,Zhao Q,He W.An investigation of the uncertainty of handbook of emission factors for road transport (HBEFA) for estimating greenhouse gas emissions: a case study in Beijing.Transp Res Rec2018;2672:79-88

[39] Liu R,Zhang Z.Integrated MOVES model and machine learning method for prediction of CO₂ and NO from light-duty gasoline vehicle.J Clean Prod2023;422:138612

[40] United States Environmental Protection Agency. MOVES and mobile source emissions research. https://www.epa.gov/moves (accessed 2025-03-13).

[41] International Sustainable Systems Research Center. International vehicle emissions Model (IVE). http://www.issrc.org/ive/ (accessed 2025-03-14).

[42] Center for Environmental Research and Technology. Comprehensive modal emission model (CMEM). https://www.cert.ucr.edu/cmem (accessed 2025-03-14).

[43] German Aerospace Center (DLR). PHEMlight-SUMO documentation. https://sumo.dlr.de/docs/Models/Emissions/PHEMlight.html (accessed 2025-03-14).

[44] Rakha H,Trani A.Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions.Transp Res Part D Transp Environ2004;9:49-74

[45] Sun D,Shen S.Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data.Transp Res Part D Transp Environ2018;62:699-714

[46] Yu Q,Song Y,Zhang H.Potential and flexibility analysis of electric taxi fleets V2G system based on trajectory data and agent-based modeling.Appl Energy2024;355:122323

[47] Jia W,Shan X.Modeling urban bus fuel consumption in Shanghai, China, based on localized MOVES.Transp Res Rec2018;2672:150-62

[48] Hjelkrem OA,Babri S,Södersten C.A battery electric bus energy consumption model for strategic purposes: validation of a proposed model structure with data from bus fleets in China and Norway.Transp Res Part D Transp Environ2021;94:102804

[49] Zhao P,Xiao Z,He Z.Spatiotemporal characteristics and driving factors of CO₂ emissions from road freight transportation.Transp Res Part D Transp Environ2023;125:103983

[50] Wen Y,Zhang J.Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data.Appl Energy2020;260:114357

[51] Li M,Wu K.Autonomous vehicle pollution monitoring: an innovative solution for policy and environmental management.Transp Res D Transp Environ2025;139:104542

[52] Li Z,Yu X,He W.Developing operating mode distributions from sparse trajectories for emission estimation.Transp Res Rec2019;2673:137-48

[53] Weng J,Qiao G,Rong J.Taxi fuel consumption and emissions estimation model based on the reconstruction of driving trajectory.Adv Mech Eng2017;9:168781401770870

[54] Ma W,Alimo PK.Vehicle carbon emission estimation for urban traffic based on sparse trajectory data.Int J Transp Sci Technol2024;16:222-33

[55] Shang W,Wu G,Fang S.Estimation of traffic energy consumption based on macro-micro modelling with sparse data from connected and automated vehicles.Appl Energy2023;351:121916

[56] Smit R,Bagheri S.Real-world emission factors for SUVs using on-board emission testing and geo-computation.Transp Res D Transp Environ2022;107:103286

[57] Yaacob NFF,Abdul Maulud KN.A review of the measurement method, analysis and implementation policy of carbon dioxide emission from transportation.Sustainability2020;12:5873

[58] Shan XN.Review of research on the integration of traffic simulation models and microscopic vehicle emission models.J Transp Eng Inf2021;19:11-24https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=cjfd2021&filename=jtgc202102002&dbcode=cjfq (accessed 2025-06-09). (in Chinese)

[59] Lejri D,Schiper N.Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale.Transp Res D Transp Environ2018;63:588-603

[60] Gräbe RJ.Are we getting vehicle emissions estimation right?.Transp Res D Transp Environ2022;112:103477

[61] Chen Y,Jiang C.Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection.Environ Technol2022;43:3801-20

[62] Shan XN,Zhang XL,Ye JH.Estimation method of light-duty vehicle emission factors based on the localization of MOVES model.J Tongji Univ (Nat Sci)2021;49:1135-143+1201https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=cjfdauto&filename=tjdz202108010&dbcode=cjfq (accessed 2025-06-09). (in Chinese)

[63] Zhang X,Liu Y,Qu X.A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation.Renew Energy2024;234:121243

[64] Wu K,Lin J.Big-data empowered traffic signal control could reduce urban carbon emission.Nat Commun2025;16:2013 PMCID:PMC11868367

[65] Chandrashekar C,Pawar DS.Modeling real-world diesel car tailpipe emissions using regression-based approaches.Transp Res D Transp Environ2024;128:104092

[66] Jia Z,Cao Z.Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation.Sci Total Environ2024;949:174724

[67] Shin S,Lee Y.Designing a steady-state experimental dataset for predicting transient NO_x emissions of diesel engines via deep learning.Expert Syst Appl2022;198:116919

[68] Fei X,Li F.Multi-component fusion temporal networks to predict vehicle exhaust based on remote monitoring data.IEEE Access2021;9:42358-69

[69] Mądziel M.Future cities carbon emission models: hybrid vehicle emission modelling for low-emission zones.Energies2023;16:6928

[70] Shepelev V,Slobodin I.Measuring and modelling the concentration of vehicle-related PM2.5 and PM10 emissions based on neural networks.Mathematics2023;11:1144

[71] Sun Y,Zhang H,Chen H.A parallel supervision system for vehicle CO₂ emissions based on OBD-independent information.IEEE Trans Intell Veh2023;8:2077-87

[72] Zhang Q,Long F.Vehicle emission forecasting based on wavelet transform and long short-term memory network.IEEE Access2018;6:56984-94

[73] Howlader AM,Gammariello R.Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network.Transp Res D Transp Environ2023;116:103654

[74] Sun Z,Ye Z.Long short-term memory network-based emission models for conventional and new energy buses.Int J Sustain Transp2021;15:229-38

[75] Li S,Haroon M.Estimation of transport CO₂ emissions using machine learning algorithm.Transp Res D Transp Environ2024;133:104276

[76] Singh M.Deep learning model based CO₂ emissions prediction using vehicle telematics sensors data.IEEE Trans Intell Veh2023;8:768-77

[77] Song G,Yu L.Delay correction model for estimating bus emissions at signalized intersections based on vehicle specific power distributions.Sci Total Environ2015;514:108-18

[78] Liu B,Hou Y,Ma X.Unveiling built environment impacts on traffic CO₂ emissions using Geo-CNN weighted regression.Transp Res D Transp Environ2024;132:104266

[79] Xiao Z,Wu L.Exploring spatio-temporal carbon emission across passenger car trajectory data.IEEE Trans Intell Transport Syst2025;26:1812-25

[80] Xu Z,Cao Y.Spatiotemporal graph convolution multifusion network for urban vehicle emission prediction.IEEE Trans Neural Netw Learn Syst2021;32:3342-54

[81] Chen Z,Xu Z,Chen N.SAGE-GSAN: A graph-based method for estimating urban taxi CO emissions using street view images.J Clean Prod2024;474:143543

[82] Alfaseeh L,Farooq B.Greenhouse gas emission prediction on road network using deep sequence learning.Transp Res D Transp Environ2020;88:102593

[83] Tian J,Zhao J,Zhang Z.A data-driven modeling method of virtual synchronous generator based on LSTM neural network.IEEE Trans Ind Inf2024;20:5428-39

[84] Cini A,Bianchi FM.Scalable spatiotemporal graph neural networks.Proc Aaai Conf Artif Intell2023;37:7218-26

[85] Xia Y,Chen X.Future reductions of China’s transport emissions impacted by changing driving behaviour.Nat Sustain2023;6:1228-36

[86] Qiu JD,Qu XM.Carbon emission accounting method for mobile sources in urban road traffic.Urban Transp China2023;21:77-86(in Chinese)

[87] Yu Z,Liu Y.Quantification and management of urban traffic emissions based on individual vehicle data.J Clean Prod2021;328:129386

[88] Wen Y,Zhou Z.A data-driven method of traffic emissions mapping with land use random forest models.Appl Energy2022;305:117916

[89] Zhang S,Huang Y,He K.Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: an empirical study in Ningbo, China.PLoS One2024;19:e0305481 PMCID:PMC11244783

[90] Dong Y,Wang M.Modeling carbon intensity of electric vehicles in the well-to-wheels phase under different traffic flow conditions.iScience2024;27:111070 PMCID:PMC11615250

[91] Peng Z,Easa SM,Wang Y.Analyzing spatiotemporal truck emission pattern using explainable machine learning: a case study in Xi’an, China.Transp Res D Transp Environ2024;137:104489

[92] Zhang 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 Syst2024;25:20715-28

[93] Zhang G,Jin J,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 Prev2024;199:107451

[94] Guo Y,Xiong C,Zhou F.Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach.Transp Res C Emerg Technol2019;98:54-72

[95] Jiang Q,Ma J.Connected automated vehicle impacts in Southern California part-II: VMT, emissions, and equity.Transp Res D Transp Environ2022;109:103381

[96] Chang X,Sun H.A smart predict-then-optimize method for dynamic green bike relocation in the free-floating system.Transp Res C Emerg Technol2023;153:104220

[97] Molloy J,Axhausen KW.Estimating the external costs of travel on GPS tracks.Transp Res D Transp Environ2021;95:102842

[98] Xiao M,Feng H,Long Q.Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: digital twin model.Sustain Energy Technol Assess2024;65:103787

[99] Li Y,Wei YA.Research progress and prospects of carbon emission trading in road transportation.J. Tongji Univ (Nat Sci Ed)2018;46:465-71https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2018&filename=TJDZ201804008 (accessed 2025-06-09). (in Chinese)

[100] Teng W,Yu Y,Yang J.Uncovering the spatiotemporal patterns of traffic-related CO₂ emission and carbon neutrality based on car-hailing trajectory data.J Cleaner Prod2024;467:142925

[101] Sun Y,Li L.Grey correlation analysis of transportation carbon emissions under the background of carbon peak and carbon neutrality.Energies2022;15:3064

[102] Li C.Evaluation and comparative analysis of the mixed traffic flow on urban roads considering the green cost.Sustain Energy Technol Assess2023;57:103292