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Yu et al. Carbon Footprints 2025, 4, 17 Carbon Footprints
DOI: 10.20517/cf.2025.12

Review Open Access

A systematic review of urban road traffic CO
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emission models

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1,2
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Chenxiao Yu , Xiaoguang Yang , Jiantao Mu , Sijin Liu 1
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Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China.
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Urban Mobility Institute, Tongji University, Shanghai 200092, China.
Correspondence to: Prof. Xiaoguang Yang, College of Transportation, Tongji Univ., No. 4800, Caoan Hwy, Jiading District,
Shanghai, China. E-mail: yangxg@tongji.edu.cn
How to cite this article: Yu, C.; Yang, X.; Mu, J.; Liu, S. A systematic review of urban road traffic CO emission models. Carbon
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Footprints 2025, 4, 17. https://dx.doi.org/10.20517/cf.2025.12
Received: 14 Mar 2025 First Decision: 29 Apr 2025 Revised: 27 May 2025 Accepted: 4 Jun 2025 Published: 10 Jun 2025

Academic Editor: Han Hao Copy Editor: Ping Zhang Production Editor: Ping Zhang

Abstract
With rapid urbanization and increasing mobility demand, urban traffic systems face intensifying congestion,
resulting in elevated CO emissions. This paper provides a systematic review of the current status of models
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estimating CO emissions from urban road traffic, considering their applicability across various traffic management
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scenarios. Urban road traffic CO emission models can generally be categorized into two main types. Traditional
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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, CO emission models, data-driven modeling, time-series analysis, spatiotemporal
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modeling, carbon emissions

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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