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Page 2 of 23 Yu et al. Carbon Footprints 2025, 4, 17 https://dx.doi.org/10.20517/cf.2025.12

INTRODUCTION
Rapid economic growth and urbanization have significantly intensified mobility demands in cities, leading
to increased energy consumption and mounting environmental concerns. Although modern vehicle
technologies have improved mobility and accessibility, their widespread use has also become a major driver
of greenhouse gas emissions. Urban road traffic is characterized by dense intersections and frequent
stop-and-go conditions, which lead to elevated CO emissions due to increased speed fluctuations and
2
inefficient vehicle operation. In China, the transportation sector contributes approximately 10% of total
[1]
carbon emissions, with road transport responsible for nearly 84% of this share . This high proportion is
largely attributable to the extensive urban road networks and the prevalence of inefficient driving
conditions, making accurate CO quantification and effective mitigation strategies essential for traffic
2
environmental management.

As an open, dynamic, and complex system shaped by the interactions among drivers, vehicles,
infrastructure, and the environment, urban road traffic makes CO emission estimation inherently a
2
complex systems problem . Moreover, these emissions are highly context-sensitive and temporally variable.
[2]
Unlike macro-level emission accounting methods that rely on aggregated indicators such as travel
[3-6]
distance or fuel consumption and often fail to capture traffic dynamics, this study specifically focuses on
urban road traffic CO emission models that incorporate real-world traffic states and variations in vehicle
2
operating behavior.

[7]
Several scholars have conducted reviews of emission modeling approaches. For instance, Zhong et al.
examined traditional vehicle emission models and data-driven prediction models, comparing their data
requirements, computational methods, and predictive accuracy across different application scenarios. Zhou
[8]
et al. focused on fuel consumption models relevant to eco-driving and eco-routing, classifying models into
white-box, gray-box, and black-box categories based on their interpretability. While these reviews provide
valuable technical insight, most are confined to single-vehicle level emissions or specific use cases, such as
eco-driving or laboratory-based assessments, limiting their applicability to complex urban traffic systems.
This leaves a critical gap in understanding how models perform across different spatial scales and under
diverse traffic management regimes, an area that remains underexplored but is vital for developing effective
urban transport decarbonization strategies. Grote , for example, reviewed CO emissions at the network
[9]
2
level, yet the review lacks timeliness and does not incorporate recent developments in trajectory-based
modeling, data fusion, or AI-driven approaches.

In contrast, this study offers a comprehensive and up-to-date review of urban road traffic CO emission
2
models with a distinct focus on their applicability to traffic management. It explicitly addresses the gap
between emission modeling and real-world urban traffic operation needs, an area that has received limited
attention in prior reviews. By systematically categorizing both traditional and data-driven models according
to operational granularity, data dependency, interpretability, and transferability, the study establishes a
comparative framework designed for practical model selection and scenario matching. Furthermore, it
incorporates recent advances in spatiotemporal modeling [e.g., Graph Convolutional Networks (GCN),
Long Short-Term Memory (LSTM)] and discusses emerging challenges in data integration, personalization,
and governance alignment.

Accordingly, the key research questions driving this review are as follows:

1. What are the key factors influencing CO emissions in urban road traffic system?
2