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Page 18 of 23 Yu et al. Carbon Footprints 2025, 4, 17 https://dx.doi.org/10.20517/cf.2025.12

[24]
subsequently reduce CO emissions during urban road traffic operations . Traffic demand management
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can achieve coordinated control of network traffic flow through the dynamic coupling of traffic assignment
[92]
models and emission models . In the field of signal control, due to the involvement of specific vehicle
operating characteristics, micro-emission models are utilized, such as the HBEFA emission module built
into Simulation of Urban MObility (SUMO) and the VSP bin classification . In addition, the integration
[93]
[64]
of vehicle-road alignment technologies presents new opportunities for emission reduction. By enabling real-
time communication between vehicles and infrastructure (e.g., traffic lights, roadside units), vehicle-road
alignment can support eco-driving strategies, adaptive signal timing, and intelligent routing, thereby
optimizing vehicle operation and minimizing idling and stop-and-go traffic, which are major contributors
to excess emissions [94,95] . In travel mode guidance, by calculating the CO emissions of urban road traffic as a
2
baseline scenario, the carbon reduction benefits of shared travel, slow traffic, and public transport policies
can be assessed [96,97] .

Interdisciplinary extensions and policy
Beyond technical optimization within traffic systems, emission models are increasingly being applied in
interdisciplinary domains such as smart city planning, carbon trading, and carbon neutrality policy
evaluation. For instance, in the context of smart cities, real-time traffic emission models can be integrated
into urban digital twin platforms to support dynamic traffic control and localized pollution mitigation
strategies . Moreover, emission data can be aligned with carbon accounting frameworks to facilitate the
[98]
[99]
design of urban carbon budgets and regional carbon quota allocation . In the realm of climate policy,
model outputs serve as key inputs for assessing the effectiveness of low-carbon transport strategies and
tracking progress toward carbon neutrality goals [100,101] . Furthermore, emission models promote green
[102]
consumption and advance carbon inclusiveness initiatives by providing transparent and accessible
emission information that encourages sustainable travel behaviors and ensures equitable access to
low-carbon benefits across diverse socioeconomic groups.

REQUIREMENT FOR FURTHER RESEARCH
Based on the development and application trends of different types of urban road traffic CO emission
2
models, future research can focus on the following aspects:

With advancements in real-time monitoring technologies such as on-board terminals, roadside IoT sensors,
and satellite remote sensing, monitoring vehicle operating conditions has become feasible. Fine-tuned
urban governance is expected to be a key development direction. Despite considerable research focused on
real-time vehicle operation conditions to establish micro-emission models based on vehicle modalities,
many models are limited to specific vehicle types and driving environments, making it challenging to
accurately reflect the overall emissions from traffic flow. For instance, trajectory data often primarily include
taxis, trucks, and buses, which may not fully capture the real emissions across the entire road network.
Especially under complex urban conditions, how to leverage operational data from local vehicles, combined
with fleet compositions and traffic flow characteristics, to assess the overall emissions level of traffic flow
and construct a more accurate dynamic emission prediction system represents a key challenge to be
addressed in future research. Advanced modeling approaches, such as Bayesian estimators or hierarchical
LSTM-GNN architectures, could be explored to bridge the gap between individual-level inference and
network-level aggregation.

Data-driven CO spatiotemporal prediction models will be core tools for future urban traffic carbon
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emission research. By integrating multi-source data and mining spatiotemporal correlations,
high-resolution and high-accuracy dynamic emission predictions can be achieved. Urban road traffic is a