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However, the effectiveness of these models is often constrained by the availability and quality of input data.
In real-world applications, the collection of fine-grained vehicle operation data can be challenging due to
limited sensor coverage, privacy concerns, or high data acquisition costs. To address this limitation, Sun
[71]
et al. constructed a supervised system based on parallel theory to improve the estimation accuracy of
vehicle CO emissions. This system combines physical models with data-driven models, leveraging the
2
robustness of the former when data are scarce and the high precision of the latter when data is abundant.
Through two real-world case studies, it was verified that this system can effectively enhance the accuracy of
emission estimates, and the physical model can maintain its robustness even when some parameters are
unknown, serving as a complement to the data-driven model.

Table 4 summarizes representative studies that have employed time-series modeling approaches (e.g.,
LSTM, RNN, GRU) for vehicle-level emission estimation, highlighting their model structures, data sources,
and input variables across three major dimensions: dynamic driving behavior, vehicle attributes, and
environmental factors.

Macroscopic models
Macroscopic emission models estimate pollutant emissions using aggregate traffic parameters such as
average speed, traffic density, and average delay rate . These models provide a generalized overview of the
[29]
influence of traffic conditions on emissions, making them suitable for large-scale assessments and policy
analysis.

Statistical regression models
At the road segment level, Grote et al. employed loop detector data to develop traffic emission prediction
[19]
models. They analyzed the correlation between emissions derived from GPS data and traffic parameters
captured by loop detectors. Their study compared the predictive performance of Multiple Linear Regression
(MLR) and Multilayer Perceptron (MLP), achieving emission predictions for 24 types of vehicles.

[77]
At the intersection level, Song et al. proposed an emission model based on delay correction. This model
associates emissions with traffic performance metrics such as average delay time and number of stops. By
establishing baseline emission factors for different intersection types and adjusting them based on
congestion severity, they developed a delay correction model capable of dynamically adjusting emissions.
Comparative evaluations demonstrated the model’s robustness and practical applicability.

Spatiotemporal models
Due to the spatial non-stationarity and temporal variability of vehicle emissions, spatiotemporal models
have emerged to account for the topological structure of road networks and dynamic environmental factors.
These models leverage techniques such as Geographically Weighted Regression (GWR), Graph Neural
Networks (GNN), and GCN to model the complex spatial-temporal relationships in traffic emissions.

Liu et al. utilized large-scale vehicle trajectory datasets to estimate road-level CO emissions and
[78]
2
developed a Geographical Convolutional Neural Network Weighted Regression (GCNNWR) model. This
hybrid model integrates convolutional neural networks to capture nonlinear spatial interactions and
regression components to model emission intensity. Empirical evidence from Beijing demonstrated that the
GCNNWR model significantly outperformed traditional spatial regression models in capturing spatial
heterogeneity, providing valuable insights for urban emission mitigation strategies.