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Microscopic emission models, by contrast, capture individual driving behavior and thus offer the potential
for higher accuracy than macroscopic and mesoscopic models, providing a more detailed representation of
real-world vehicle emissions. However, the accuracy of these models is largely dependent on the precision
of input parameters. The actual results may be constrained by sensor noise and the high costs of data
cleaning, often resulting in effective accuracy that falls below laboratory calibration values .
[57]
Moreover, emission models are frequently integrated with traffic simulation models. Existing research
reveals inconsistencies in whether microscopic traffic simulation models can reliably reproduce real-world
pollutant emission characteristics. One notable issue is the discrepancy between the VSP distributions
generated by traffic simulators and those observed in reality . Lejri et al. found that COPERT may
[59]
[58]
overestimate NOx emissions during smooth traffic flow and underestimate them during congestion.
Although PHEM performs well in terms of accuracy, its lack of detailed information on the actual fleet
composition may introduce certain biases. The study noted that when using the PHEM model, the absolute
global relative errors for fuel consumption and NOx emissions could reach 5.0% and 9.2%, respectively.
Gräbe et al. coupled MATSim with HBEFA to compare simulated emissions with PEM-based
[60]
measurements. Their results showed that the greenhouse gas CO emissions from light and heavy vehicles
2
over a 61.7-kilometer route were 77% and 57% of the measured values, respectively, which may be related to
errors in road classification and the HBEFA model’s failure to account for road gradient effects.

Complexity
Microscopic emission models capture the state and behavior of individual vehicles, requiring highly detailed
input data and substantial computational processing. As a result, these models can be computationally
intensive and time-consuming when applied to large-scale traffic networks. For instance, coupling the
microscopic traffic simulator VISSIM with the PHEM emission model involves extensive calibration and
detailed speed profiles . In contrast, mission models based on average speed or aggregated traffic
[61]
conditions enable much faster processing, making them more suitable for large-scale or real-time
applications.

Transparency
Transparency refers to the extent to which a model’s internal mechanisms, including its structure,
parameters, and training process, and whether these elements can be accessible and comprehensible to
humans. Zhou et al. categorize the emission models reviewed in this study as black-box models, which are
[8]
data-driven and expressed through mathematical relationships. According to this framework, traditional
emission models can be classified into physical models (e.g., CMEM, PHEM), hybrid models (e.g., MOVES,
COPERT, HBEFA), and data-driven models (VT-Micro).

Transferability
Vehicle emission characteristics vary significantly across regions, heavily influenced by local traffic
conditions, driving behaviors, vehicle emission levels, and fuel quality. Emission models are typically
developed based on the local driving cycles, such as the U.S. Federal Test Procedure (FTP), and rely on
extensive experimental or empirical datasets. However, many countries and regions have yet to establish
comprehensive and standardized motor vehicle emission factor databases. This situation results in
inconsistent methodologies and fragmented data sources. Although some researchers have attempted
[62]
localized calibration of these emission factor models to enhance their applicability and accuracy ,
challenges persist regarding their precision and practical usability in real-world applications.