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Table 3. The comparison of MOVES, IVE, CMEM, PHEM and VT-Micro models

Model MOVES IVE CMEM PHEM VT-Micro

Input Vehicle operation mode, vehicle fleet Vehicle operation mode, vehicle fleet Physical parameters (engine capacity, vehicle mass, Speed and gradient profiles of Speed profiles of
composition, vehicle activity, weather composition, vehicle activity, weather maximum power, torque, etc.), instantaneous driving vehicles vehicles
conditions, etc. conditions, etc. pattern

Spatial National, regional, project level National, regional, local, link Vehicle-level Vehicle-level Vehicle-level
scale
Time scale Hour, day, week, month, year Hour, day, week, month, year Real-time or instantaneous Real-time or instantaneous Real-time or
instantaneous
Vehicle Passenger cars, trucks, buses, motorcycles, Bus, truck, small engine, motorcycle Car, truck Passenger cars, light-duty, heavy- Light duty vehicles
type motorhomes duty, buses & coaches, and trucks
motorcycles
Energy Gasoline, diesel, CNG, LPG, electricity, Gasoline, diesel, NG, ethanol, propane, Diesel, gasoline Diesel, gasoline Gasoline, diesel
type ethanol (E85) CNG and LPG
Road type Rural, urban / / / /
Driving Different counties Developing countries FTP, US06, MEC01 European countries FTP
cycle
Version MOVES 5.0.0, 2024 IVE 2007 CMEM 3.0, 2005 Continuously updated. Unavailable VT-Micro 2.0,
2004
Reference [40] [41] [42] [43] [44]

MOVES: Motor vehicle emission simulator; IVE: international vehicle emissions; CMEM: comprehensive modal emission model; PHEM: passenger car and heavy-duty emission model; VT-Micro: virginia tech
microscopic energy and emission model; FTP: federal test procedure; CNG: compressed natural gas; LPG: liquefied petroleum gas; NG: natural gas.

researchers have increasingly focused on the problem of trajectory reconstruction for emission calculations [52,53] . Ma et al. proposed a trajectory
[54]
reconstruction method based on interpolation of acceleration distributions, demonstrating that the reconstructed trajectory closely approximates the real
trajectory, achieving 2% to 17% higher accuracy compared to other methods. Shang et al. developed a traffic energy consumption model that integrates
[55]
macroscopic and microscopic data, and reconstructed trajectories using a nonparametric kernel smoothing algorithm combined with variational theory. For
data with varying sampling frequencies, this approach significantly improved the accuracy of emission estimates based on reconstructed trajectories.

Accuracy
Macroscopic and mesoscopic emission models rely on aggregated data (such as regional average speed and fleet age distribution), which obscure individual
driving heterogeneity. This simplification can lead to the smoothing of emissions in localized hotspots, such as intersections, inherently limiting the accuracy

of these models. Smit et al. found that based on data from portable emission measurement system (PEMS) for five SUVs, the emission factors under actual
[56]
urban hot operating conditions were seven times higher for nitrogen oxides and four times higher for nitrogen dioxide than those estimated by the COPERT
model in Australia.