## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modeling** & **Classical** & **Applicable Scenarios** & **Core Concept** \\ \hline \multirow{8}{*}{Based on Average Velocity} & MOBILE & & Dividing vehicle types into 28 \\  & Model & & categories to predict future vehicular \\  & & Country, Regional, and Macro-Level & emission factors. \\  & COERT & Energy Consumption and & estimating various vehicular pollutants \\  & Model & Emission Calculations & such as \(NO_{x}\) and \(SO_{2}\) based on \\  & EMFAC & & regional sales statistics of \\  & Model & & automotive fuels. \\ \hline \multirow{8}{*}{Based on Driving Conditions} & MOVES & & Establishing the relationship between \\  & Model & & vehicular emissions and \\  & & Model & second-by-second vehicle operating \\  & & Micro/Meso-Level Energy & conditions based on Vehicle-Specific \\  & & Consumption and Emission Analysis & Power (VSP) \\  & IVE & Applicable to Road Segments, & Using the ES-VSP to categorize \\  & Model & Intersections, etc. & vehicular operating conditions \\  & & & into 60 types \\  & CMEM & & Segmenting vehicular emission data \\  & Model & & based on extensive testing under \\  & & & various operating conditions \\ \hline \multirow{2}{*}{/} & \multirow{2}{*}{V-T micro} & Fuel Consumption and Traffic & The primary traffic parameters are \\  & & Emissions Analysis for & vehicle speed and acceleration \\ \cline{1-1}  & & Individual Vehicles & Utilizing a second-order relationship \\ \hline \multirow{2}{*}{/} & METANET & & \\ \cline{1-1}  & Model & Mesoscopic-Level & model between traffic flow parameters \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Emission Models [18; 19; 20]. In terms of vehicle platooning, compared to individual vehicle travel, traveling in a platoon formation reduces the spacing between vehicles and changes their aerodynamic characteristics, thus improving energy efficiency. Studies have shown that the aerodynamic drag of vehicles in a platoon decreases, resulting in reduced energy consumption and lower carbon dioxide emissions [26; 27]. A review of the existing literature shows that although the theories and methods of traffic flow operation simulation and carbon emission characteristic analysis have been completed through the joint efforts of domestic and foreign scholars, the relevant theoretical results have become increasingly complete. The MOBILE and COBERT models do not consider the impact of actual road operating conditions and driving behavior on the energy consumption and emission of motor vehicles, although the MOVES and IVE models developed based on the VSP principle consider the road operating conditions. The parameters of road environment, vehicle classification, and fuel level are mainly based on the data of European and American countries, and there is a lack of relevant research after the introduction of intelligent vehicles into highways. The main purpose of this study was to reveal the carbon emission characteristics of mixed vehicles with different levels of driving automation in an off-ramp impact area. Specifically, the study included the following three tasks: (1) constructing a carbon emission model for traffic flow based on scenario analysis, (2) investigating the carbon emission characteristics of traffic flow under a mixed-vehicle scenario, and (3) investigating the carbon emission characteristics of traffic flow under a vehicle queue driving scenario. The results of this study help understand the carbon emission characteristics of heterogeneous traffic flows in the influence zone of freeway off-ramps. This paper is divided into four sections. Section 2 introduces the data acquisition and simulation experimental scenarios, and it presents the carbon emission models for traffic flow under various scenarios. In Section 3, numerical simulations and analyses are discussed to analyze the carbon emission characteristics under intelligent vehicle mixing and vehicle queue-travel scenarios. Finally, Section 4 presents the conclusions of the study. See Figure 1.