## 2 Literature Review
### Vehicle Emissions Estimation Models  
A number of vehicle emissions estimation models have been introduced by different researchers in the last decades. Using look-up tables, a micro-scale model called CORSIM is built to estimate emissions based on dynamometer data. To ascertain the total emissions of each link, the CORSIM model applies default emission rates per second to each vehicle that travels on the given link, based on acceleration and speed [17]. EMIT is a model for estimating HC, CO\({}_{2}\), CO, and NOx, which is built from dynamometer data of 344 light-duty vehicles and employs a regression equation with acceleration and speed [18]. At the project or regional level, a United States agency has proposed a model called MOVES in 2010 for the estimations of greenhouse gas emissions: CO, VOCs, PM, and NOx generated from light-duty vehicles [19]. Features such as vehicle mass, total resistance force, velocity, acceleration, and driveline performance have been employed by [PERSON] and colleagues to build a model for estimating CO\({}_{2}\) emissions using instantaneous vehicle power [20]. A function of acceleration and velocity observed from a dynamometer experiment has been applied to the INTEGRATION model for the estimation of emissions from measured fuel consumption. Additionally, it is further developed for the simulation and optimization of trip-based microscopic traffic [21]. Using more parameters, including 55 parameters, a model named CMEM is proposed by a group of researchers to estimate parameters for a wide range of light-duty vehicles. For dynamometer testing, this model uses emissions per second data of CO, CO\({}_{2}\), NO, and HC, along with physical vehicle features (engine size, vehicle mass, and aerodynamic drag coefficient) and operating features (acceleration and speed) [22]. Another example of using data-intensive parameters is MEASURE, which was invented by the Georgia Institute of Technology. It calculates the emissions of NO\({}_{x}\), CO, and VOCs from vehicle operating modes, including acceleration, deceleration, cruise, and idling. However, CO\({}_{2}\) estimation is not included in this model, while it has over 30 features as its inputs [23]. Another well-known framework has been developed by the European Environment Agency (EEA) called COPERT, which became one of the standard methodologies for road transport emission inventories in EEA member countries [24]. It estimates primary air pollutants (CO, NOx, PM, VOC, SO\({}_{2}\), NH\({}_{3}\), heavy metals) and greenhouse gas emissions (CO\({}_{2}\), N\({}_{2}\)O, CH\({}_{4}\)) using functions of the mean traveling speed throughout a complete driving cycle [25]. However, the framework neglected other characteristics while estimating the emissions of a specific vehicle, such as engine size, cylinders, and engine model. Furthermore, some recent research authors have applied Machine Learning and Deep Learning methodologies for vehicle emission models. [PERSON] and colleagues, for instance, have proposed a model using the Artificial Neural Network to predict emissions of NOx and CO from heavy-duty vehicles. Though the outcome is positive, CO\({}_{2}\) has also not been included, and the model is appropriate for gasoline vehicles [26]. When testing on the real-world driving conditions of 70 diesel vehicles, a group of researchers implemented a machine learning model to make projections of emissions alongside the performance of vehicles. A look-up table, non-linear regression, and Neural Network Multilayer Perceptron models are consequently applied for instantaneous NOx predictions. Despite the model taking inputs of vehicle acceleration and speed, its outputs focus only on NOx estimation, and CO\({}_{2}\) remains excluded [27]. [PERSON] et al. have built a model for estimating vehicle emission rates, including CO, CO\({}_{2}\), HC, and NOx from vehicle idling by using Portable Emission Measurement System.