## 2 Literature Review
### Vehicle Consumption Estimation Models  
On the other hand, some researchers have focused on the fuel consumption of vehicles rather than CO\({}_{2}\) emissions, as fuel consumption (and economic costs) seem to be more relevant to consumers in general. The vehicle fuel consumption models are classified into 2 categories: theoretical fuel consumption models and statistical fuel consumption models [29]. The theoretical fuel consumption model concentrates on the operation features of the vehicle, such as output power and engine parameters, while the statistical fuel consumption model converges the statistical attributes from vehicle activity and fuel consumption data, including acceleration and speed [30]. One of the fuel consumption models is based on a novel macroscopic model that considers trip time and intersection distance for prediction [31]. Using the distribution of Vehicle Specific Power, a fuel consumption prediction model is proposed by [PERSON] et al., which comprises a fuel consumption model and traffic condition predictor to provide a real-time prediction. From this, an API is developed for fuel consumption estimation, using on-board diagnostic (OBD) data for verification, with a 20% forecasting error. By collecting driving behavior data from consumers' smartphones, a prediction model of fuel consumption is developed based on a backpropagation (BP) neural network, random forests, and support vector regression with a relative error of less than 10%. It is also found that the average acceleration and deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are major indicators for fuel consumption estimation [10]. Furthermore, [PERSON] et al. has proposed an approach to estimate fuel consumption by onboard vehicle information system Onboard Diagnoses-II (OBD-II) using Support Vector Machine and Lagrange interpolation. The model successfully provided precise fuel consumption with a square root mean difference of 2.43 [32]. Applying a Machine Learning model, a neural-network-based fuel prediction model is presented by utilizing seven predictors obtained from road grade and vehicle speed. It could optimize fuel usage over the entire fleet, with a peak-to-peak error rate of less than 4% in both city and highway [11]. Furthermore, vehicle emission and consumption can be predicted based on one single model. For example, by using GPS Big Data, an N-Dimensional framework is proposed by a group of researchers for estimating and visualizing fuel consumption and emissions. They stated that analyzing GPS big data generated from vehicles can deliver practical insight on the quantity and distribution of energy use and emissions in real-world driving conditions (acceleration, idle, cruise, and deceleration). This model has claimed effectiveness by a prediction accuracy of 88.6% [8]. Additionally, several statistical models of vehicle emissions and fuel consumption, which are published by [PERSON] et al., could be integrated to predict the spatial and temporal distribution of traffic emissions and fuel consumption [18]. Overall, it can be seen from the mentioned studies that numerous researchers have proposed different models for estimating carbon dioxide emissions and fuel consumption using micro-scale methodologies, or Machine Learning and Deep Learning. The common vehicle characteristics for building these models are engine size, vehicle mass, and aerodynamic drag coefficient; and standard operating features used are acceleration and speed. The research trend generally emphasizes improving estimation models, rather than analyzing different vehicle types and brands using vehicle measurements, making it a limited market analysis for users and manufacturers. As a result, for a better knowledge of the vehicle market and its environmental effects, a comparative view of different types of vehicles and their influence on the environment is significant. Based on these metric analyses, recommended prediction models should be built using selective vehicle features.