# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 3 Data Description
### Dataset  
In this study, three tested Euro 6 light-duty diesel vehicles were selected for data. The tested diesel vehicles collected various types of variables using PEMS devices in 1 s intervals over 6 days. PEMS is a vehicle emission-testing device, which was designed to meet worldwide regulatory standards in a package that can be installed in a wide range of light-duty vehicles. PEMS measures exhaust flow such as the NO\({}_{x}\) emitted from vehicles during field testing on roads. Table 2 lists the information about the tested vehicles utilized in this research. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Driving Portion**} & \multicolumn{5}{c}{**KOR-NIER Route for Cold Test**} & \multicolumn{5}{c}{**KOR-NIER Route for Hot Test**} \\ \cline{2-9}  & **Urban** & **Rural** & **Motorway** & **Total** & **Urban** & **Rural** & **Motorway** & **Total** \\ \hline Trip distance (km) & 32.7 & 29.0 & 28.9 & 90.6 & 26.5 & 19.7 & 27.4 & 73.6 \\ Trip share (\%) & 36.1 & 32.0 & 31.9 & 100.0 & 36.0 & 26.7 & 37.3 & 100.0 \\ Trip duration (s) & 3576 & 1361 & 999 & 5936 & 4308 & 951 & 930 & 6189 \\ Average vehicle speed (km/h) & 32.9 & 76.7 & 104.1 & - & 22.1 & 74.6 & 106.1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of KOR-NIER routes. Figure 2: In this research, KOR-NIER Routes 1 and 2 for the cold test and hot test, respectively, were chosen in Seoul city and its surroundings. The NO\({}_{\rm x}\) emission of Euro 6 diesel vehicles cannot be explained by one segment because the interactive mechanisms of NO\({}_{\rm x}\) formation in the Euro 6 diesel vehicle are more complex than those in the previous diesel vehicle standards. These interactive mechanisms are mutually affected by three segments, including road environmental and atmospheric factors; furthermore, the after-treatment performance can be a critical factor, unlike in the case of the previous standard. The road environment factors include the location (longitude and latitude), road grade (vertical slope) calculated from the altitude (mean of 20.4 m and maximum of 79 m for the cold test and mean of 25.1 m and maximum of 175 m for the hot test in these test routes), and distance, where there were two or three lanes with widths of 3.5 m in the RDE test routes. The collected road data, such as road grade, have considerable effects on the NO\({}_{\rm x}\) emission rate; in particular, a higher road grade leads to a higher NO\({}_{\rm x}\) emission rate while driving [38]. In addition, the altitude influences the NO\({}_{\rm x}\) emission rate [39]. Furthermore, in terms of vehicle speed, the acceleration and deceleration of the vehicle are strongly correlated with NO\({}_{\rm x}\) emissions [40]. All these variables in the road environmental factors can be summarized as concepts of vehicle-specific power (VSP), [41; 42]; the VSP value has been used to determine vehicle emissions [43; 44] and can be defined as follows:  
\[\rm VSP=\frac{A_{r}V+BV^{2}+CV^{3}+MV(a+g\sin\theta)}{M}, \tag{7}\]  
where VSP is the instantaneous power per unit mass of the vehicle (kW/ton), \(a\) is the vehicle acceleration (m/s\({}^{2}\)), \(V\) is the vehicle speed (m/s), \(A_{r}\) is the rolling resistance coefficient (kW - s/m), \(B\) is the speed correction to the rolling resistance coefficient (kW - s\({}^{2}\)/m\({}^{2}\)), \(C\) is the air drag resistance coefficient (kW - s\({}^{3}\)/m\({}^{3}\)), \(M\) is the vehicle mass (ton), \(\theta\) is the road grade (%), and \(g\) is the gravitational constant \(\left(\frac{9.8}{S^{2}}\right)\). Therefore, the VSP method was applied in this study, and the passenger car coefficient values of \(A_{r}\) (0.156461), \(B\) (0.00200193), \(C\) (0.000492646), and \(M\) (1.4788) suggested by the EPA were used. Regarding atmospheric factors, including the NO\({}_{\rm x}\) concentrations, data on relative humidity, ambient temperature, and ambient pressure were collected; these attributes are important variables that strongly affect NO\({}_{\rm x}\) concentrations.