# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**UNEC Regulations**} & \multicolumn{4}{c}{**Limits of Motor Vehicle During Acceleration (dB(A))**} \\ \hline \multirow{2}{*}{Categories of vehicles} & Regulation No. & R9/00 & R51/00 & R51/01 & R51/02 & R51/03I & R51/03 II \\  & Year & 1969 & 1982 & 1988 & 2005 & 2016 & 2020 & 2024 \\  & Revision No. & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline \multirow{3}{*}{M1} & PMR \(\leq\) 120 & 82 & 80 & 77 & 74 & 72 & 70 & 68 \\  & 120 \(<\) PMR \(\leq\) 160 & 82 & 80 & 77 & 74 & 73 & 71 & 69 \\  & PMR \(>\) 160 & 82 & 80 & 77 & 74 & 75 & 73 & 71 \\ \hline \multirow{3}{*}{M2} & M \(\leq\) 2.5 t & 84 & 81 & 77 & 74 & 72 & 70 & 69 \\  & 2.5 t \(<\) M \(\leq\) 3.5 t & 84 & 81 & 77 & 74 & 74 & 72 & 71 \\  & M \(>\) 3.5 t \(\text{P}_{n}\)\(\leq\) 135 kW & 89 & 82 & 80 & 78 & 75 & 73 & 72 \\  & M \(>\) 3.5 t \(\text{P}_{n}\)\(>\) 135 kW & 91 & 85 & 83 & 80 & 75 & 74 & 72 \\ \hline \multirow{3}{*}{M3} & P\({}_{n}\) \(\leq\) 150 kW & 89 & 82 & 80 & 78 & 76 & 74 & 73 \\  & 150 kW \(<\) P\({}_{n}\)\(\leq\) 250 kW & 91 & 85 & 83 & 80 & 78 & 77 & 76 \\  & P\({}_{n}\)\(>\) 250 kW & 91 & 85 & 83 & 80 & 80 & 78 & 77 \\ \hline \multirow{3}{*}{N1} & M \(\leq\) 2.5 t & 84 & 81 & 78 & 76 & 72 & 71 & 69 \\  & M \(>\) 2.5 t & 84 & 81 & 79 & 77 & 74 & 73 & 71 \\ \hline \multirow{3}{*}{N2} & P\({}_{n}\) \(\leq\)135 kW & 89 & 86 & 83 & 78 & 77 & 75 & 74 \\  & P\({}_{n}\)\(>\) 135 kW & 89 & 86 & 84 & 80 & 78 & 76 & 75 \\ \hline \multirow{3}{*}{N3} & P\({}_{n}\) \(\leq\) 150 kW & 89 & 86 & 83 & 78 & 79 & 77 & 76 \\  & 150 kW \(<\) P\({}_{n}\)\(\leq\) 250 kW & 92 & 88 & 84 & 80 & 81 & 79 & 77 \\ \cline{1-1}  & P\({}_{n}\)\(>\) 250 kW & 92 & 88 & 84 & 80 & 82 & 81 & 79 \\ \hline \hline \end{tabular} Note: PMR, power-to-mass ratio; M, Mass; P\({}_{n}\), Power; M, Vehicles having at least four wheels and used for the carriage of passengers (e.g., standard car with 2, 3, 4 doors). M1: Vehicles used for the carriage of passengers, comprising not more than eight seats in addition to the driver’s seat = 9. (Larger Than Standard Car, e.g., London Cab/E7 Type Vehicle 8 seat + Driver seat). M2: Vehicles used for the carriage of passengers, comprising more than eight seats in addition to the driver’s seat, and having a maximum mass not exceeding 5 tons. (Bus). M3: Vehicles used for the carriage of passengers, comprising more than eight seats for the carriage of passengers, comprising more than eight seats in addition to the driver’s seat, and having a maximum mass exceeding 5 tons. (Bus). N Power-driven vehicles that have at least four wheels and used for the carriage of goods. N1: Vehicles used for the carriage of goods and having a maximum mass not exceeding 3.5 tons. (Pick-up Truck, Van). N2: Vehicles used for the carriage of goods and having a maximum mass exceeding 3.5 tons, but not exceeding 12 tons. (Commercial Truck). N3: Vehicles used for the carriage of goods and having a maximum mass exceeding 12 tons. (Commercial Truck).  
\end{table} 
Table 1: Limits of motor vehicle noises during acceleration regarding noise emissions in United Nations Economic Commission for Europe (UNEC) regulations. [PERSON], et al. [17] used ANN for modelling traffic noise which generates around high-rise building along the urban expressway. In their research, multi-layer perception (MLP) was used to train the network, and they found the minimum number of hidden layers is 3 by using the trial-and-error method. [PERSON] and [PERSON] [18] used ANN to conduct annoyance evaluations of electric vehicle noise generated by single-car pass-by situation with a constant speed. The proposed ANN models were able to save time, money and resources comparing with linear regression models based on psycho-acoustic parameter. Previous research mainly covered vehicle noise prediction, simulation, and co-relation between noise-level and pedestrian safety for design improvement and noise control.