# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 5 Model Training of Tunnel Lighting ANN  
### Process of Model Training  
#### 5.1.1 Model Simplifying and Parameters Preprocessing  
Based on the description in Table 14 and combined with the regulation strategy of the VCT lighting system, if the outside light sensing data and inside target lighting settings of the actual system are directly fed into the training process of the neural network, this process will present the following characteristics:  
* Six input-layer parameters and two output-layer parameters are distributed in a wide range without uniform units. * Both input- and output-layer parameters are mixed types of discrete and continuous parameters. * Various parameter types and numerical ranges have an unbalanced influence on the link weights of network neurons and often cause the jitter of training process. In order to alleviate this type of imbalance and divergence, it is essential to preprocess the parameters. For a particular tunnel, after the completion of the scheme design and construction, the input parameters affecting the regulation of the lighting system have the following characteristics:  
* Direction is determined, either unidirectional or bidirectional. * Design traffic volume is determined. * Design speed is determined. * The target lighting parameters do not fluctuate obviously under the influence of the three above parameters, but are deeply affected by real-time weather, _OCT_ and O Luminance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Characteristics** \\ \hline O Luminance & Outside tunnel light luminance 1 & Generally noted as \(L_{20}\)[12]. \\  & Outside tunnel light luminance 1 & Continuous parameter. \\  & & Range: 0-18,000 [cd/m\({}^{2}\)]. \\ \hline OCT & Outside tunnel color temperature 1 & Continuous parameter. \\  & & Range: 0-20,000 K. \\ \hline Direction & Driving direction 2 & Discrete parameter. \\  & & Range: Bidirectional, Unidirectional. \\ \hline Weather & Weather condition 2 & Discrete parameter. \\  & & Range: Normal (Sunny, Cloudy). \\  & & Abnormal (Rainy, Snowy, Foggy). \\ \hline  & & Discrete: Several intervals. \\ \cline{3-3}  & & Direction & Volume [veh/(h\(\cdot\)ln)] \\ \cline{3-3}  & & \(\leq\)350, \\ Volume & Tunnel design hourly traffic volume per lane 2 & Unidirectional & 350 \textless{} N \textless{} 1200, \\  & & \(\geq\)1200 \\  & & & \(\leq\)180, \\  & & Bidirectional & 180 \textless{} N \textless{} 650, \\  & & \(\geq\)650 \\ \hline Speed & Tunnel design speed 2 & Discrete parameter \\  & & Range: 40, 60, 80, 100, 120 [km/h] \\ \hline _ICT_ & Inside target lighting & Discrete parameter. \\  & color temperature & Range: 3500 K, 4000 K, 6000 K. \\ \hline I Luminance & Inside target lighting luminance & Continuous parameter. \\  & & Converted by _OCT_ with reduction coefficient. \\ \hline \hline \end{tabular} Note. 1 This parameter aligns with different sample moment. 2 This parameter is static attribute determined by tunnel design or actual operation.  
\end{table} 
Table 14: Parametric characteristics of input and output variables. As such, the input-layer characteristics can be simplified into three parameters. Output parameters need no simplification. In addition, in view of the fact that the input-layer and output-layer parameters contain the mixture of discrete and continuous parameters, the discrete parameters need to be handled via a one-hot or embedding algorithm. According to the regulation strategy, since other lighting zones are converted by referring to the threshold zone, subsequent processing will more or less inherit the parameters of the threshold zone. Therefore, this preprocessing and normalization of the input and output parameters in the lighting network is exemplified with the threshold zone data, as in Table 15.  
After this process, with each group of input and output data from the actual lighting system, data labels should be ready to train the lighting network, and these data will be fed into the below network to execute this training process as shown in Figure 9.