Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels

Abstract

Compared with open roadways, traffic safety in highway tunnels requires more attention to build smoothly transitioned and well-coupled light environments for drivers to alleviate visual discomfort so as to achieve a balanced sense of driving safety and comfort. In this study, in order to overcome the drawbacks of existing tunnel lighting control modes that disregard the color temperature of natural light characteristics and collaborative influence of color temperature and luminance of natural light on tunnel lighting quality, one artificial neural network (ANN) model is designed and trained to simulate one physical lighting control system that takes into consideration color temperature and luminance simultaneously. In this model, multiple parameters of discrete and continuous types of input layer and output layer are synergistically analyzed. The model was also trained with quantities of field data from one tunnel in service and includes one hidden layer with 10 neurons. The simulation results showed that this model obtains a high degree of fitness with inside luminance and 100% recognition rate with inside color temperature in the threshold zone, which conforms to the regulation strategy of actual lighting control systems with high confidence. The proposed model will greatly enhance the reliability and sustainability of the lighting system during its normal operation, which can also support other lighting scenarios due to its flexibility and scalability with multiple-input and multiple-output (MIMO) capabilities.