Driving in tunnel areas depends more heavily on light conditions than that on open roadways. Traditional lighting systems in highway tunnels adjust lighting parameters only caring about outside light luminance, and focus is usually on energy conservation; however, little concern is about drivers’ actual physical and psychological needs. How to leverage the enormous research progress of traffic safety, light environment, human factors engineering, and modern lighting sources to create an ideal tunnel light environment that aids with ensuring driving safety and lower interference effects caused by the change of light environment will greatly improve safety level and reduce adverse influence on drivers’ visual health in a tunnel area. An intelligent lighting control system designed with multiple influence factors are systematically considered. Based on sensor data from outside natural light conditions, target lighting parameters are determined per each lighting zone requires; then, lighting commands will be transferred and parsed by adaptive lighting controllers and modules, eventually LED lighting properties are altered step by step. This system helps a lot with optimizing tunnel lighting quality and improving drivers’ visual performance; as a result, it contributes to lower the fluctuation of drivers’ workload and get a smooth traffic flow, and ultimately this technically ensures physical and mental health of drivers in a tunnel area.

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.