Guangdong University of Technology School of Civil and Transportation Engineering, Guangzhou 511400, PR China.
China Merchants Chongqing Communications Technology Research and Design Institute Co Ltd, Chongqing 400000, PR China.
Accid Anal Prev. 2023 Nov;192:107237. doi: 10.1016/j.aap.2023.107237. Epub 2023 Aug 4.
The service states of tunnel lighting will directly affect the lighting conditions, which affect traffic safety. Therefore, it is imperative to evaluate and predict traffic safety accurately in different lighting states. In this research, three hundred experimental scenarios of the service states of tunnel lighting were designed and implemented to evaluate the impact of different service states of tunnel lighting on traffic safety. The evaluation was achieved through a visual identification experiment in a physical tunnel. The experimental results show higher simulated vehicle speeds pose a greater threat to traffic safety. The severity of lighting attenuation contributes to an increased risk to traffic safety. An increase in the number of luminaires failure also poses a greater threat to traffic safety. The newly proposed traffic safety factor was employed to evaluate traffic safety quantitatively in road tunnels. To improve the accuracy and comprehensiveness of the traffic safety factor prediction in different lighting service states, an advanced neural network prediction system was developed. The prediction system was constructed using the Sparrow Search Algorithm (SSA) to optimize Extreme Learning Machine (ELM) neural network, and the dataset from the experiment was used for the prediction model. The SSA-ELM neural network model is a reliable model that can predict the traffic safety factor comprehensively and accurately. The recommended threshold value for the traffic safety factor is 0.6. When the value falls below 0.6, it shows that the service states of tunnel lighting pose a threat to traffic safety in the tunnel. These findings can provide insights into the safe and energy-efficient maintenance of road tunnels.
隧道照明的服务状态将直接影响照明条件，从而影响交通安全。因此，准确评估和预测不同照明状态下的交通安全势在必行。本研究设计并实施了 300 种隧道照明服务状态的实验场景，以评估不同的隧道照明服务状态对交通安全的影响。通过在物理隧道中的视觉识别实验进行评估。实验结果表明，较高的模拟车速对交通安全构成更大的威胁。照明衰减的严重程度会增加交通安全风险。灯具故障数量的增加也会对交通安全构成更大的威胁。新提出的交通安全系数用于定量评估道路隧道中的交通安全。为了提高不同照明服务状态下交通安全系数预测的准确性和全面性，开发了先进的神经网络预测系统。预测系统使用麻雀搜索算法（SSA）对极限学习机（ELM）神经网络进行优化，并使用实验数据集进行预测模型。SSA-ELM 神经网络模型是一种可靠的模型，可以全面准确地预测交通安全系数。建议的交通安全系数阈值为 0.6。当该值低于 0.6 时，表示隧道中的照明服务状态对交通安全构成威胁。这些发现可以为道路隧道的安全和节能维护提供参考。
