摘要
为解决动态交通路况下公路隧道实际路面亮度不随车流量自适应变化以及由此引起的行车安全与照明能耗问题,提出一种基于车流量预测的隧道中间段动态照明控制方法。首先,以茶条岭隧道某一时段实测的车流量数据为基础,通过数据预处理获得训练预测模型的数据样本;其次,考虑数据样本的时序性特点,建立基于长短时记忆神经网络(LSTM)的车流量预测模型,并采用灰狼算法对LSTM网络的超参数进行优化,提高网络的预测精度;然后,通过对隧道中间段的行车速度以及交通流量等关键要素进行参数分析,发现交通流量与道面亮度之间存在强相关关系,基于这一规律配置灯具亮度分级调节模型,结合LSTM车流量预测模型,在隧道内部实现超前调节以使得内部照明能及时且有效地应对交通态势变化;最后,通过试验验证所构建的车流量预测模型和亮度调控方法的有效性。结果表明,所提出的方法能够在保证隧道照明安全性的前提下兼顾节能性。
In order to solve the problem where the actual pavement brightness in highway tunnel does not automatically adapt to the traffic flow in dynamic traffic conditions and the traffic safety problem and lighting energy consumption problem arising from that problem,a dynamic lighting control method for middle section of tunnel has been devised based on traffic flow prediction.First,the traffic flow data measured at the Chatiaoling Tunnel in a time period is obtained and then preprocessed to generate the data sample required for training of the prediction model.Second,given the time sequence of the data sample,the traffic flow prediction model is devised based on the long short-term memory(LSTM)neural network,and the hyper-parameters of the LSTM network are optimized by using the grey wolf algorithm,so as to enhance the prediction accuracy of the network.Third,the parametric analysis of the key factors such as driving speed and traffic flow in the middle section of tunnel indicates that there is a strong correlation between traffic flow and pavement brightness.Based on that correlation,the lamp brightness regulation model is created.By using the LSTM traffic flow prediction model,advanced regulation inside the tunnel is achieved,so that the internal lighting can respond to the traffic conditions in a timely and effective manner.At last,tests are conducted to verify the effectiveness of the traffic flow prediction model and brightness regulation method that have been devised.As the results indicate,the method devised herein will guarantee adequate lighting in tunnel and also help with energy conservation.
作者
段中兴
杜婉欣
DUAN Zhongxing;DU Wanxin(College of Information and Control Engineering,Xi′an University of Architecture and Technology,Xi′an 710399)
出处
《现代隧道技术》
CSCD
北大核心
2024年第3期157-165,共9页
Modern Tunnelling Technology
基金
国家重点研发计划课题(2022YFC3203605)。
关键词
交通流预测
LSTM网络
灰狼算法
隧道照明
路面亮度
Traffic flow prediction
LSTM network
Grey wolf algorithm
Tunnel lighting
Pavement brightness
作者简介
段中兴(1969-),男,博士,教授,主要从事智能系统与智能信息处理、智能检测与机器视觉、建筑环境控制与节能优化等方面研究,E-mail:zhx_duan@163.com.
