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公路隧道照明智能控制技术研究
引用本文:罗海星,袁振洲,况爱武.公路隧道照明智能控制技术研究[J].山东科学,2012,25(4):64-68.
作者姓名:罗海星  袁振洲  况爱武
作者单位:1.城市交通复杂系统理论与技术教育部重点实验室,北京交通大学,北京 100044; 2.长沙理工大学交通运输工程学院,湖南 长沙 410114
基金项目:国家重点基础研究发展计划(973计划),中央高校基本科研业务费专项资金
摘    要:为实现隧道内“灯光随车移动”控制技术,在隧道合适位置布设测速线圈,采用RBF神经网络模型预测相邻线圈间的车速。根据隧道特点建立隧道停车视距模型,从而确定了既符合实际又能保证行车安全的灯光长度。最后,给出照明灯智能控制思想。实例分析表明,当交通量低于3 000辆/天时,节能率能达到90%以上。

关 键 词:隧道照明  RBF神经网络模型  隧道停车视距  智能控制  
收稿时间:2012-03-09

Research on intelligent control of highway tunnel illumination
LUO Hai-xing,YUAN Zhen-zhou,KUANG Ai-wu.Research on intelligent control of highway tunnel illumination[J].Shandong Science,2012,25(4):64-68.
Authors:LUO Hai-xing  YUAN Zhen-zhou  KUANG Ai-wu
Institution:1.MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong  University, Beijing 100044, China; 2.School of Traffic and Transportation Engineering, Changsha University of  Science & Technology, Changsha 410004, China
Abstract:We deployed velocity coils at some suitable locations and employed RBF neural network to predict the velocity between two adjacent coils for the implementation of such intelligent control technology as light following vehicle.We also constructed a tunnel characteristic based tunnel stopping sight distance model,which determined a realistic and safe light length.We eventually presented the idea of smart illumination control.The analysis of practical cases shows that energy saving rate is more than 90% when the traffic throughput is lower than 3 000 pcu a day.
Keywords:tunnel illumination  RBF neural network model  tunnel stopping sight distance  intelligent control
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