公路隧道照明智能控制技术研究

doi:10.3976/j.issn.1002-4026.2012.04.015

J4 ›› 2012, Vol. 25 ›› Issue (4): 64-68.doi: 10.3976/j.issn.1002-4026.2012.04.015

公路隧道照明智能控制技术研究

罗海星¹，袁振洲¹，况爱武²

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

收稿日期:2012-03-09 出版日期:2012-08-20 发布日期:2012-08-20
作者简介:罗海星（1988-），男，硕士研究生，研究方向为交通运输规划与管理。Email:haix_luo@126.com
基金资助:
国家重点基础研究发展计划（973计划）（2012CB725403）；中央高校基本科研业务费专项资金(T11JB00330)

Research on intelligent control of highway tunnel illumination

LUO Hai-Xing¹, YUAN Zhen-Zhou¹, KUANG Ai-Wu²

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

Received:2012-03-09 Online:2012-08-20 Published:2012-08-20

摘要/Abstract

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

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.

Key words: tunnel illumination, RBF neural network model, tunnel stopping sight distance, intelligent control

中图分类号:

U453.7

罗海星，袁振洲，况爱武. 公路隧道照明智能控制技术研究[J]. J4, 2012, 25(4): 64-68.

LUO Hai-Xing, YUAN Zhen-Zhou, KUANG Ai-Wu. Research on intelligent control of highway tunnel illumination[J]. J4, 2012, 25(4): 64-68.

公路隧道照明智能控制技术研究

Research on intelligent control of highway tunnel illumination

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