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Dynamic luminance tuning method for tunnel lighting based on data mining of real-time traffic flow

机译：基于数据挖掘的隧道照明动态亮度调谐方法

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摘要

Tunnel lighting constitutes one of the major expenses incurred in transportation lighting, and hence substantial research has been conducted to improve the efficiency of lighting and thus to minimize operating costs. This paper investigates an intelligent method for adjusting tunnel lighting with dynamic control based on data mining of traffic flow distribution, traffic composition, and vehicle speed distribution. Field monitoring data of traffic flow in five real expressway tunnels, which are in HeDa expressway, Jilin Province, China, was used in the analysis. The K-MEANS clustering algorithm was used to group (or cluster) the distribution of daily traffic volume into six-time periods, in which the traffic volume includes two peak periods (8:01-11:23 and 14:31-19:01). A dynamic luminance regulation method is proposed that distinguishes operational strategies under different time periods. Furthermore, the impact of tunnel length and traffic flow on the effect of energy-saving and system sustainability of the proposed method was assessed. The results show that when using the proposed method, the energy-savings in tunnel lighting could be between about 50% and 60% for a daily traffic volume between 750 and 2500 vehicles. The results also show that the switching frequency of the lighting system is significantly reduced, which would significantly enhance the sustainability of the lighting system.
机译：隧道照明构成运输照明中产生的主要费用之一，因此进行了大量研究以提高照明效率，从而最大限度地减少运营成本。本文研究了基于交通流量分布，交通构成和车速分布的数据挖掘调节隧道照明的智能方法。在中国吉林省吉林省Heda Expressway的五个真正高速公路隧道中交通流量的现场监测数据在分析中使用。 K-means聚类算法用于将日常交通量分布的分布为六次，其中交通量包括两个高峰时段（8：01-11：23和14：31-19： 01）。提出了一种动态亮度调节方法，以区分不同时间段的操作策略。此外，评估了隧道长度和交通流量对所提出的方法的节能和系统可持续性影响的影响。结果表明，当使用所提出的方法时，隧道照明中的节能可能在750到2500辆之间的日常交通量约50％和60％之间。结果还表明，照明系统的开关频率显着降低，这将显着提高照明系统的可持续性。

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