An Intelligent Luminance Control Method for Tunnel Lighting Based on Traffic Volume
Li Qin,
Li-Li Dong,
Wen-Hai Xu,
Li-Dong Zhang and
Arturo S. Leon
Additional contact information
Li Qin: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Li-Li Dong: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Wen-Hai Xu: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Li-Dong Zhang: High Grade Highway Construction Authority of Jilin Province, Jilin 130012, China
Arturo S. Leon: Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204, USA
Sustainability, 2017, vol. 9, issue 12, 1-12
Abstract:
This paper presents an intelligent control method for tunnel lighting based on traffic volume. The monitoring data for a period of 12 days of the Chibai tunnel (located in the Jilin province of China) under different weather conditions was selected as the case study. The data used in the analysis included traffic volume, vehicle speed, the time of light-emitting diodes (LEDs) operating at their lowest luminance level, and the average time interval between two consecutive vehicles. The traffic flow analysis indicated that the tunnel has a relatively heavy traffic volume in the daytime (7:00 a.m. to 6:00 p.m.) and a relatively low traffic volume in the nighttime (12:00 a.m. to 6:00 a.m. and 7:00 p.m. to 12:00 a.m.). Thus, we propose a tunnel lighting control method that distinguishes day and night operational strategies. In the daytime, the luminance of tunnel zones depends on tunnel exterior luminance, traffic volume and vehicle speed regardless of vehicle presence. In the night, the “vehicle in, light brightens; vehicle out, light darkens” control method is adopted for the tunnel luminance, which depends on vehicle presence.
Keywords: intelligent control; LED; traffic flow; tunnel lighting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:9:y:2017:i:12:p:2208-:d:120954
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