Department of Physics, School of Science, Key Laboratory of Education Ministry on Luminescence and Optical Information Technology, National Physical Experiment Teaching Demonstration Center, Beijing Jiaotong University, Beijing, China
Xiaoting Zhao
Department of Physics, School of Science, Key Laboratory of Education Ministry on Luminescence and Optical Information Technology, National Physical Experiment Teaching Demonstration Center, Beijing Jiaotong University, Beijing, China
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, China
Xiaodong Zhang
Beijing Municipal Institute of City Planning and Design (BICP), Beijing, China
Liang Wang
Beijing Municipal Institute of City Planning and Design (BICP), Beijing, China
Xinghua Zhang
Department of Physics, School of Science, Key Laboratory of Education Ministry on Luminescence and Optical Information Technology, National Physical Experiment Teaching Demonstration Center, Beijing Jiaotong University, Beijing, China
Yisheng Lv
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Fanchao Meng
Department of Physics, School of Science, Key Laboratory of Education Ministry on Luminescence and Optical Information Technology, National Physical Experiment Teaching Demonstration Center, Beijing Jiaotong University, Beijing, China
We propose a novel fiber-optic auditory nerve of ground (FANG) in the suburb based on the fiber-optic distributed vibration sensor (DVS). The feasibility and effectiveness of the principle prototype FANG for traffic flow monitoring are proved and investigated by the field experiment. One of the 31.8 km-long redundant optical fiber of the buried optical-fiber cable for data transmission is utilized as the sensing fiber. Then, the phase-sensitive optical time-domain reflectometer (φ-OTDR) based DVS is realized and regarded as the FANG. The vibration events at 9 observation points with different ground conditions along the sensing fiber are detected by a threshold algorithm during 6.5 hours from 8:00 am. Then, the vibration events are analyzed in combination with the ground conditions to recognize the machine working in the factory, rammer working and the vehicles passed through near different areas and roads. The traffic flow is estimated by the vibration-counting with a counting error that is believed to be in an acceptable range. The distribution and the fluctuation trends of the estimated traffic flow are useful and enlightening for the traffic monitoring and pre-warning of special events, such as an accident. The accuracy can be improved by artificial intelligence methods in the future. It seems that our proposed FANG can be a potential and effective tool for the internet of things, smart ground and smart traffic in the suburb where the video and other information collection methods are not available.
