International Journal of Control and Automation Vol. 11, No. 5 (2018), pp.129-142 http//dx.doi.org/10.14257/ijca.2018.11.5.12 ISSN: 2005-4297 IJCA Copyright © 2018 SERSC Australia A Study on the Analysis Method of Passenger Flow in Airport Using Laser Sensor 1 Seok-Hee Lee 1 , Hyung-Taek Lee 2 , Nhu-Quynh Phan 3 and Gwang-Yong Gim 4* 1 04143 Solution & Service Business Division, LG Hitachi., LG Mapo Bldg 155, Mapo-daero, Seoul, Republic of Korea 2 08380 #301, Ace Techno Tower 5, 20, Digital-ro 31-gil, Guro-gu, Seoul, Republic of Korea 3 06978 Dept. Business Administration, Graduate School Soongsil Univ., 369 Sangdo-ro, Dongjak-gu, Seoul, Republic of Korea 4 06978 Dept. Business Administration, Soongsil Univ., 369 Sangdo-ro, Dongjak-gu, Seoul, Republic of Korea 1 chris@lghitachi.cokr, 2 htlee@innotium.com, 3 phannhuquynh@hotmail.com, 4* gygim@ssu.ac.kr Abstract There are wide and recent research efforts to extract location information of people in certain spaces, with new services using this information in real space being launched in the market. In particular, there are emerging services and solutions that measure movement flow of people in airports, public facilities, commercial facilities, factories, warehouses, and other locations to mitigate congestion or enhance security and safety in these spaces. Methods of measuring the movement flow of people include camera image analysis, Wi-Fi, beacon, and RFID, but these methods are inconvenient as the tracking targets must carry a certain medium (smartphone, RFID tag, etc.), while camera images infringe upon personal privacy, posing many problems for commercialization of these movement flow technologies. In order to solve this problem, the current study applies a movement flow measurement technology using laser sensors, applying it to a Korean airport to verify its feasibility through an experiment. The researchers propose a method to improve customer service by analyzing the passenger movement flow, congestion levels, and wait time at the airport using the acquired data. Keywords: Flow Tracking Analysis, Big Data, Laser Sensor, Visualization, Congestion 1. Introduction 1.1. Research Background As the importance of key technologies including artificial intelligence (AI), fin- tech, big data, and internet of things (IoT) is increasing with the advent of the Fourth Industrial Revolution and the prevalence of IoT rises, the use of vast amount of data collected and accumulated via cable and wireless networks is increasing in tandem. In the field of IoT, sensors are increasingly embedded in various devices, effectively establishing the infrastructure of sensor networks that collect a diverse range of data. In more cases, companies and organizations are measuring the Received (December 17, 2017), Review Result (March 10, 2018), Accepted (March 19, 2018) * Corresponding Author