Application of Unmanned Aerial Vehicles to Pedestrian Traffic Monitoring and Management for Shopping Streets

• Main Authors: Chomphunut Sutheerakul, Nopadon Kronprasert, Manop Kaewmoracharoen, Preda Pichayapan
• Format: Conference Proceeding
• Published: 2018
• Subjects: Social Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020167199&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/57934
• Institution: Chiang Mai University

© 2017 The Authors. Published by Elsevier B.V. Collecting data of pedestrian traffic flows is typically complicated or labor-intensive. Using conventional techniques, such as manual observers, on-site video records, and questionnaire surveys, to investigate pedestrian flow characteristics and behavior along a pedestrian shopping street may be restrictive. This study focuses on applying an Unmanned Aerial Vehicle (UAV) - a small aircraft remotely controlled - to monitor pedestrian traffic flows and using data collected from UAVs to manage pedestrian demand and supply. An unmanned aerial vehicle (UAV), also known as a drone, is an innovative technology in various transportation applications. It is capable of carrying a video camera to record high-quality images and real-time videos, and global positioning system (GPS) to transmit spatial and temporal data to the ground. The efforts are made for two thrusts. The first part is to investigate the feasibility of UAV technology to collect data on pedestrian flow characteristics and data on pedestrian supply characteristics. The second part is to evaluate pedestrian service characteristics along shopping streets. The study selected a 2-km shopping street network in Chiang Mai City, Thailand, as a case study. The results showed that UAV can be an alternative viable technology in monitoring pedestrian traffic characteristics in outdoor pedestrian zones. Data collected from UAV technology can be used to develop traffic demand and supply management plans in a more efficient and cost-effective way than conventional data collection techniques.