Comparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors

Simple item page
dc.contributor.authorTraganmaturapot, Peerada
dc.contributor.authorSonehara, Noboru
dc.contributor.authorHiruma, Nobuharu
dc.contributor.authorCooharojananone, Nagul
dc.contributor.authorKodate, Akihisa
dc.contributor.authorAtchariyachanvanich, Kanokwan
dc.date.accessioned2026-08-06T10:43:21Z
dc.date.available2026-08-06T10:43:21Z
dc.date.issued2024-01-01
dc.description.abstractRecent observations indicate an increase in the frequency of crowd crush incidents, highlighting the urgent need for effective mitigation strategies. Addressing this issue necessitates a comprehensive understanding of the factors influencing visitor decision-making to prevent adverse outcomes such as crowd crushes. This study introduces a Streamlit dashboard designed to visualize foot traffic data in the Sendagaya area and integrates contextual data from 10 key factors, including events and locations, points of interest (POIs), periods of time, online search activity, height of buildings, temperature and weather conditions, currency exchange rates, earthquakes, number of international flight arrivals, and hotel room rates. This integration facilitates comparative analysis of foot traffic patterns during standard periods versus periods coinciding with significant events, such as the 2023 Jingu Gaien Fireworks Festival, to assess their impact on congestion levels. Conducted exclusively in Sendagaya, the study utilized 8 Wi-Fi sensors throughout August 2023, encompassing 3 key stages: data collection, preprocessing, and dashboard development. The analysis revealed significant determinants-including events, points of interest, time periods, and online activity-that influence visitor foot traffic, while other factors exhibited no discernible impact. These findings have important implications for enhancing decision-making processes, preparedness measures, risk management strategies, and data-driven policymaking for sustainable tourism development.
dc.identifier.citation2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems Scis and Isis 2024, 2024
dc.identifier.doi10.1109/SCISISIS61014.2024.10760113
dc.identifier.other2-s2.0-85214705397
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14917
dc.source2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems Scis and Isis 2024
dc.subjectbusiness intelligence
dc.subjectdata analysis
dc.subjectdata visualization
dc.subjectdata-driven
dc.subjectfoot traffic
dc.subjectloT
dc.subjectpeople flow
dc.subjecttransportation
dc.subjectWi-Fi sensors
dc.titleComparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors
dc.typeConference Paper

Files

Collections

Research