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Author: search.filters.author.Sonehara, Noboru

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    Comparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors
    (2024-01-01)
    Traganmaturapot, Peerada
    ;
    Sonehara, Noboru
    ;
    Hiruma, Nobuharu
    ;
    Cooharojananone, Nagul
    ;
    Kodate, Akihisa
    Recent 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.
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    Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics
    (2023-01-01)
    Traganmaturapot, Peerada
    ;
    Sonehara, Noboru
    ;
    Hiruma, Nobuharu
    ;
    Cooharojananone, Nagul
    ;
    Jirapongwanich, Jirakit
    Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management.
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    Determinants of Personal Health Information Disclosure: A Case of Mobile Application
    (2018-01-01)
    Atchariyachanvanich, Kanokwan
    ;
    Mitinunwong, Nichaporn
    ;
    Tamthong, Butsaraporn
    ;
    Sonehara, Noboru
    This study explored the factors that affect personal health information (PHI) disclosure via a mobile application (app) in Thailand. Since mobile apps are increasingly popular, as is the Thai people's concern on their health condition, many mobile app service providers want to know which factors would persuade customers to reveal their PHI via mobile apps. This research model was, therefore, developed and included the six factors of: personalized service, self-presentation, mobile app reputation, familiarity, perceived benefits and privacy concerns. The hypotheses were tested by structural equation modeling using the questionnaire responses from 294 valid subjects. Surprisingly, privacy concern was not significantly negatively related to the intention to disclose PHI. However, the significance effect of the perceived benefit, personalized service and self-presentation were consistent with previous studies. In addition, the respondents were willing to reveal different personal information in different situations. The implication of the result will shed light on the development of a healthcare mobile app service provider.