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A Model for Calculating the Spatial Coverage of Audible Disaster Warnings Using GTFS Realtime Data

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  • Akihiko Nishino

    (Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Kanagawa, Japan)

  • Akira Kodaka

    (Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Kanagawa, Japan)

  • Madoka Nakajima

    (Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Kanagawa, Japan)

  • Naohiko Kohtake

    (Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Kanagawa, Japan)

Abstract

In the event of a large-scale disaster, the dissemination of audible disaster warning information via sirens is effective in ensuring a rapid response. Sirens can be installed not only on fixed towers, but also on public transport and other vehicles passing through residential areas, and at spots where residents congregate, to increase area coverage. Although models to calculate the spatial coverage of audible information delivered from fixed sirens have been constructed, no general-purpose model has been developed to assess the delivery from vehicles. In this study, we focused on the General Transit Feed Specification (GTFS), which is an open format for geospatial information on public transport. We conducted a spatial analysis using a geographic information system (GIS) on the basis of the acquired bus location information. We developed a model to calculate the spatial coverage of the audible information delivery for overlapping hazard maps and population. Assuming a flood occurred in the vicinity of Brisbane Central Station, Queensland, Australia, we confirmed that the developed model was capable of characterizing the time-series changes in the exposed population in the target area. Since the GTFS format is currently distributed across various countries, this assessment model is considered to be highly versatile and widely applicable.

Suggested Citation

  • Akihiko Nishino & Akira Kodaka & Madoka Nakajima & Naohiko Kohtake, 2021. "A Model for Calculating the Spatial Coverage of Audible Disaster Warnings Using GTFS Realtime Data," Sustainability, MDPI, vol. 13(23), pages 1-10, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13471-:d:695890
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    References listed on IDEAS

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    2. Sagar Ratna Bajracharya & Narendra Raj Khanal & Pashupati Nepal & Sundar Kumar Rai & Pawan Kumar Ghimire & Neera Shrestha Pradhan, 2021. "Community Assessment of Flood Risks and Early Warning System in Ratu Watershed, Koshi Basin, Nepal," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    3. Vieri Tarchiani & Giovanni Massazza & Maurizio Rosso & Maurizio Tiepolo & Alessandro Pezzoli & Mohamed Housseini Ibrahim & Gaptia Lawan Katiellou & Paolo Tamagnone & Tiziana De Filippis & Leandro Rocc, 2020. "Community and Impact Based Early Warning System for Flood Risk Preparedness: The Experience of the Sirba River in Niger," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    4. Jinjoo Bok & Youngsang Kwon, 2016. "Comparable Measures of Accessibility to Public Transport Using the General Transit Feed Specification," Sustainability, MDPI, vol. 8(3), pages 1-13, March.
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    1. Haraguchi, Masahiko & Funahashi, Tomomi & Biljecki, Filip, 2024. "Assessing governance implications of city digital twin technology: A maturity model approach," Technological Forecasting and Social Change, Elsevier, vol. 204(C).

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