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

Akihiko Nishino, Akira Kodaka, Madoka Nakajima and Naohiko Kohtake
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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

Sustainability, 2021, vol. 13, issue 23, 1-10

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.

Keywords: disaster information; warning dissemination; spatial coverage rate; GTFS; GIS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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