JDR Vol.17 No.3 pp. 487-496
doi: 10.20965/jdr.2022.p0487


Calculating the Coverage Rate of a Transportation-Based Flood Warning Dissemination System in Brisbane

Akihiko Nishino, Akira Kodaka, Madoka Nakajima, and Naohiko Kohtake

Keio University
Collaboration Complex, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8526, Japan

Corresponding author

October 9, 2021
December 21, 2021
April 1, 2022
warning dissemination system, global navigation satellite system, public transportation, coverage rate, General Transit Feed Specification

There is a growing need to introduce warning dissemination systems in disaster-prone regions to improve the coverage of information distribution. In this study, a warning dissemination system was designed in which disaster information transmitted by a global navigation satellite system (GNSS) is received by terrestrial infrastructure, such as sirens and public transportation, converted into audio messages, and delivered automatically. The originality of the designed system lies in its appropriate integration of existing satellite systems and terrestrial infrastructure, making the system potentially applicable in many regions. First, we evaluated the effectiveness of the designed system in distributing audio messages using public buses in Brisbane, Australia, where large floods occur frequently. Real-time location information for public buses was acquired in the format of General Transit Feed Specification (GTFS), which is currently used in many countries. Time-series changes in the coverage rate relative to both the flood inundation zone and population were calculated using a geographic information system (GIS). The simulation results showed that the system could reach 60% of the flood inundation zone and 70% of the population on a holiday, indicating that the designed system could be effectively adapted to the target area. The coverage rate was found to peak during 15:00–16:00, with minimum rates observed late at night and early in the morning. These results will allow the development of an effective disaster management plan. In the future, this system will be evaluated in other regions using the same calculation process.

Cite this article as:
A. Nishino, A. Kodaka, M. Nakajima, and N. Kohtake, “Calculating the Coverage Rate of a Transportation-Based Flood Warning Dissemination System in Brisbane,” J. Disaster Res., Vol.17 No.3, pp. 487-496, 2022.
Data files:
  1. [1] UNDRR, “Sendai Framework Indicators,” [accessed October 6, 2021]
  2. [2] Lima-Paris Action Agenda, [accessed October 6, 2021]
  3. [3] R. Haigh, D. Amaratunga, and K. Hemachandra, “A capacity analysis framework for multi-hazard early warning in coastal communities,” Procedia Engineering, Vol.212, pp. 1139-1146, doi: 10.1016/j.proeng.2018.01.147, 2018.
  4. [4] I. Aguirre-Ayerbe, M. Merino, S. L. Aye, R. Dissanayake, F. Shadiya, and C. M. Lopez, “An evaluation of availability and adequacy of Multi-Hazard Early Warning Systems in Asian countries: A baseline study,” Int. J. of Disaster Risk Reduction, Vol.49, doi: 10.1016/j.ijdrr.2020.101749, 2020.
  5. [5] M. Golnaraghi, “An Overview: Building a global knowledge base of lessons learned from good practices in multi-hazard early warning systems,” M. Golnaraghi (Eds.), “Institutional partnerships in multi-hazard early warning systems,” pp. 1-8, Springer, doi: 10.1007/978-3-642-25373-7_1, 2012.
  6. [6] A. C. Burwell, “Global Positioning System disaster notification messaging service,” Master Thesis, Naval Postgraduate School Monterey CA, 2013.
  7. [7] J. Ventura-Traveset, A. R. Mathur, and F. Toran, “Provision of Emergency Communication Messages through Satellite Based Augmentation Systems for GNSS: The ESA ALIVE Concept,” International Federation of Surveyors (FIG) Articles of the Month, 2007.
  8. [8] D. Iwaizumi, S. Iino, H. Satoh, M. Takaishi, N. Iso, and N. Kohtake, “Improvement of reception and transmission performance on early warning system for multi country with QZSS augmentation signal,” J. Disaster Res., Vol.10, pp. 373-385, doi: 10.20965/jdr.2015.p0373, 2015.
  9. [9] UNISDR, “Flood Hazard and Risk Assessment,” [accessed October 6, 2021]
  10. [10] M. M. Rahman, N. K. Goel, and D. S. Arya, “Study of early flood warning dissemination system in B angladesh,” J. of Flood Risk Management, Vol.6, pp. 290-301, doi: 10.1111/jfr3.12012, 2013.
  11. [11] S. H. M. Fakhruddin, A. Kawasaki, and M. S. Babel, “Community responses to flood early warning system: Case study in Kaijuri Union, Bangladesh,” Int. J. of Disaster Risk Reduction, Vol.14, pp. 323-331, doi: 10.1016/j.ijdrr.2015.08.004, 2015.
  12. [12] K. Prathumchai and R. Bhula-or, “Understanding Households,” Perceptions of Risk Communication During a Natural Disaster: A Case Study of the 2011 Flood in Thailand,” J. Disaster Res., Vol.15, pp. 621-631, doi: 10.20965/jdr.2020.p0621, 2020.
  13. [13] M. Miyamoto, R. Osti, and T. Okazumi, “Development of an integrated decision-making method for effective flood early warning system,” J. Disaster Res., Vol.9, pp. 55-68, doi: 10.20965/jdr.2014.p0055, 2014.
  14. [14] Queensland Floods Commission of Inquiry, “Queensland Floods Commission of Inquiry: Final Report,” 2012, [accessed October 6, 2021]
  15. [15] R. C. Van den Honert and J. McAneney, “The 2011 Brisbane floods: causes, impacts and implications,” Water, Vol.3, pp. 1149-1173, doi: 10.3390/w3041149, 2011.
  16. [16] Z. Zheng, J. Lee, M. Saifuzzaman, and J. Sun, “Exploring association between perceived importance of travel/traffic information and travel behaviour in natural disasters: a case study of the 2011 Brisbane floods,” Transportation Research Part C: Emerging Technologies, Vol.51, pp. 243-259, doi: 10.1016/j.trc.2014.12.011, 2015.
  17. [17] M. Kammerbauer and J. Minnery, “Risk communication and risk perception: lessons from the 2011 floods in Brisbane,” Disasters, Vol.43, pp. 110-134, doi: 10.1111/disa.12311, 2019.
  18. [18] A. J. Maddern, E. P. Privopoulos, and C. Q. Howard, “Emergency vehicle auditory warning signals: physical and psychoacoustic considerations,” Proc. of the 2011 Conf. of the Australian Acoustical Society (ACOUSTICS 2011), Paper No.3, 2011.
  19. [19] “WPS2906 Six Cell Mass Notification Warnign Product,” Whelen Engineering Company, Inc., [accessed October 6, 2021]
  20. [20] S. Choy, J. Handmer, J. Whittaker, Y. Shinohara, T. Hatori, and N. Kohtake, “Application of satellite navigation system for emergency warning and alerting,” Computers, Environment and Urban Systems, Vol.58, pp. 12-18, doi: 10.1016/j.compenvurbsys.2016.03.003, 2016.
  21. [21] S. Choy, Y. B. Bai, S. Zlatanova et al., “Australia-Japan Qzss Emergency Warning Service Trial Project,” The Int. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.44, pp. 21-28, doi: 10.5194/isprs-archives-XLIV-3-W1-2020-21-2020, 2020.
  22. [22] E. Bopp and J. Douvinet, “Spatial performance of location-based alerts in France,” Int. J. of Disaster Risk Reduction, Vol.50, Article No.101909, doi: 10.1016/j.ijdrr.2020.101909, 2020.
  23. [23] A. J. Mathews, M. Haffner, and E. A. Ellis, “GIS-based modeling of tornado siren sound propagation: Refining spatial extent and coverage estimations,” Int. J. of Disaster Risk Reduction, Vol.23, pp. 36-44, doi:, 2017.
  24. [24] Y. Hada, T. Suzuki, H. Shimora, K. Megro, and N. Kodama, “Issues and Future Prospect on Practical Use of Probe Vehicle Data for Disaster Reduction – Provision of the Vehicle Tracking Map in the 2007 Niigataken Chuetsu-oki Earthquake –,” J. of Japan Association for Earthquake Engineering, Vol.9, pp. 148-159, doi: 10.5610/jaee.9.2_148, 2009 (in Japanese and English abstract).
  25. [25] P. Prommaharaj, S. Phithakkitnukoon, M. G. Demissie, L. Kattan, and C. Ratti, “Visualizing public transit system operation with GTFS data: A case study of Calgary,” Heliyon, Vol.6, doi: 10.1016/j.heliyon.2020.e03729, 2020.
  26. [26] A. Lim, S. Sharma, A. Bhaskar, and S. Arkatkar, “An open source framework for GTFS data analytics: case study using the Brisbane TransLink network,” The 41st Australasian Transport Research Forum (ATRF), 2019.
  27. [27] Tranlink GTFS Real-Time Feed, [accessed October 6, 2021]
  28. [28] Flood extent – Queensland – January 2011, Queensland Spatial Catalogue,$=C3F4BC07-88B3-410C-904B-957933079AA8 [accessed October 6, 2021]
  29. [29] Population estimates Regions, Queensland Government Statistician’s Office, [accessed October 6, 2021]
  30. [30] J. B. Holt, C. P. Lo, and T. W. Hodler, “Dasymetric estimation of population density and areal interpolation of census data,” Cartography and Geographic Information Science, Vol.31, pp. 103-121, doi: 10.1559/1523040041649407, 2004.
  31. [31] R. K. Rai, M. J. C. van den Homberg, G. P. Ghimire, and C. McQuistan, “Cost-benefit analysis of flood early warning system in the Karnali River Basin of Nepal,” Int. J. of Disaster Risk Reduction, Vol.47, Article No.101534, doi: 10.1016/j.ijdrr.2020.101534, 2020.
  32. [32] OpenMobilityData, [accessed October 6, 2021]
  33. [33] I. Thompson, M. Shrestha, N. Chhetri, and D. B. Agusdinata, “An institutional analysis of glacial floods and disaster risk management in the Nepal Himalaya,” Int. J. of Disaster Risk Reduction, Vol.47, Article No.101567, doi: 10.1016/j.ijdrr.2020.101567, 2020.
  34. [34] E. N. Rosas-Bermejo, G. Rafael-Valdivia, and R. Paucar-Curasma, “Analysis of sound propagation for outdoor emergency speakers networks,” 2016 IEEE ANDESCON, doi: 10.1109/ANDESCON.2016.7836269, 2016.
  35. [35] Z. M. L. T. San, W. W. Zin, A. Kawasaki, R. A. Acierto, and T. Z. Oo, “Developing Flood Inundation Map Using RRI and SOBEK Models: A Case Study of the Bago River Basin, Myanmar,” J. Disaster Res., Vol.15, No.3, pp. 277-287, doi: 10.20965/jdr.2020.p0277, 2020.
  36. [36] H. Goto and A. T. Murray, “Acoustical properties in emergency warning siren coverage planning,” Computers, Environment and Urban Systems, Vol.81, doi:, 2020.
  37. [37] A. Nishino, M. Nakajima, and N. Kohtake, “GNSS-based M2M early warning system for the improved reach of information,” 2016 IEEE Aerospace Conf., doi: 10.1109/AERO.2016.7500880, 2016.

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