Abstract
Roadway closures magnify the adverse effects of disasters on people since any type of such disruption increases the emergency response travel time (ERTT), which is of central importance for the safety and survival of the affected people. Especially in the State of Florida, high winds due to hurricanes, such as the Hurricane Hermine, lead to notable roadway disruptions and closures that compel special attention. As such, in this paper, the accessibility of emergency response facilities, such as police stations, fire stations and hospitals in the City of Tallahassee, the capital of Florida, was extensively studied using real-life data on roadway closures during Hurricane Hermine. A new metric, namely Accessibility Decrease Index, was proposed, which measures the change in ERTT before and in the aftermath of a hurricane such as Hermine. Results clearly show those regions with reduced emergency response facility accessibility and roadways under a disruption risk in the 1-week window after Hermine hit Tallahassee. City officials can pinpoint these critical locations for future improvements and identify those critical roadways, which are under a risk of disruption due to the impact of the hurricane. This information can be utilized to improve emergency response plans by improving the roadway infrastructure and providing alternative routes to public.
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References
AccuWeather (2017) Tallahassee, FL Radar. https://www.accuweather.com/en/us/tallahassee-fl/weather-radar. Accessed 5 Feb 2017
Albert A, Kaur J, Gonzalez M (2017) Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In: KDD '17 Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada, 13–17 August 2017, pp 1357–1366
Amirinia G, Jung S (2017) Buffeting response analysis of offshore wind turbines subjected to hurricanes. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2017.06.005
ASCE (2010) Minimum design loads for buildings and other structures. ASCE Stand 608. https://doi.org/10.1061/9780784412916
Asner GP, Goldstein G (1997) Correlating stem biomechanical properties of Hawaiian canopy trees with hurricane wind damage1. Biotropica 29:145–150. https://doi.org/10.1111/j.1744-7429.1997.tb00018.x
Baker CJ, Bell HJ (1992) Aerodynamics of urban trees. J Wind Eng Ind Aerodyn 44:2655–2666. https://doi.org/10.1016/0167-6105(92)90057-H
Berg R (2016) Hurricane Hermine (AL092016) National hurricane center tropical cyclone report. https://www.nhc.noaa.gov/data/tcr/AL092016_Hermine.pdf. Accessed 5 Feb 2017
Blackwell TH, Kaufman JS (2002) Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med 9:288–295. https://doi.org/10.1197/aemj.9.4.288
Brunsdon C (1995) Estimating probability surfaces for geographical point data: an adaptive kernel algorithm. Comput Geosci 21:877–894. https://doi.org/10.1016/0098-3004(95)00020-9
Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks, pp 1–11. arXiv Prepr arXiv:150800092
Census US (2016) Population and housing unit estimates. https://www.census.gov/programs-surveys/popest/data/tables.2016.html. Accessed 5 Feb 2017
Costea D, Leordeanu M (2016) Aerial image geolocalization from recognition and matching of roads and intersections. arXiv:1605.08323
Deng YB, Shiota T, Shandas R et al (1993) Determination of the most appropriate velocity threshold for applying hemispheric flow convergence equations to calculate flow rate: selected according to the transorifice pressure gradient. Digital computer analysis of the Doppler color flow convergence. Circulation 88:1699–1708
DigiTally (2017) DigiTally. In: City Tallahassee. http://www.talgov.com/Main/digitally.aspx. Accessed 1 Jan 2017
Gardiner BA (1994) Wind and wind forces in a plantation spruce forest. Bound-Layer Meteorol 67:161–186. https://doi.org/10.1007/BF00705512
Islam MS, Aktar S (2011) Measuring physical accessibility to health facilities—a case study on Khulna City. World Health Popul 12:33–41. https://doi.org/10.12927/whp.2011.22195
Kakareko G, Jung S, Vanli OA et al (2017) Hurricane loss analysis based on the population-weighted index. Front Built Environ 3:46. https://doi.org/10.3389/FBUIL.2017.00046
Kocatepe A, Ozguven EE, Ozel H, Horner MW, Moses R (2016) Transportation accessibility assessment of critical emergency facilities: aging population-focused case studies in Florida. In: Zhou J, Salvendy G (eds) Second international conference, ITAP 2016, Held as Part of HCI International 2016, Toronto, Canada, pp 407–416). Retrieved from https://www.springer.com/us/book/9783319399485
Krizhevsky A, Sutskever I, Geoffrey EH (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1–9. https://doi.org/10.1109/5.726791
Lawrence S, Giles CL, Tsoi Ah Chung, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8:98–113. https://doi.org/10.1109/72.554195
McPherson EG, van Doorn NS, Peper PJ (2016) Urban tree database and allometric equations. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany
NDAA (2017) NWS radar image from Tallahassee. https://radar.weather.gov/radar.php?rid=tlh
Ozel H, Ozguven EE, Kocatepe A, Horner MW (2016) Aging population-focused accessibility assessment of multimodal facilities in Florida. Transp Res Rec 2584:45–61
Pons PT, Markovchick VJ (2002) Eight minutes or less: does the ambulance response time guideline impact trauma patient outcome? J Emerg Med 23:43–48. https://doi.org/10.1016/S0736-4679(02)00460-2
Pons PT, Haukoos JS, Bludworth W et al (2005) Paramedic response time: does it affect patient survival? Acad Emerg Med 12:594–600. https://doi.org/10.1197/j.aem.2005.02.013
Powell MD, Houston SH, Amat LR, Morisseau-Leroy N (1998) The HRD real-time hurricane wind analysis system. J Wind Eng Ind Aerodyn 77–78:53–64. https://doi.org/10.1016/S0167-6105(98)00131-7
Saliba D, Buchanan J, Kington RS (2004) Function and response of nursing facilities during community disaster. Am J Public Health 94:1436–1441. https://doi.org/10.2105/AJPH.94.8.1436
Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. In: Seventh international conference document analysis and recognition, 2003 proceedings, vol 1, pp 958–963. https://doi.org/10.1109/icdar.2003.1227801
Stacey GR, Belcher RE, Wood CJ, Gardiner BA (1994) Wind flows and forces in a model spruce forest. Bound-Layer Meteorol 69:311–334. https://doi.org/10.1007/BF00708860
Stiles WS (1959) Color vision: the approach through increment-threshold sensitivity. Proc Natl Acad Sci USA 45:100–114. https://doi.org/10.1073/pnas.45.1.100
Ulak MB, Kocatepe A, Ozguven EE et al (2017) Geographic information system—based spatial and statistical analysis of severe crash hotspot accessibility to hospitals. Transp Res Rec J Transp Res Board 2635:90–97. https://doi.org/10.3141/2635-11
Ulak MB, Kocatepe A, Konila-Sriram LM et al (2018) Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective. Nat Hazards. https://doi.org/10.1007/s11069-018-3260-9
U.S. Census Bureau (2015) 2010 US Census Blocks in Florida. In: Florida Geographic Data Library. http://www.fgdl.org/metadataexplorer/explorer.jsp. Accessed 15 Jan 2015
Vickery P, Skerlj P, Twisdale L (2000) Simulation of hurricane risk in the US using empirical track model. J Struct Eng 126:1222–1237. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1222)
WeatherSTEM (2017) WeatherSTEM. https://www.weatherstem.com. Accessed 5 Feb 2017
Widener MJ, Farber S, Neutens T, Horner MW (2015) Spatiotemporal accessibility to supermarkets using public transit: an interaction potential approach in Cincinnati, Ohio. J Transp Geogr 42:72–83. https://doi.org/10.1016/j.jtrangeo.2014.11.004
Zhu Y, Newsam S (2015) Land use classification using convolutional neural networks applied to ground-level images. In: ACM SIGSPATIAL international conference on advances in geographic information systems. ACM Press, New York, pp 1–4
Acknowledgements
The authors would like to thank the City of Tallahassee, especially Michael Ohlsen and John Powell, for providing data and valuable insight. This research is partly supported by US NSF Award 1640587, and United States Department of Transportation Grant DTRT13-G-UTC42, administered by the Center for Accessibility and Safety for an Aging Population (ASAP). The opinions, results and findings expressed in this manuscript are those of the authors and do not necessarily represent the views of the City of Tallahassee, the United States Department of Transportation, the Center for Accessibility and Safety for an Aging Population (UNF).
Funding
This study was funded by US National Science Foundation (Grant No. 1640587) and United States Department of Transportation (Grant No. DTRT13-G-UTC42).
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Kocatepe, A., Ulak, M.B., Kakareko, G. et al. Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions. Nat Hazards 95, 615–635 (2019). https://doi.org/10.1007/s11069-018-3507-5
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DOI: https://doi.org/10.1007/s11069-018-3507-5