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A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE'S RESPONSE TIME

Yıl 2017, Cilt: 5 Sayı: 1, 47 - 60, 01.03.2017
https://doi.org/10.15317/Scitech.2017.69

Öz

Recently, for ensuring human life and safety, routing and intervening emergency vehicles

as soon as possible an important subject. Ambulance, firefighter, police and other emergency vehicles

are the main object of the intervention. Reaching the emergency area as soon as possible is important for

saving human life and preventing economic loss. Directing and routing emergency vehicles from the

moment they receive an emergency call to the event location must be considered carefully. In this study,

ensuring the shortest response time for the emergency vehicles, obstacles like speed bumps, traffic

lights, parking status of the streets, railroad crossings and crossroads which reduce the speed of

emergency vehicles and increasing the intervention time are detected. In order to determine the effect of

obstacles, a new Segment Effect Value (SEV) formula is developed. Values are assigned to the street

segments according to obstacles in particular streets. SEV formula makes possible to determine the

routes that provides the shortest intervention time. Results are compared with the shortest route and

the shortest time route.

Kaynakça

  • Ahuja, R., Orlin, K., James, B., Pallottino, S., Scutella, M. G., 2002, Dynamic Shortest Paths Minimizing Travel Times and Costs, MIT Sloan Working Paper; No. 4390-02.
  • Aktas, S.G., Swalehe, M., 2016, “Dynamic Ambulance Deployment to Reduce Ambulance Response Times using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkey”, Procedia Environmental Sciences, Vol. 36, pp. 199 – 206.
  • Altınbaş, K. H., Bilir, N., 2001, Ambulance Times of Ankara Emergency Aid and Rescue Services Ambulance System”, European Journal of Emergency Medicine, Vol. 8, pp. 43-50.
  • Ateş, S., Coşkun, Z. M., Aydınoğlu, A. Ç., “Coğrafi Bilgi Sistemleri ile En Uygun Ambulans Yerlerinin Belirlenmesi”, 13. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, 18-22 Nisan 2011.
  • Bandyopadhyay, M., Singh, V., 2016, “Development of Agent Based Model For Predicting Emergency Response Time”, Perspectives in Science, Vol. 8, pp. 138—141.
  • Blackwell, T. H. Kaufman, J . S., 2001, “Response Time Effectiveness: Comparison of Response Time and Survival in an Urban Emergency Medical Services System”, Academic Emergency Management, Vol. 9, pp. 288-295.
  • Brown, L.H., Whitney, C.L., Hunt, R.C., Addario, M., Hogue, T., 2000, “Do Warning Lights and Sirens Reduce Ambulance Response Times?”, Prehospital Emergency Care, Vol. 4, pp. 70–74.
  • Campbell A, M., Vandenbussche D., Hermann W., 2008, “Routing for Relief Efforts”, Transportation Science, Vol. 42, pp. 127–145.
  • Haghani, A., H. Hu., Q. Tian., “An Optimization Model for Real-Time Emergency Vehicle Dispatching and Routing”. In: the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 12-16 January 2003.
  • Ho, J., Casey, B., 1998, “Time Saved with Use of Emergency Warning Lights and Sirens During Response to Requests for Emergency Medical Aid in Urban Environment”, Annals of Emergency Medicine, Vol. 32 (5), pp. 585–588.
  • Ho, J., Lindquist, M., 2001, “Time Saved with Use of Emergency Warning Lights and Siren While Responding to Requests for Emergency Medical Aid in A Rural Environment”, Prehospital Emergency Care, Vol. 5, pp. 159–162.
  • Huang, D., Chu, X., Mao Z., 2012, “A Simulation Framework for Emergency Response of Highway Traffic Accident”, Procedia Engineering, Vol. 29, pp. 1075 - 1080.
  • Kerstin P., Jan, P., Jörgen, J., Gun N., 2011, “Time Saved with High Speed Driving of Ambulances”, Accident Analysis and Prevention, Vol. 43(3), pp. 818–822.
  • Kobusingye OC, Hyder AA, Bishai D, Joshipura M, Hicks ER, Mock C., 2010, Emergency Medical Services,New York: John Wiley & Sons Ltd; 2010.p.167-. 169. 17.
  • Konstantinos G. Zografos., George M. Vasilakis., Ioanna M. Giannouli., 2000, “Methodological Framework for Developing Decision Support Systems (DSS) for Hazardous Materials Emergency Response Operations”, Journal of Hazardous Materials, Vol. 71 (1–3.7), pp. 503–521.
  • Lam, S.S., Zhang, J., Zhang, Z. C., Oh, H. C., Overton, J., Ng, Y. Y., Ong, M. E., 2015, “Dynamic Ambulance Reallocation for The Reduction of Ambulance Response Times Using System Status Management”, The American Journal of Emergency Medicine, Vol. 33, pp. 159-166.
  • Lin, S.H., Lai, C.L., 2000, “Kinetic Characteristic of Textile Wastewater Ozonation in Fluidized and Fixed Activated Carbon Belts”, Water Research, Vol. 34, pp. 763-772.
  • Liu, H., Hall, R., 2002, W. INCISIM: User’s Manual. California Path Research Report; UCB-ITS-PWP-2000-15. Minciardi, R., Sacile, R., Trasforini, E., 2007, “A Decision Support System for Resource Intervention in Real-Time Emergency Management”, International Journal of Emergency Management, Vol. 4 (1), pp. 59-71.
  • Mohd, S., Mohd, I., Syed, M., 2008, “Ambulance Response Time and Emergency Medical Dispatcher Program: A study in Kelantan, MALAYSIA”, Southeast Asian Journal of Tropical Medicine and Public, Vol 39 (6).
  • Narad, R. A., Iesbock, K. R., 1999, “Regulation of Ambulance Response Time in California”, Prehospital Emergency Care, Vol. 3, pp. 131-135.
  • Ong M, E., Ng FS., Overton J., Yap S., Andresen D., Yong DK., Lim SH., Anantharaman V., 2009, “Geographic Time Distribution of Ambulance Calls in Singapore: Utility of Geographic Information System in Ambulance Deployment”, Annals Academy of Medicine, Vol. 38, pp.91-94.
  • Ozbay, K., Bartin, B., 2003, “Incident Management Simulation”, Simulation, Vol. 79(2), pp. 69-82.
  • Paraskevi S. Georgiadoua, Ioannis A. Papazoglou, Chris T. Kiranoudisa, Nikolaos C. Markatosa., 2010, “Multi-Objective Evolutionary Emergency Response Optimization for Major Accidents”, Journal of Hazardous Materials, Vol. 178, pp. 792–803.
  • Peter S, J., Hall, G. B., 1999, “Assessment of Ambulance Response Performance Using A Geographic Information System”, Social Science & Medicine, Vol. 49, pp. 1551-1556.
  • Stefan, R., Walter,.G., 2014, “A Math-Heuristic for The Warehouse Location–Routing Problem in Disaster Relief”, Computers & Operations Research, Vol. 42, pp. 25 – 39.
  • Yoon, S,W., Velasquez, J.D., Partridge, B.K., Nof., S,Y., 2008, “Transportation Security Decision Support System for Emergency Response: A Training Prototype”, Decision Support Systems, Vol. 46, pp. 139–148.
  • Zhang, Z., He, Q., Gou, J., Li,X., 2016, “Performance Measure for Reliable Travel Time of Emergency Vehicles, Transportation Research Part C, Vol. 65, pp. 97–110.
  • Ziliaskopoulos, A., H. Mahmassani., 1993, “Time Dependent, Shortest-Path Algorithm for Real-Time Intelligent Vehicle Highway System Applications”, Transportation Research Record, 1480; pp. 94- 100.

Acil Müdahale Araçlarının Müdahale Zamanını Azaltmak İçin Cbs Tabanlı Bir Navıgasyon Yaklaşım

Yıl 2017, Cilt: 5 Sayı: 1, 47 - 60, 01.03.2017
https://doi.org/10.15317/Scitech.2017.69

Öz

İnsan yaşamını ve güvenliğini sağlamak için acil durum araçlarının olabildiğince hızlı müdahalesi

önemli bir konu haline gelmiştir. Ambulans, itfaiye, polis ve diğer araçları acil durum müdahale

araçlarının başında gelmektedir. Yaşam kayıplarının ve ekonomik kayıpların önüne geçmek için hızlı

müdahale büyük önem taşımaktadır. İhbar alınmasından itibaren olay yerine gidene kadarki

yönlendirme dikkatle yapılmalıdır. Müdahale zamanını kısaltmak için hız bariyerleri, trafik ışıkları, park

etmiş araçlar, demiryolu geçitleri ve kavşaklar gibi hız kesici engellerin bilinmesi gerekmektedir. Bu

engellerin etkisini ortaya koymak için Segment Etki Değeri (SED) isimli bir formül geliştirilmiştir ve bu

formül ile her bir cadde segmentine değer atanmıştır. Böylece araçların bu engellerle

karşılaşmayacakları en hızlı güzergah üzerinden gitmelerinin sağlanması amaçlanmıştır. En kısa yol ve

en hızlı yol arasındaki farklar paylaşılmıştır.

Kaynakça

  • Ahuja, R., Orlin, K., James, B., Pallottino, S., Scutella, M. G., 2002, Dynamic Shortest Paths Minimizing Travel Times and Costs, MIT Sloan Working Paper; No. 4390-02.
  • Aktas, S.G., Swalehe, M., 2016, “Dynamic Ambulance Deployment to Reduce Ambulance Response Times using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkey”, Procedia Environmental Sciences, Vol. 36, pp. 199 – 206.
  • Altınbaş, K. H., Bilir, N., 2001, Ambulance Times of Ankara Emergency Aid and Rescue Services Ambulance System”, European Journal of Emergency Medicine, Vol. 8, pp. 43-50.
  • Ateş, S., Coşkun, Z. M., Aydınoğlu, A. Ç., “Coğrafi Bilgi Sistemleri ile En Uygun Ambulans Yerlerinin Belirlenmesi”, 13. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, 18-22 Nisan 2011.
  • Bandyopadhyay, M., Singh, V., 2016, “Development of Agent Based Model For Predicting Emergency Response Time”, Perspectives in Science, Vol. 8, pp. 138—141.
  • Blackwell, T. H. Kaufman, J . S., 2001, “Response Time Effectiveness: Comparison of Response Time and Survival in an Urban Emergency Medical Services System”, Academic Emergency Management, Vol. 9, pp. 288-295.
  • Brown, L.H., Whitney, C.L., Hunt, R.C., Addario, M., Hogue, T., 2000, “Do Warning Lights and Sirens Reduce Ambulance Response Times?”, Prehospital Emergency Care, Vol. 4, pp. 70–74.
  • Campbell A, M., Vandenbussche D., Hermann W., 2008, “Routing for Relief Efforts”, Transportation Science, Vol. 42, pp. 127–145.
  • Haghani, A., H. Hu., Q. Tian., “An Optimization Model for Real-Time Emergency Vehicle Dispatching and Routing”. In: the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 12-16 January 2003.
  • Ho, J., Casey, B., 1998, “Time Saved with Use of Emergency Warning Lights and Sirens During Response to Requests for Emergency Medical Aid in Urban Environment”, Annals of Emergency Medicine, Vol. 32 (5), pp. 585–588.
  • Ho, J., Lindquist, M., 2001, “Time Saved with Use of Emergency Warning Lights and Siren While Responding to Requests for Emergency Medical Aid in A Rural Environment”, Prehospital Emergency Care, Vol. 5, pp. 159–162.
  • Huang, D., Chu, X., Mao Z., 2012, “A Simulation Framework for Emergency Response of Highway Traffic Accident”, Procedia Engineering, Vol. 29, pp. 1075 - 1080.
  • Kerstin P., Jan, P., Jörgen, J., Gun N., 2011, “Time Saved with High Speed Driving of Ambulances”, Accident Analysis and Prevention, Vol. 43(3), pp. 818–822.
  • Kobusingye OC, Hyder AA, Bishai D, Joshipura M, Hicks ER, Mock C., 2010, Emergency Medical Services,New York: John Wiley & Sons Ltd; 2010.p.167-. 169. 17.
  • Konstantinos G. Zografos., George M. Vasilakis., Ioanna M. Giannouli., 2000, “Methodological Framework for Developing Decision Support Systems (DSS) for Hazardous Materials Emergency Response Operations”, Journal of Hazardous Materials, Vol. 71 (1–3.7), pp. 503–521.
  • Lam, S.S., Zhang, J., Zhang, Z. C., Oh, H. C., Overton, J., Ng, Y. Y., Ong, M. E., 2015, “Dynamic Ambulance Reallocation for The Reduction of Ambulance Response Times Using System Status Management”, The American Journal of Emergency Medicine, Vol. 33, pp. 159-166.
  • Lin, S.H., Lai, C.L., 2000, “Kinetic Characteristic of Textile Wastewater Ozonation in Fluidized and Fixed Activated Carbon Belts”, Water Research, Vol. 34, pp. 763-772.
  • Liu, H., Hall, R., 2002, W. INCISIM: User’s Manual. California Path Research Report; UCB-ITS-PWP-2000-15. Minciardi, R., Sacile, R., Trasforini, E., 2007, “A Decision Support System for Resource Intervention in Real-Time Emergency Management”, International Journal of Emergency Management, Vol. 4 (1), pp. 59-71.
  • Mohd, S., Mohd, I., Syed, M., 2008, “Ambulance Response Time and Emergency Medical Dispatcher Program: A study in Kelantan, MALAYSIA”, Southeast Asian Journal of Tropical Medicine and Public, Vol 39 (6).
  • Narad, R. A., Iesbock, K. R., 1999, “Regulation of Ambulance Response Time in California”, Prehospital Emergency Care, Vol. 3, pp. 131-135.
  • Ong M, E., Ng FS., Overton J., Yap S., Andresen D., Yong DK., Lim SH., Anantharaman V., 2009, “Geographic Time Distribution of Ambulance Calls in Singapore: Utility of Geographic Information System in Ambulance Deployment”, Annals Academy of Medicine, Vol. 38, pp.91-94.
  • Ozbay, K., Bartin, B., 2003, “Incident Management Simulation”, Simulation, Vol. 79(2), pp. 69-82.
  • Paraskevi S. Georgiadoua, Ioannis A. Papazoglou, Chris T. Kiranoudisa, Nikolaos C. Markatosa., 2010, “Multi-Objective Evolutionary Emergency Response Optimization for Major Accidents”, Journal of Hazardous Materials, Vol. 178, pp. 792–803.
  • Peter S, J., Hall, G. B., 1999, “Assessment of Ambulance Response Performance Using A Geographic Information System”, Social Science & Medicine, Vol. 49, pp. 1551-1556.
  • Stefan, R., Walter,.G., 2014, “A Math-Heuristic for The Warehouse Location–Routing Problem in Disaster Relief”, Computers & Operations Research, Vol. 42, pp. 25 – 39.
  • Yoon, S,W., Velasquez, J.D., Partridge, B.K., Nof., S,Y., 2008, “Transportation Security Decision Support System for Emergency Response: A Training Prototype”, Decision Support Systems, Vol. 46, pp. 139–148.
  • Zhang, Z., He, Q., Gou, J., Li,X., 2016, “Performance Measure for Reliable Travel Time of Emergency Vehicles, Transportation Research Part C, Vol. 65, pp. 97–110.
  • Ziliaskopoulos, A., H. Mahmassani., 1993, “Time Dependent, Shortest-Path Algorithm for Real-Time Intelligent Vehicle Highway System Applications”, Transportation Research Record, 1480; pp. 94- 100.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Fatih Sarı

Yayımlanma Tarihi 1 Mart 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 5 Sayı: 1

Kaynak Göster

APA Sarı, F. (2017). A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 5(1), 47-60. https://doi.org/10.15317/Scitech.2017.69
AMA Sarı F. A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME. sujest. Mart 2017;5(1):47-60. doi:10.15317/Scitech.2017.69
Chicago Sarı, Fatih. “A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 5, sy. 1 (Mart 2017): 47-60. https://doi.org/10.15317/Scitech.2017.69.
EndNote Sarı F (01 Mart 2017) A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 5 1 47–60.
IEEE F. Sarı, “A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME”, sujest, c. 5, sy. 1, ss. 47–60, 2017, doi: 10.15317/Scitech.2017.69.
ISNAD Sarı, Fatih. “A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 5/1 (Mart 2017), 47-60. https://doi.org/10.15317/Scitech.2017.69.
JAMA Sarı F. A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME. sujest. 2017;5:47–60.
MLA Sarı, Fatih. “A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, c. 5, sy. 1, 2017, ss. 47-60, doi:10.15317/Scitech.2017.69.
Vancouver Sarı F. A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE’S RESPONSE TIME. sujest. 2017;5(1):47-60.

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