Acil Müdahale Araçlarının Müdahale Zamanını Azaltmak İçin Cbs Tabanlı Bir Navıgasyon Yaklaşım
İnsan yaşamını ve güvenliğini sağlamak için acil durum araçlarının olabildiğince hızlı müdahalesiönemli bir konu haline gelmiştir. Ambulans, itfaiye, polis ve diğer araçları acil durum müdahalearaçlarının başında gelmektedir. Yaşam kayıplarının ve ekonomik kayıpların önüne geçmek için hızlımüdahale büyük önem taşımaktadır. İhbar alınmasından itibaren olay yerine gidene kadarkiyönlendirme dikkatle yapılmalıdır. Müdahale zamanını kısaltmak için hız bariyerleri, trafik ışıkları, parketmiş araçlar, demiryolu geçitleri ve kavşaklar gibi hız kesici engellerin bilinmesi gerekmektedir. Buengellerin etkisini ortaya koymak için Segment Etki Değeri (SED) isimli bir formül geliştirilmiştir ve buformül ile her bir cadde segmentine değer atanmıştır. Böylece araçların bu engellerlekarşılaşmayacakları en hızlı güzergah üzerinden gitmelerinin sağlanması amaçlanmıştır. En kısa yol veen hızlı yol arasındaki farklar paylaşılmıştır.
A GIS BASED NEW NAVIGATION APPROACH FOR REDUCING EMERGENCY VEHICLE'S RESPONSE TIME
Recently, for ensuring human life and safety, routing and intervening emergency vehiclesas soon as possible an important subject. Ambulance, firefighter, police and other emergency vehiclesare the main object of the intervention. Reaching the emergency area as soon as possible is important forsaving human life and preventing economic loss. Directing and routing emergency vehicles from themoment 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, trafficlights, parking status of the streets, railroad crossings and crossroads which reduce the speed ofemergency vehicles and increasing the intervention time are detected. In order to determine the effect ofobstacles, a new Segment Effect Value (SEV) formula is developed. Values are assigned to the streetsegments according to obstacles in particular streets. SEV formula makes possible to determine theroutes that provides the shortest intervention time. Results are compared with the shortest route andthe shortest time route.
___
- 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.