A Prototype Web-Based Emergency Response System That Incorporates the Findings from the Shortest Route Techniques for Path Optimization

Authors

  • Olawale J Omotosho Computer Science Department, Babcock University, Ilishan-Remo, Nigeria Author
  • Charles Okonji Computer Science Department, Babcock University, Ilishan-Remo, Nigeria Author
  • Ogbonna A C Author
  • Sodiya Adesina Computer Science Department, Babcock University, Ilishan-Remo, Nigeria Author

Keywords:

Emergency Response Management System, Technology Acceptance Model, Ant Colony Optimization algorithm, Lagos State Emergency

Abstract

This paper holistically reviewed the present  emergency response operations of the Lagos State  Emergency Management Authority (LASEMA), and  identified deficiencies. This ultimately led to the  development of an improved network, premised on the  assumption that all the response management sub-stations  (LRU) of LASEMA in Lagos State were networked to a  central location where all command operations are easily  disseminated. An improved framework was then designed,  that utilised an improved Ant Colony Optimization technique layered on the Google Map functionality to  determine the shortest route to an incident site for the  emergency vehicle conveying the first responders to the  incident site. A detailed discussion on the design,  development, implementation and evaluation approaches  used for the Emergency Response Management System  (ERMS) was done. How data used in this work were  collected, tested for quality of its contents and then  analysed using the descriptive analysis of the Statistical  Package for Social Sciences (SPSS) software was  extensively discussed.  

Also, the data collected before and after the implementation  of the developed Emergency Response Management  System (ERMS) were analysed using the descriptive  analysis of the SPSS software, as to measure the perceived  performance of the system, based on the variables defined  from the Technology Acceptance Model (TAM). From the  analyses of the results of these metrics, we concluded that  the ERMS was able  

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References

Dorigo M, Maniezzo V, Colorni A. (1996). Ant System: Optimization by a colony of cooperating

agents. IEEE Trans Syst Man Cybernet Part B 1996; 26 (1):29–41.

Dorigo, M. (1992). Optimization, Learning and Natural Algorithms; a PhD Thesis, Politecnico di Milano, Italy 1992.

Dorigo M. & Stützle T. (2002). The ant colony optimization metaheuristic: Algorithms, applications and advances. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics. Kluwer Academic Publishers,

Dwiputranti Made Irma, Oktora Adriyani, Okdinawatt Jane, Fazan M Nurkamal (2019). Acceptance and Use of Information Technology: Understanding Information Systems for Indonesia’s Humanitarian Relief Operations; Gadjah Mada International Journal of Business, Vol. 21, No. 3 (September-December 2019): 242-262Retrieved from https://lasema.lagosstate.gov.ng/

Lazarowska, A., (2014). Ant Colony Optimization based navigational decision support system, in Procedia Computer Science, 35, 1013 – 1022.

Lihan Lihan (2017). An Application on Mobile Devices with Android and IOS Operating Systems Using Google Maps APIs for the Traveling Salesman Problem; Applied Artificial Intelligence; 31:4, 332-345. DOI: 10.1080/08839514.2017.1339983, ISSN: 0883-9514 1087-6545 (Online) http://www.tandfonline.com/Ioi/uaa120

Marco Darigo, Mauro Birattari and Thomas Stutzle (2006). Ant Colony Optimization; an Article Publication in IEEE Computational Intelligence Magazine December 2006; DOI.10.1109/MCI.2006.329691

Marco Darigo and Thomas Stutzle (2014). The Ant Colony Optimization Meta-heuristic: Algorithms, Applications, and Advances; a Technical Report IRIDIA-2000-32

Mikel Fagel and Greg Benson (2016). A Golden Hour of Disasters: The Road to Recovery; A Presentation at ASIS Orlando 2016

Shyama, K., & Kumar, P. N. R., (2015). On the amenability and suitability of Ant Colony Algorithms for Convoy Movement Problem, in Procedia - Social and Behavioural Sciences, 189, 3 – 16.

Tomera, M., (2014). Ant colony optimization algorithm applied to ship steering control, in Procedia Computer Science, 35, 83 – 92.

Vimala Nunavath. Andreas Prinz; & Tina Comes (2016). Identifying First Responders Information Needs: Supporting Search and Rescue Operations for Fire Emergency Operations; International Journal of Information Systems for Crisis Responses and Management, 8(1).

Vinson F. (2013). Top 10 Reasons why mobile qtechnology is more important than ever. Retrieved http://localorganicrankings.com/top-10-reasons-why mobile-technology-is-more-important-than-ever/

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Published

2020-03-25

How to Cite

A Prototype Web-Based Emergency Response System That Incorporates the Findings from the Shortest Route Techniques for Path Optimization . (2020). International Journal of Innovative Research in Computer Science & Technology, 8(2), 29–36. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13335