Highly Accurate Emergency Vehicle Management for Multiple Path Using Support Vector Machine Based Predictor
Cyriac Jose1, K S Vijula Grace2, C Asha Beaula3
1Cyriac Jose, Department of Electronics & Communication Engineering, Noorul Islam Centre for Higher Education, Thuckalay, India.
2K S Vijula Grace, Department of Electronics & Communication Engineering, Noorul Islam Centre for Higher Education, Thuckalay, India.
3C Asha Beaula, Department of Electronics & Communication Engineering, National Engineering College, Kovilpatti, India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript published on 30 June 2019 | PP: 1845-1852 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6870058719/19©BEIESP
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Abstract: Emergency vehicle management (EVM) plays an important role in different lifesaving field such as medicine, fire and safety, and defense etc. The transportation time of the EV mainly depends upon various parameter such as traffic density (TD), weather condition (WC), road condition, conjunction etc. Before selecting a route, it is necessary to confirm that the selected route can reach the destination with minimum time without any destruction of the carrier. A highly accurate emergency vehicle management for multiple path using support vector machine (MP-EVM-SVM) with objective function which is modelled mathematically is proposed in this work. Objective function is modelled based on various parameters such as distance, traffic density, slope, road type, road width etc. MP-EVM-SVM system can be used in the real-time applications such as transportation time estimation and optimum path selection from various path. MP-EVM-SVM can predict the best path which can reach the destination with less time in smooth manner. Proposed algorithm gives 97% of accuracy which is high when compared to the conventional path prediction techniques.
Keywords: Adaptive Neuro Inference System (ANFIS), Emergency Vehicle Management (EVM), Support Vector Machine (SVM), Traffic Density (TD).
Scope of the Article: Data Base Management System