International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2018, Vol. 3, Issue 3, Part C

Forecast of road traffic accidents in Jordan utilizing artificial neural network (ANN)


Author(s): M Umapathi and K Geetha

Abstract: Highway-related mischances are viewed as one of the most significant issues in the advanced world as movement mischances make genuine risk human life around the world. Jordan, a creating nation, has a high and developing level of car crashes bringing about in excess of 13000 fatalities in the vicinity of 1989 and 2012 with a normal yearly cost of over $500 million. The expectation of future car crashes is in this manner of most extreme significance so as to value the size of the issue and accelerate the basic leadership towards its lightening. In this paper, a car crash forecast show was created utilizing the novel Artificial Neural Network (ANN) reproduction with the point of distinguishing its appropriateness for the expectation of auto collisions under Jordanian conditions. The outcomes showed that the evaluated auto collisions, in view of adequate information, are sufficiently close to genuine auto collisions and in this manner are dependable to foresee future car crashes in Jordan.

Pages: 242-245 | Views: 401 | Downloads: 12

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How to cite this article:
M Umapathi, K Geetha. Forecast of road traffic accidents in Jordan utilizing artificial neural network (ANN). Int J Stat Appl Math 2018;3(3):242-245.
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International Journal of Statistics and Applied Mathematics