Logo image
Back
Prediction of road traffic death rate using neural networks optimised by genetic algorithm
Journal article   Peer reviewed

Prediction of road traffic death rate using neural networks optimised by genetic algorithm

Seyed Ali Jafari, Sepideh Jahandideh, Mina Jahandideh and Ebrahim Barzegari Asadabadi
International journal of injury control and safety promotion, Vol.22(2), pp.153-157
03/04/2015
DOI: https://doi.org/10.1080/17457300.2013.857695
PMID: 24304230

Metrics

1 Record Views
14 Times Cited - Web of Science

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities

Source: InCites

Abstract

artificial neural network genetic algorithm prediction road traffic death rate
Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Details

Logo image