Abstract: Road safety researchers working on road accident data have witnessed success in road traffic accidents analysis through the application data analytic techniques, though, little progress was made into the prediction of road injury. This paper applies advanced data analytics methods to predict injury severity levels and evaluates their performance. The study uses predictive modelling techniques to identify risk and key factors that contributes to accident severity. The study uses publicly available data from UK department of transport that covers the period from 2005 to 2019. The paper presents an approach which is general enough so that can be applied to different data sets from other countries. The results identified that tree based techniques such as XGBoost outperform regression based ones, such as ANN. In addition to the paper, identifies interesting relationships and acknowledged issues related to quality of data.

Mohamed K Nour, Atif Naseer, Basem Alkazemi and Muhammad Abid Jamil, “Road Traffic Accidents Injury Data Analytics” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111287

@article{Nour2020,
title = {Road Traffic Accidents Injury Data Analytics},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111287},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111287},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Mohamed K Nour and Atif Naseer and Basem Alkazemi and Muhammad Abid Jamil}
}