Analysis and Prediction of Crash Fatalities in Australia

Fady M.A Hassouna1, *, Ian Pringle2
1 Department of Civil Engineering, An-Najah National University, Nablus, Palestine
2 Ningbo Institute of TAFENSW, Chengdu Campus, Sichuan, China

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Creative Commons License
© 2019 Hassouna and Pringle

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at Department of Civil Engineering, An-Najah National University, Nablus, Palestine; Tel: +970592660000;
E-mails: and



As fatalities, injuries, and economic losses from road accidents are a major concern for governments and their citizens, Australia, like other countries, has designed and implemented a wide range of strategies to reduce the rate of road accidents.


As part of the strategy design process, data on crash deaths were collected and then analyzed to develop more effective strategies. The data of crash deaths in Australia during the years 1965 to 2018 were analyzed based on gender, causes of crash deaths, and type of road users, and then the results were compared with global averages, then a prediction model was developed to forecast the future annual crash fatalities.


The results indicate that, based on gender, the rate of male road fatalities in Australia was significantly higher than that of female road fatalities. Whereas based on the cause of death, the first cause of death was over speeding. Based on the type of road users, the drivers and passengers of 4-wheel vehicles had the highest rate of fatalities.


The prediction model was developed based on Autoregressive Integrated Moving Average (ARIMA) methodology, and annual road fatalities in Australia for the next five years 2019-2022 have been forecast using this model.

Keywords: Road fatalities, Fatalities prediction, ARIMA model, Prediction model, Crashes in Australia, Road accidents.