Abstract
The paper aims to provide insights of choosing suitable time series models and analysing road traffic accidents and injuries taking road traffic accident (RTA) and injuries (RTI) data in Oman as a case study as the country faces one of the highest numbers of road accidents per year. Data from January 2000 to June 2019 from several secondary sources were gathered. Time series decomposition, stationarity and seasonality checking were performed to identify the appropriate models for RTA and RTI. SARIMA (3, 1, 1)(2, 0, 0)(12) and SARIMA (0, 1, 1)(1, 0, 2)(12) models were found to be the best for the road traffic accident and injury data, respectively, comparing many different models. AIC, BIC and other error values were used to choose the best model. Model diagnostics were also performed to confirm the statistical assumptions, and 2-year forecasting was performed. The analyses in this paper would help many government departments, academic researchers and decision-makers to generate policies to reduce accidents and injuries.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
A. Boulieri, S. Liverani, K. de Hoogh, M. Blangiardo, A space–time multivariate Bayesian model to analyse road traffic accidents by severity. J. R. Stat. Soc. Ser. A 180(1), 119–139 (2017)
G.E. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, Time Series Analysis: Forecasting and Control (Wiley, London, 2015)
J.D. Cryer, K.S. Chan, Time Series Analysis with Application in R (Springer, Berlin, 2008)
R.J. Hyndman et al., Another look at forecast-accuracy metrics for intermittent demand. Foresight Int. J. Appl. Forecast. 4(4), 43–46 (2006)
F. Mannering, C. Bhat, Analytic methods in accident research: methodological frontier and future directions. Anal. Methods Accid. Res. 1, 1–22 (2014)
NCSI, Monthly Statistical Bulletin (National Centre for Statistics & Information, Sultanate of Oman, 2000–2019)
M. Peden, A. Hyder, Road traffic injuries are a global public health problem. BMJ 324(7346), 1153 (2002)
M. Peden, R. Scurfield, D. Sleet, D. Mohan, A.A. Hyder, E. Jarawan, C.D. Mathers, et al., World report on road traffic injury prevention (2004)
R.O. Police, Traffic Statistic (Director General of Traffic, 2013–2019)
M.A. Quddus, Time series count data models: an empirical application to traffic accidents. Accid. Anal. Prev. 40(5), 1732–1741 (2008)
R. Raeside, D. White, Predicting casualty numbers in Great Britain. J. Transp. Res. Board (1897), 142–147 (2004)
X. Zhang, Y. Pang, M. Cui, L. Stallones, H. Xiang, Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann. Epidemiol. 25(2), 101–106 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Hasani, G., Asaduzzaman, M., Soliman, AH. (2021). Time Series Modelling Strategies for Road Traffic Accident and Injury Data: A Case Study. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-71704-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71703-2
Online ISBN: 978-3-030-71704-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)