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Time series modelling strategies for road traffic accident and injury data: a case study

AL-HASANI, Ghanim, ASADUZZAMAN, Md and SOLIMAN, Abdel-Hamid (2021) Time series modelling strategies for road traffic accident and injury data: a case study. In: Advances in Data Science and Information Engineering. Springer, Switzerland, pp. 553-559. ISBN 978-3-030-71704-9

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Abstract or description

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 two-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.

Item Type: Book Chapter, Section or Conference Proceeding
Uncontrolled Keywords: Time series modelling; Model diagnostics; SARIMA; Road traffic accidents (RTA); Road traffic injuries (RTI)
Faculty: School of Digital, Technologies and Arts > Engineering
Depositing User: Md ASADUZZAMAN
Date Deposited: 08 Nov 2021 15:31
Last Modified: 09 Nov 2021 04:30
URI: https://eprints.staffs.ac.uk/id/eprint/7069

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