A Stochastic Model to Predict Road Accidents in Albania
DOI:
https://doi.org/10.24297/jam.v23i.9656Keywords:
Albania, Accidents, SARIMA, Forecast, Time seriesAbstract
The importance of predicting accident rates lies in the improvement of road infrastructure and the effective implementation of laws and traffic regulations. The statistical change of many phenomena over time is described by time series. This paper aims to forecast the number of individuals involved in road accidents in Albania by applying SARIMA model approach. This study used monthly number of individuals involved in road traffic accidents in Albania from 2016 to 2023. Using forecasting techniques for the number of traffic accidents can serve as a valuable strategy for achieving various objectives including the implementation of traffic safety campaigns, strategies, and action plans outlined in traffic safety initiatives. The model was found to be effective in capturing the underlying patterns and trends in the data, providing valuable insights for understanding and forecasting traffic accident occurrences in Albania.
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References
Box, G.E.; Jenkins, G.M.; Reinsel, G.C. (2015). Time Series Analysis: Forecasting and Control; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 5 ed.
Chatfield, C., & Xing, H. (2019). The Analysis of Time Series: An Introduction with R (7th ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781351259446
Deretić, N., Stanimirović, D., Awadh, M. A., Vujanović, N., & Djukić, A. (2022). SARIMA Modelling Approach for Forecasting of Traffic Accidents. Sustainability, 14, 4403. https://doi.org/10.3390/su14084403
Farida Merabet, Halim Zeghdoudi.. (2020). On Modelling seasonal ARIMA series: Comparison, Application and Forecast (Number of Injured in Road Accidents in Northeast Algeria). WSEAS Transactions on Systems and Control. 2020;15:235-246. 10.37394/23203.2020.15.25
Getahun, K.A. (2021). Time series modeling of road traffic accidents in Amhara Region. J Big Data 8, 102. https://doi.org/10.1186/s40537-021-00493-z
INSTAT (2024), https://www.instat.gov.al/al/temat/industria-tregtia-dhe-sh%C3%ABrbimet/transporti-aksidentet-dhe-karakteristikat-e-mjetet-rrugore/#tab2M.
Kaçorri (Salillari), D., Basholli, A. and Prifti, L. (2023) “Non-homogeneous Poisson Process with polynomial function rate to predict road accidents: A case study in Albania”, AS-Proceedings, 1(2), pp. 446–450. doi: 10.59287/as-proceedings.194
Montgomery, D. C., Jennings, C. L., Kulahci, M. (2024). Introduction to Time Series Analysis and Forecasting. United States: Wiley.
Rabbani, M.B.A., Musarat, M.A., Alaloul, W.S. et al. (2021). A Comparison Between Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) Based on Time Series Model for Forecasting Road Accidents. Arab J Sci Eng 46, 11113–11138. https://doi.org/10.1007/s13369-021-05650-3
Theodor D. Popescu. (2020). Time Series Analysis for Assessing and Forecasting of Road Traffic Accidents - Case Studies. WSEAS Transactions on Mathematics. 19:177-185. 10.37394/23206.2020.19.17
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Copyright (c) 2024 Denisa Kacorri (Salillari), Anita Caushi, Albina Basholli, Luela Prifti
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.