Forecasting the number of road accidents in Poland using trend models

Abdullah E., Emam A. 2016. Traffic accidents analyzer using big data. 2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas,   Google Scholar

p. 392-397. https://doi.org/10.1109/CSCI.2015.187   Google Scholar

Al-Madani H. 2018. Global road fatality trends’ estimations based on country-wise microlevel data. Accident Analysis & Prevention, 111: 297-310. https://doi.org/10.1016/j.aap.2017.11.035   Google Scholar

Arteaga C., Paz A., Park J. 2020. Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach. Safety Science, 132, art. no 104988, https://doi.org/10.1016/j.ssci.2020.104988   Google Scholar

Bąk I., Cheba K., Szczecińska B. 2019. The statistical analysis of road traffic in cities of Poland. Transportation Research Procedia, 39: 14-23. https://doi.org/10.1016/j.trpro.2019.06.003   Google Scholar

Batko W. 1984. Methods of synthesis of predictive diagnoses in technical diagnostics. Habilitation dissertation. Akademia Górniczo-Hutnicza, Kraków.   Google Scholar

Biswas A.A., Mia J., Majumder A. 2019. Forecasting the number of road accidents and casualties using random forest regression in the context of Bangladesh. 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). http://dx.doi.org/10.1109/ICCCNT45670.2019.8944500   Google Scholar

Chand A., Jayesh S., Bhasi A.B. 2021. Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today: Proceedings, 47(15): 5135-5141. https://doi.org/10.1016/j.matpr.2021.05.415   Google Scholar

Dudek G. 2013. Forecasting time series with multiple seasonal cycles using neural networks with local learning. In: Artificial Intelligence and Soft Computing. Eds. L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada. ICAISC 2013. Lecture Notes in Computer Science, 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_5   Google Scholar

Fijorek K., Mróz K., Niedziela K., Fijorek D. 2010. Forecasting hourly electricity prices in polish day-ahead market: Data mining approach. Rynek Energii, 91(6): 46-50.   Google Scholar

Global status report on road safety 2018. 2018. World Health Organization. Retrieved from https://www.who.int/publications/i/item/9789241565684   Google Scholar

Gorzelanczyk P., Pyszewska D., Kalina T., Jurkovic M. 2020. Analysis of road traffic safety in the Pila poviat. Scientific Journal of Silesian University of Technology. Transport. 107: 33-52.   Google Scholar

https://doi.org/10.20858/sjsutst.2020.107.3   Google Scholar

Helgason A. 2016. Fractional integration methods and short Time series: evidence from asimulation study. Political Analysis, 24(1): 59-68. Retrieved from http://www.jstor.org/stable/24573204   Google Scholar

Kowalski L. 2005. Prognozowanie na podstawie szeregów czasowych. Prognozowanie i Symulacje. Retrieved from http://pis.rezolwenta.eu.org/Materialy/PiS-W-5.pdf   Google Scholar

Las losowy. 2022. Wikipedia. Wolna encyklopedia. Retrieved from https://pl.wikipedia.org/wiki/Las_losowy   Google Scholar

Lavrenz S., Vlahogianni E., Gkritza K., Ke Y. 2018. Time series modeling in traffic safety research. Accident Analysis & Prevention, 117: 368-380. https://doi.org/10.1016/j.aap.2017.11.030   Google Scholar

Mamczur M. 2022. Jak działa regresja liniowa? I czy warto ją stosować? Blog o data science, AI, uczeniu maszynowym i wizualizacji danych. https://miroslawmamczur.pl/jak-dziala-regresja-liniowa-i-czy-warto-ja-stosowac/   Google Scholar

Marciniak J. 1982. Diagnostyka techniczna kolejowych pojazdów mechanicznych. WKiŁ, Warszawa.   Google Scholar

Monederoa B.D., Gil-Alanaa L.A., Martínezaa M.C.V. 2021. Road accidents in Spain: Are they persistent? IATSS Research, 45(3): 317-325. https://doi.org/10.1016/j.iatssr.2021.01.002   Google Scholar

Piłatowska M. 2012. The choice of the order of autoregression depending on the parameters of the generating model. Econometrics, 4(38).   Google Scholar

Prochazka J., Camaj M. 2017. Modelling the number of road accidents of uninsured drivers and their severity. Proceedings of International Academic Conferences 5408040, International Institute of Social and Economic Sciences.   Google Scholar

Road Accident Statistics. 2024. Retrieved from https://statystyka.policja.pl/   Google Scholar

Sunny C.M., Nithya S., Sinshi K.S., Vinodini V.M.D. Lakshmi A.K.G., Anjana S., Manojkumar T.K. 2019. Forecasting of road accident in Kerala: A case study. Environmental Science, Computer Science. 2018 International Conference on Data Science and Engineering (ICDSE). https://doi.org/10.1109/ICDSE.2018.8527825   Google Scholar

Szmuksta-Zawadzka M., Zawadzki J. 2009. Forecasting on the basis of Holt-Winters models for complete and incomplete data. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 38: 85-99.   Google Scholar

Tambouratzis T., Souliou D., Chalikias M., Gregoriades A. 2014. Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees. Journal of Artificial Intelligence and Soft Computing Research, 4(1): 31-42. http://dx.doi.org/10.2478/jaiscr-2014-0023   Google Scholar

Tylicki H. 1993. Optimisation of methods of forecasting reliable operation of equipment. Zagadnienia Eksploatacji Maszyn, 3.   Google Scholar

Vilaça M., Silva N., Coelho M.C. 2017. Statistical analysis of the occurrence and severity of crashes involving vulnerable road users. Transportation Research Procedia, 27: 1113-1120. https://doi.org/10.1016/j.trpro.2017.12.113   Google Scholar

Wójcik A. 2014. Autoregressive vector models as a response to the critique of multi-equation structural econometric models. Publishing house of the University of Economics, Katowice.   Google Scholar

Yang Z. Zhang W., Feng J. 2022. Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework. Safety Science, 146: 105522. https://doi.org/10.1016/j.ssci.2021.105522   Google Scholar

Zhu L., Lu L., Zhang W., Zhao Y., Song M. 2019. Analysis of accident severity for curved roadways based on Bayesian networks. Sustainability, 11(8): 2223. https://doi.org/10.3390/su11082223   Google Scholar