Forecasting the number of road accidents in Poland by day of the week and the impact of pandemics and pandemic-induced changes

References

• Abdullah, E., & Emam, A., Traffic accidents analyzer using big data, in: Proc. - 2015 int. conf. comput. sci. comput. intell. CSCI 2015, institute of electrical and electronics engineers Inc, 2016: pp. 392–22. 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, accid. Accident Analysis & Prevention, 111(2018), 297–310. https://doi.org/10.1016/j.aap.2017.11.035
   PubMedGoogle Scholar
• Arteaga, C., Paz, A., & Park, J. (2020). Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach saf. Science, 132. https://doi.org/10.1016/j.ssci.2020.104988
   Web of Science ®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
• 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 July 2019 Economics 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
   Google Scholar
• Bloomfield, P. (1973). An exponential model in the spectrum of a scalar time series. Biometrika 60 217–226 https://www.jstor.org/stable/2334533.
   Google Scholar
• Chand, A., Jayesh, S., & Bhasi, A. B. Road traffic accidents: An overview of data sources, analysis techniques and contributing factors, Materials today: Proceedings. Volume 47, Part 15, 2021. 5135–5141. 2214-7853, https://doi.org/10.1016/j.matpr.2021.05.415.
   Google Scholar
• Chen, C. Analysis and forecast of traffic accident big data ITM web conference, 12 (2017), p. 04029, https://doi.org/10.1051/itmconf/20171204029
   Google Scholar
• Chudy-Laskowska, K., & Pisula, T. (2014). Forecast of the number of road accidents in Poland. Logistics, 6. http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-5f2d4da1-bd53-4662-be23-4eea5c08aa96
   Google Scholar
• Chudy-Laskowska, K., & Pisula, T. (2015). Prognozowanie liczby wypadków drogowych na Podkarpaciu. Logistics, 4. https://www.researchgate.net/publication/316596821_Prognozowanie_liczby_wypadkow_drogowych_na_Podkarpaciu
   Google Scholar
• Data mining techniques StatSoft. https://www.statsoft.pl/textbook/stathome_stat.html?https%3A%2F%2Fwww.statsoft.pl%2Ftextbook%2Fstdatmin.html
   Google Scholar
• Dudek, G. (2013). Exponential smoothing models for short-term power system load forecasting. Energy Market, 3(106). http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-d5fe006d-c182-451c-ac79-a5f4f908ce79
   Google Scholar
• Dudek, G. (2013). Forecasting time series with multiple seasonal cycles using neural networks with local learning. In L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, & J. M. Zurada, (Eds.), Artificial intelligence and soft computing, ICAISC 2013. Lecture Notes in Computer Science (Vol. 7894). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_5
   Google Scholar
• Dutta, B., Barman, M. P., & Patowary, A. N. (2020). Application of arima model for forecasting road accident deaths in India. International Journal of Agricultural and Statistical Sciences, 16(2), 607–615. https://www.researchgate.net/publication/348349751_APPLICATION_OF_ARIMA_MODEL_FOR_FORECASTING_ROAD_ACCIDENT_DEATHS_IN_INDIA
   Web of Science ®Google Scholar
• Fijorek, K., Mróz, K., Niedziela, K., & Fijorek, D. (2010). Forecasting electricity prices on the day-ahead market using data mining methods energy market 12. https://www.cire.pl/pliki/2/prognozowanie_cen_rdn.pdf
   Google Scholar
• Fiszeder, P. (2009). GARCH class models in empirical financial research. scientific publishers of the nicolaus copernicus university.
   Google Scholar
• Forecasting based on time series. (2022) http://pis.rezolwenta.eu.org/Materialy/PiS-W-5.pdf
   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. Series Transport, 107(33), 52. 0209-3324. https://doi.org/10.20858/sjsutst.2020.107.3
   Google Scholar
• Helgason, A. (2016). Fractional integration methods and short time series: Evidence from a simulation study. Political Analysis, 24(1), 59–68. https://doi.org/10.1093/pan/mpv026
   Web of Science ®Google Scholar
• Karlaftis, M., & Vlahogianni, E. (2009). Memory properties and fractional integration in trans-portation time-series. Transportation Research Part C: Emerging Technologies, 17(4), 444–453. https://doi.org/10.1016/j.trc.2009.03.001
   Web of Science ®Google Scholar
• Kashpruk, N. (2010). Comparative research of statistical models and soft computing for identification of time series and forecasting. Opole University of Technology. https://dbc.wroc.pl/Content/108023/Praca%20Doktorska_%20Nataliia%20Kashpruk_popr.pdf
   Google Scholar
• Khaliq, K. A., Chughtai, O., Shahwani, A., Qayyum, A., & Pannek, J. (2019). Road accidents detection, data collection and data analysis using V2X communication and edge/cloud computing. Electronics, 8(8), 896. https://doi.org/10.3390/electronics8080896
   Web of Science ®Google Scholar
• Kumar, S., Viswanadham, V., & Bharathi, B. (2019). Analysis of road accident. IOP Conference Series: Materials Science and Engineering, 590(1), 012029. https://doi.org/10.1088/1757-899X/590/1/012029
   Google Scholar
• Lavrenz, S., Vlahogianni, E., Gkritza, K., & Ke, Y. (2018). Time series modeling in traffic safetyresearch. Accident Analysis & Prevention, 117, 368–380. https://doi.org/10.1016/j.aap.2017.11.030
   PubMed Web of Science ®Google Scholar
• Lewis, C. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth Scientific
   Google Scholar
• Li, L., Shrestha, S., & Hu, G. (2017). Analysis of road traffic fatal accidents using data mining techniques. IEEE 15th international conference on software engineering research, management and applications (SERA), pp. 363–370, https://doi.org/10.1109/SERA.2017.7965753.
   Google Scholar
• Łobejko, S. (2015). Time series analysis and forecasting with SAS. Main business school in Warsaw. https://docplayer.pl/21098378-Analiza-i-prognozowanie-szeregow-czasowych-z-programem-sas.html
   Google Scholar
• Mamczur, M. Machine learning How does linear regression work? And is it worth using? https://miroslawmamczur.pl/jak-dziala-regresja-liniowa-i-czy-warto-ja-stosowac/
   Google Scholar
• Marcinkowska, J. (2015). Statistical methods and data mining in assessing the occurrence of syncope in the group of narrow-QRS tachycardia (AVNRT and AVRT). Medical University of Karol Marcinkowski in Poznań. http://www.wbc.poznan.pl/Content/373785/index.pdf
   Google Scholar
• McIlroy, R. C., Plant, K. A., Hoque, M. S., Wu, J., Kokwaro, G. O., Nam, V. H., & Stanton, N. A. (2019). Who is responsible for global road safety? A cross-cultural comparison ofactor maps. Accident Analysis & Prevention, 122, 8–18. https://doi.org/10.1016/j.aap.2018.09.011
   PubMed Web of Science ®Google Scholar
• Monederoa, B. D., Gil-Alanaa, L. A., & Martínezaa, M. C. V. (2021, October). Road accidents in Spain: Are they persistent? IATSS Research, 45(3), 317–325. https://doi.org/10.1016/j.iatssr.2021.01.002
   Web of Science ®Google Scholar
• Muck, J. Econometrics. Modeling of time series. Stationary. Unit root tests. ARDL models. Co-integration. http://web.sgh.waw.pl/~jmuck/Ekonometria/EkonometriaPrezentacja5.pdf
   Google Scholar
• Perczak, G., & Fiszeder, P. (2014). GARCH model - using additional information on minimum and maximum prices. Bank and Credit, 2.
   Google Scholar
• Piłatowska, M. (2012). The choice of the order of autoregression depending on the parameters of the generating model. Econometrics, 4(38). https://dbc.wroc.pl/Content/22753/Pilatowska_Wybor_Rzedu_Autoregresji_w_Zale%C5%BCnosci_Od_Parametrow.pdf
   Google Scholar
• Prochazka, J., & Camaj, M. 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. 2017
   Google Scholar
• Procházka, J., Flimmel, S., Čamaj, M., & Bašta, M. (2017). Modelling the Number of Road Accidents. Publishing house of the University of Economics in Wrocław. https://doi.org/10.15611/amse.2017.20.29
   Google Scholar
• Rajput, H., Som, T., & Kar, S. (2015). An automated vehicle license plate recognition system Computer. Long Beach California, 48, 56–61. https://doi.org/10.1109/MC.2015.244
   Web of Science ®Google Scholar
• Random forest. (2022) https://pl.wikipedia.org/wiki/Las_losowy
   Google Scholar
• Road safety assessment handbook. https://www.iung.pl/PJA/wydane/11/PJA11_3.pdf
   Google Scholar
• Sebego, M., Naumann, R. B., Rudd, R. A., Voetsch, K., Dellinger, A. M., & Ndlovu, C. (2008). The impact of alcohol and road traffic policies on crash rates in Botswana, 2004–2011: A time-seriesanalysis. Accident Analysis & Prevention, 70(2008), 33–39. https://doi.org/10.1016/j.aap.2014.02.017
   Google Scholar
• Shetty, P., Sachin, P. C., Kashyap, V. K., & Madi, V. (2017). Analysis of road accidents using data mining techniques. International Research Journal of Engineering and Technology, 04. https://www.sciencepubco.com/index.php/ijet/article/download/15626/6532
   Google Scholar
• Statistic Road Accident. (2022). 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. 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. Research papers of the Wrocław University of Economics, No. 38. https://www.dbc.wroc.pl/Content/15648/Szmuksta-Zawadzka_Zawadzka_O_prognozowaniu_na_podstawie_modeli.pdf
   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. https://doi.org/10.2478/jaiscr-2014-0023
   Google Scholar
• Top Advantages and Disadvantages of Hadoop 3 DataFlair. (2022). https://data-flair.training/blogs/advantages-and-disadvantages-of-hadoop/
   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
• WHO. The Global status on road safety. (2018). 403. https://www.who.int/publications/i/item/9789241565684
   Google Scholar
• Wójcik, A. (2014). Autoregressive vector models as a response to the critique of multi-equation structural econometric models (Vol. 193). Publishing house of the University of Economics in Katowice.
   Google Scholar
• Wrobel, M. S. Application of neural fuzzy systems in chemistry. PhD thesis. University of Silesia. 2017.
   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(2022), 105522. 0925-7535. https://doi.org/10.1016/j.ssci.2021.105522
   Google Scholar
• Zheng, Z., Wang, C., Wang, P., Xiong, Y., Zhang, F., Lv, Y., & Chen, P. (2018). Framework for fusing traffic information from social and physical transportation data. PLoS One, 13, e0201531. https://doi.org/10.1371/journal.pone.02015318
   PubMed Web of Science ®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
   Web of Science ®Google Scholar