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Every year a very large number of people die on the roads. Although the number decreases year by year, it remains high. The pandemic has reduced the number of road accidents, but the value is still very high. For this reason, it is necessary to know on which days the highest number of traffic accidents occur, and to know the forecast of accidents by day of the week for the coming years, so that we can do everything possible to minimize the number of traffic accidents. The purpose of the article is to make a forecast of the number of road accidents in Poland according to the day of the week. The research was divided into two parts. The first was the analysis of annual data from the Police statistics on the number of road accidents in Poland in 2000-2021, and on this basis the forecast of the number of road accidents for 2022-2031 was determined. The second part of the research, dealt with monthly data from 2000-2021. Again, the analyzed forecast for the period January 2022 – December 2023 was determined. The results of the study indicate that we can still expect a decline in the number of accidents in the coming years, which is particularly evident when analyzing annual data. It is worth noting that the prevailing pandemic distorts the results obtained. The research was conducted in MS Excel, using selected trend models.
Słowa kluczowe
Rocznik
Tom
Strony
175--192
Opis fizyczny
Bibliogr. 53 poz., tab., wykr.
Twórcy
autor
- Akademia Nauk Stosowanych im. Stanisława Staszica, ul. Podchorążych 10, 64-920 Piła
Bibliografia
- ABDULLAH E., EMAM A. 2015. Traffic accidents analyzer using big data. International Conference on Computational Science and Computational Intelligence (CSCI). 7-9 Dec. https://doi.org/10.1109/CSCI.2015.187
- 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
- ARTEAGA C., PAZ A., PARK J. 2020. Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach. Safety Science, 132, Article 104988.
- 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
- 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).
- BLOOMFIELD P. 1973. An exponential model in the spectrum of a scalar time series. Biometrika, 60: 217-226. Retrieved from https://www.jstor.org/stable/2334533
- 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
- CHEN C. 2017. Analysis and forecast of traffic accident big data. ITM Web of Conferences, 12: 04029. https://doi.org/10.1051/itmconf/20171204029
- CHUDY-LASKOWSKA K., PISULA T. 2014. Forecast of the number of road accidents in Poland. Logistics, 6.
- CHUDY-LASKOWSKA K., PISULA T. 2015. Forecasting the number of traffic accidents in Subcarpathia. Logistics, 4.
- Data mining techniques. 2022. StatSoft. Retrieved from https://www.statsoft.pl/textbook/stathome_stat.html?https%3A%2F%2Fwww.statsoft.pl%2Ftextbook%2Fstdatmin.html
- DUDEK G. 2013. Exponential smoothing models for short-term power system load forecasting. Energy Market, 3(106).
- 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
- 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.
- FIJOREK K., MRÓZ K., NIEDZIELA K., FIJOREK D. 2010. Forecasting electricity prices on the day-ahead market using data mining methods. Energy Market, 12.
- FISZEDER P. 2009. GARCH class models in empirical financial research. Scientific Publishers of the Nicolaus Copernicus University, Toruń.
- Forecasting based on time series. 2022. PiS-W-5. pis.rezolwenta.eu.org. Retrieved from http://pis.rezolwenta.eu.org/Materialy/PiS-W-5.pdf
- Global status on road safety. 2018. World Health Organization. Retrieved from https://www.who.int/publications/i/item/9789241565684
- GORZELAŃCZYK P., JURKOVIČ M., KALINA T., MOHANTY M. 2022. Forecasting the road accident rate and the impact of the COVID-19 on its frequency in the polish provinces. Communications – Scientific Letters of the University of Zilina, 24(4). https://doi.org/10.26552/com.C.2022.4.A216-A231
- 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. https://doi.org/10.20858/sjsutst.2020.107.3
- 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
- JURKOVIC M., GORZELANCZYK P., KALINA T., JAROS, J., MOHANTY M. 2022. Impact of the COVID-19 pandemic on road traffic accident forecasting in Poland and Slovakia. Open Engineering, 12(1): 578-589. https://doi.org/10.1515/eng-2022-0370
- 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
- KASHPRUK N. 2010. Comparative research of statistical models and soft computing for identification of time series and forecasting. Opole University of Technology, Opole.
- 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. Electron, 8, https://doi.org/10.3390/electronics8080896
- 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
- LAVRENZ S., VLAHOGIANNI E., GKRITZA K., KE Y. 2018. Time series modeling in traffic safety research. Accident Analysis & Prevention, 117: 368–380.
- LI L, SHRESTHA S., HU G. 2017. Analysis of road traffic fatal accidents using data mining techniques. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), p. 363-370. https://doi.org/10.1109/SERA.2017.7965753
- ŁOBEJKO S. 2015. Time series analysis and forecasting with SAS. Main Business School, Warszawa.
- MAMCZUR M. 2022. Machine learning How does linear regression work? And is it worth using? Retrieved from https://miroslawmamczur.pl/jak-dziala-regresja-liniowa-i-czy-warto-ja-stosowac/
- 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, Poznań. Retrieved from http://www.wbc.poznan.pl/Content/373785/index.pdf
- 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 of actor maps. Accident Analysis & Prevention, 122: 8-18. https://doi.org/10.1016/j.aap.2018.09.011
- 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
- MUCK J. 2022. Econometrics. Modeling of time series. Stationary. Unit root tests. ARDL models. Co-integration. Retrieved from http://web.sgh.waw.pl/~jmuck/Ekonometria/EkonometriaPrezentacja5.pdf
- PERCZAK G., FISZEDER P. 2014. GARCH model – using additional information on minimum and maximum prices. Bank and Credit, 2.
- PIŁATOWSKA M. 2012. The choice of the order of autoregression depending on the parameters of the generating model. Econometrics, 4(38).
- 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.
- PROCHÁZKA J., FLIMMEL S., ČAMAJ M., BAŠTA M. 2017. Modelling the Number of Road Accidents. Wydawnictwo Universytetu Ekonomicznego, Wrocław. https://doi.org/10.15611/amse.2017.20.29
- RAJPUT H., SOM T., KAR S. 2015. An automated vehicle license plate recognition system. Computer, 48(8): 56-61. https://doi.org/10.1109/MC.2015.244
- Random forest. 2022. Wikipedia. Wolna encyklopedia. Retrieved from https://pl.wikipedia.org/wiki/Las_losowy
- Road safety assessment handbook. 2022. Retrieved from https://www.iung.pl/PJA/wydane/11/PJA11_3.pdf
- 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-series analysis. Accident Analysis & Prevention, 70: 33-39. https://doi.org/10.1016/j.aap.2014.02.017
- 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, 4.
- Statistic Road Accident. 2022. Retrieved from https://statystyka.policja.pl/
- SUNNY C.M., NITHYA S., SINSHI K.S., VINODINI V.M.D. LAKSHMI A.K.G., ANJANA S., MANOJKUMAR T.K. 2018. Forecasting of Road Accident in Kerala: A Case Study. 2018 International Conference on Data Science and Engineering (ICDSE). https://doi.org/10.1109/ICDSE.2018.8527825
- 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, 38.
- 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
- Top Advantages and Disadvantages of Hadoop 3 DataFlair. 2022. Retrieved from https://data-flair.training/blogs/advantages-and-disadvantages-of-hadoop/
- 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
- 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 in Katowice, 193.2014.
- 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
- ZHENG Z., WANG C., WANG P., XIONG Y., ZHANG F., LV Y. 2018. Framework for fusing traffic information from social and physical transportation data. PLoS One, 13. https://doi.org/10.1371/journal.pone.0201531
- 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6ce67f34-e71a-4fc8-8f7f-906496d01b8f
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