Impact of information on the number of traffic accidents on the outcome of the forecast
Piotr Gorzelanczyk
Akademia Nauk Stosowanych im. Stanisława StaszicaAbstrakt
Every year, more and more vehicles appear on the world's roads. This leads to increased traffic on the roads. Road accidents have become a rapidly growing threat. They cause loss of human life and economic assets. This is due to the rapid growth of the world's human population and the very rapid development of motorization. The main problem in forecasting and analyzing data on the number of traffic accidents is the small size of the dataset that can be used for analysis in this regard. And on the other hand, road accidents cause, globally, millions of deaths and injuries annually is their density in time and space. It is worth noting that the pandemic has reduced the number of traffic accidents. However, the value is still very high.
The purpose of the article is to assess the impact of information on the number of traffic accidents on the outcome of the forecast. To this end, using historical statistical data, the forecast of the number of traffic accidents for the following years was determined, and how this variability of the input data affects the value of the average percentage error of the forecast was determined. Based on the study, it can be concluded that a smaller number of input data, historical data on the number of accidents, instead of 32 years, 7 years, makes the determination of the forecast of the number of accidents for subsequent years, is at a satisfactory level, the average absolute percentage error of MAPE less than 7%. The article concludes with the determination of the forecast for future years. It is worth noting that the prevailing pandemic distorts the results obtained.
Słowa kluczowe:
Forecasting, traffic accident, number of time series elements, mean absolute percentage error MAPEBibliografia
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
Crossref
Google Scholar
Bank Danych Lokalnych. Główny Urząd Statystyczny. Retrieved from https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica (access 21.01.2022). Google Scholar
BARTUSKA L., HANZL J., LIZBETINOVA L. 2016. Possibilities of Using the Data for Planning the Cycling Infrastructure. Procedia Engineering, 161: 282–289. https://doi.org/10.1016/j.proeng.2016.08.555
Crossref
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. In Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 6–8 July 2019.
Crossref
Google Scholar
CHUDY-LASKOWSKA K., PISULA T. 2014. Forecast of the Number of Road Accidents in Poland. Logistics, 6: 2710-2721. Google Scholar
ČUBRANIĆ-DOBRODOLAC M., ŠVADLENKA L., ČIČEVIĆ S., DOBRODOLAC M. 2020. Modelling driver propensity for traffic accidents: a comparison of multiple regression analysis and fuzzy approach. International Journal of Injury Control and Safety Promotion, 27(2): 156–167. https://doi.org/10.1080/17457300.2019.1690002
Crossref
Google Scholar
DUDEK G. 2013. Exponential smoothing models for short-term power system load forecasting. Energy Mark, 3: 14-19. 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. Google Scholar
FIJOREK K., MRÓZ K., NIEDZIELA K., FIJOREK D. 2010. Forecasting electricity prices on the day-ahead market using data mining methods. Energy Mark, 2010. Google Scholar
GORZELANCZYK P. 2022. Change in the Mobility of Polish Residents during the COVID-19 Pandemic. Communications – Scientific letters of the University of Zilina, 24(3): A100-111. doi: 10.26552/com.C.2022.3.A100-A111
Crossref
Google Scholar
GORZELANCZYK P. 2023a. Forecasting the number of road accidents in Poland using weather-dependent trend models. Technical Sciences, 26: 57–76. doi: https://doi.org/10.31648/ts.8289
Crossref
Google Scholar
GORZELANCZYK P. 2023b. Application of neural networks to forecast the number of road accidents in provinces in Poland. Heliyon, 9(1). https://doi.org/10.1016/j.heliyon.2022.e12767
Crossref
Google Scholar
GORZELANCZYK P. 2023c. Using neural networks to forecast the number of road accidents in Poland taking into account weather conditions. Results in engineering. In print.
Crossref
Google Scholar
GORZELANCZYK P. 2023d. Forecasting the Number of RoadAccidents in Polaish Provinces Using Trend Models. Applied Sciences, 13(5): 2898. https://doi.org/10.3390/app13052898
Crossref
Google Scholar
GORZELANCZYK P., BAZELA J. 2021. Analysis of pedestrian behavior at crosswalks and evaluation of functioning pedestrian crossings. Transport Problems, 16(4): 135-147.
Crossref
Google Scholar
GORZELAŃCZYK P., BAZELA J., KALINA T., JURKOVIČ M. 2022a. Analysis of pedestrian behavior in the city of Pila. Communications – Scientific Letters of the University of Žilina, 24(2): F14-F26.
Crossref
Google Scholar
GORZELAŃCZYK P., BORKOWSKA A., SZUBERT P., KALINA T., JURKOVIČ M. 2022b. Analysis and evaluation of the effectiveness of safety systems at railroad crossings in Poland. Communications – Scientific Letters of the University of Žilina, 24(3): F46-F61.
Crossref
Google Scholar
GORZELANCZYK P., HUK A. 2022. Road traffic safety : a case study of the Pila poviat in Poland. Scientific Journal of Silesian University of Technology. Series Transport, 114: 31-42.
Crossref
Google Scholar
GORZELANCZYK P., JURKOVIČ M., KALINA T., MOHANTY M. 2022c. 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): A216-231. https://doi.org/10.26552/com.C.2022.4.A216-A231
Crossref
Google Scholar
GORZELANCZYK P., JURKOVIČ M., SKIBIŃSKA J., KALINA T. 2022d. Road safety and the causes of road accidents in Poland. Transport Problems, 17(3): 17-30.
Crossref
Google Scholar
GORZELANCZYK P., KALINA T., JURKOVIČ M. 2022e. Impact of the COVID-19 Pandemic on Car-Sharing in Poland. Communications – Scientific letters of the University of Zilina, 24(4): A172-186. doi: 10.26552/com.C.2022.4.A172-A186
Crossref
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. ISSN: 0209-3324. https://doi.org/10.20858/sjsutst.2020.107.3
Crossref
Google Scholar
GORZELANCZYK P., TYLICKI H. 2023. Forecasting the number of road accidents in Poland depending on the day of the week using neural networks. LOGI – Scientific Journal on Transport and Logistics, 14(1): 35-42. https://doi.org/10.2478/logi-2023-0004
Crossref
Google Scholar
HELGASON A. 2016. Fractional integration methods and short Time series: Evidence from asimulation study. Political Analysis, 24(1): 59–68. http://www.jstor.org/stable/24573204
Crossref
Google Scholar
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: 578–589. https://doi.org/10.1515/eng-2022-0370
Crossref
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
Crossref
Google Scholar
KASHPRUK N. 2010. Comparative Research of Statistical Models and Soft Computing for Identification of Time Series and Forecasting. Opole University of Technology. Google Scholar
Las losowy. 2022. Wikipedia. Wolna encyklopedia. Retrieved from https://pl.wikipedia.org/wiki/Las_losowy (access 17.04.2022). 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
Crossref
Google Scholar
ŁOBEJKO S. 2015. Time Series Analysis and Forecasting with SAS. Main Business School, Warsaw. Google Scholar
MAMCZUR M. 2020. Jak działa regresja liniowa? I czy warto ją stosować? Mirosław Mamczur. Blog o data science, AI, uczeniu maszynowym i wizualizacji danych. Retrieved from https://miroslawmamczur.pl/jak-dziala-regresja-liniowa-i-czy-warto-ja-stosowac/ (access 17.04.2022). Google Scholar
MONEDERO B.D., GIL-ALANAA L.A., MARTÍNEZAA M.C.V. 2021. Road accidents in Spain: Are they persistent? IATSS Research, 45(3): 317-332. https://doi.org/10.1016/j.iatssr.2021.01.002
Crossref
Google Scholar
PIŁATOWSKA M. 2012. The choice of the order of autoregression depending on the parameters of the generating model. Econometrics, 4: 16–35. Google Scholar
Pojazdy samochodowe i motorowery zarejestrowane w Polsce, stan na 31 grudnia. 2023. Polski Związek Motorowy. Retrieved from https://www.pzpm.org.pl/content/download/2591/10569/file/park%20pojazdow%20PL%201990_2018.pdf (access 21.01.2023). Google Scholar
PROCHAZKA J., CAMAJ M. 2017. Modelling the number of road accidents of uninsured drivers and their severity. In Proceedings of International Academic Conferences 5408040, International Institute of Social and Economic Sciences, Geneva, Switzerland, 27 June 2017.
Crossref
Google Scholar
PROCHÁZKA J., FLIMMEL S., ČAMAJ M., BAŠTA M. 2017. Modelling the Number of Road Accidents. 20-th AMSE. Applications of Mathematics and Statistics in Economics. International Scientific Conference: Szklarska Poręba, 30 August – 3 September 2017, p. 355-364. https://doi.org/10.15611/amse.2017.20.29
Crossref
Google Scholar
Prognozowanie na podstawie szeregów czasowych. Retrieved from http://pis.rezolwenta.eu.org/Materialy/PiS-W-5.pdf (access 17.04.2022). Google Scholar
Statystyka – Portal polskiej Policji. Retrieved from https://statystyka.policja.pl/ (access 17.04.2022). Google Scholar
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. In Proceedings of the 2018 International Conference on Data Science and Engineering (ICDSE), Kochi, India, 7–9 August 2018. https://doi.org/10.1109/ICDSE.2018.8527825
Crossref
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, 38. Google Scholar
The Global Status on Road Safety. 2018. World Health Organization. Retrieved from https://www.who.int/publications/i/item/9789241565684 (access 17.04.2022). Google Scholar
WÓJCIK A. 2014. Autoregressive Vector Models as a Response to the Critique of Multi-Equation Structural Econometric Models. Studia Ekonomiczne, 193: 112-128. Google Scholar
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