PRACA ORYGINALNA
Estimating the number of accidents on Poland's roads based on the kind of road
Więcej
Ukryj
1
Transport, Stanisław Staszic State University of Applied Sciences in Piła
Data nadesłania: 27-06-2023
Data ostatniej rewizji: 08-01-2024
Data akceptacji: 18-01-2024
Data publikacji: 20-12-2024
Autor do korespondencji
Piotr Gorzelańczyk
Transport, Stanisław Staszic State University of Applied Sciences in Piła
The Archives of Automotive Engineering – Archiwum Motoryzacji 2024;106(4):5-17
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
On Polish highways, a staggering number of individuals pass away each year. The quantity is still quite large even if the value is declining year after year. The value of traffic accidents has greatly decreased due to the epidemic, but it is still quite high. In order to reduce this number, it is required to identify the roads where the majority of accidents occur and to understand the predicted number of accidents in the upcoming years.
The article's goal is to predict how many accidents will occur on Polish roads based on the kind of roads. In order to achieve this, yearly data from the Police's statistics for the years 2001–2021 on the number of accidents in Poland were analyzed, and a prediction for the years 2022–2024 was generated. As is evident, either the number of accidents is rising or it is stabilizing. This is mostly caused by the rise in automobile traffic.
Additionally, predictions indicate that given the existing circumstances, a significant rise in the number of accidents on Polish roads may be anticipated. This is especially evident on the nation's growing number of freeways. It should be remembered that the current epidemic distorts the findings. Selected time series models were used in the investigation in Statistica.
REFERENCJE (36)
1.
Al-Madani H. Global road fatality trends’estimations based on country-wise microlevel data. Accident Analysis & Prevention. 2018;111:297–310.
https://doi.org/10.1016/j.aap.....
2.
Arteaga C, Paz A, Park J. Injury severity on traffic crashes: A text mining with an interpretable machinelearning approach. Safety Science. 2020;32:104988.
https://doi.org/10.1016/j.ssci....
3.
Bąk I, Cheba K, Szczecińska B. The statistical analysis of road traffic in cities of Poland. Transportation Research Procedia. 2019;39:14–23.
https://doi.org/10.1016/j.trpr....
5.
Chand A, Jayesh S, Bhasi AB. Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today: Proceedings. 2021;7(15):5135–5141.
https://doi.org/10.1016/j.matp....
7.
Chudy-Laskowska K, Pisula T. Forecasting the number of road accidents in Subcarpathia. (Prognozowanie liczby wypadków drogowych na Podkarpaciu. In Polish). Logistics. 2015;4:2782–2796.
8.
Chudy-Laskowska K, Pisula T. Forecastof Road Accidents in Poland. (Prognoza liczby wypadków drogowych w Polsce. In Polish). Logistics. 2014;6:2710–2721.
10.
Dutta B, Barman MP, Patowary AN. Application of Arima model for forecasting road accident deaths in India. International Journal of Agricultural and Statistical Sciences. 2020;16(2):607–615.
11.
Fiszeder P. GARCH class models in empirical financial research. (Modele klasy GARCH w empirycznych badaniach finansowych. In Polish). Scientific Publishers of the Nicolaus Copernicus University. Torun, 2009.
12.
Gorzelańczyk P. Change in the Mobility of Polish Residents during the Covid-19 Pandemic. Communications - Scientific letters of the University of Zilina. 2022;24(3):A100–111.
https://doi.org/10.26552/com.C....
13.
Gregorczyk A, Swarcewicz M. Analysis of variance in a repeated measures design to determine the effects of factors affecting linuron residues in soil. Polish Journal of Agronomy. 2012;11:15–20.
14.
Helgason A. Fractional integration methods and short Time series: evidence from a simulation study. Political Analysis. 2016;24(1):59–68.
https://doi.org/10.1093/pan/mp....
15.
Karlaftis M, Vlahogianni E. Memory properties and fractional integration in transportation time -series. Transportation Research Part C: Emerging Technologies. 2009;7(4):444–453.
https://doi.org/10.1016/j.trc.....
16.
Kashpruk N. Comparative research of statistical models and soft computing for identification of time series and forecasting. (Badania porównawcze modeli statystycznych i obliczeń miękkich dla identyfikacji ciągów czasowych i prognoz. In Polish). Opole University of Technology. Opole, 2010.
17.
Khaliq KA, Chughtai O, Shahwani A, Qayyum A, Pannek J. Road accidents detection, data collection and data analysis using V2X communication and edge/cloud computing. Electronics. 2019;8(8):896.
https://doi.org/10.3390/electr....
18.
Kumar S, Viswanadham V, Bharathi B. Analysis of road accident. IOP Conference Series Materials Science and Engineering. 2019;590(1):012029.
https://doi.org/10.1088/1757-8....
19.
Lavrenz S, Vlahogianni E, Gkritza K, Ke Y. Time series modeling in traffic safety research. Accident Analysis & Prevention. 2018;117:368–380.
https://doi.org/10.1016/j.aap.....
20.
Łobejko S. Time series analysis and forecasting with SAS. (Analiza i prognozowanie szeregów czasowych z wykorzystaniem narzędzi SAS. In Polish). Main business school in Warsaw. Warsaw, 2015.
21.
McIlroy RC, Plant KA, Hoque MS, Wu J, Kokwaro GO, Nam VH, et al. Who is responsible for global road safety? A cross-cultural comparison of actor maps. Accident Analysis & Prevention. 2019;122:8–18.
https://doi.org/10.1016/j.aap.....
23.
Perczak G, Fiszeder P. The GARCH Model - the Application of Additional Information about Low and High Prices (Model GARCH - wykorzystanie dodatkowych informacji o cenach minimalnych i maksymalnych. In Polish). Bank and Credit. 2014;45(2):105–132.
24.
Prochazka J, Camaj M. Modelling the number of road accidents of uninsured drivers and their severity. Proceedings of International Academic Conference. Geneva, 2017.
https://doi.org/10.20472/IAC.2....
25.
Procházka J, Flimmel S, Čamaj M, Bašta M. Modelling the Number of Road Accidents. Publishing house of the University of Economics in Wrocław. Wrocław, 2017:355–364.
https://doi.org/10.15611/amse.....
27.
Sebego M, Naumann RB, Rudd RA, Voetsch K, Dellinger AM, Ndlovu C. The impact of alcohol and road traffic policies on crash rates in Botswana, 2004–2011: A time-series analysis. Accident Analysis & Prevention. 2014;70:33–39.
https://doi.org/10.1016/j.aap.....
28.
Shetty P, Sachin PC, Kashyap VK, Madi V. Analysis of road accidents using data mining techniques. Inernational Research Journal of Engineering and Technology. 2017;04.
30.
Tambouratzis T, Souliou D, Chalikias M, Gregoriades A. 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. 2014;4(1):31–42.
https://doi.org/10.2478/jaiscr....
31.
Vilaça M, Silva N, Coelho MC. Statistical analysis of the occurrence and severity of crashes involving vulnerable road users. Transportation Research Procedia. 2017;27:1113–1120.
https://doi.org/10.1016/j.trpr....
33.
Wójcik A. Vector Autoregression Models (VAR) - Response to Criticism Structural Econometric Models (Modele wektorowo-autoregresyjne jako odpowiedź na krytykę strukturalnych wielorównaniowych modeli ekonometrycznych. In Polish). Economic Studies. 2014;193:112–128.
34.
Yang Z, Zhang W, Feng J. Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework. Safety Science. 2022;146:105522.
https://doi.org/10.1016/j.ssci....
35.
Zheng Z, Wang C, Wang P, Xiong Y, Zhang F, Lv Y. Framework for fusing traffic information from social and physical transportation data. PLoS One. 2018;13(8):e0201531.
https://doi.org/10.1371/journa....
36.
Zhu L, Lu L, Zhang W, Zhao Y, Song M. Analysis of accident severity for curved roadways based on bayesian networks. Sustainability. 2019;11(8):2223.
https://doi.org/10.3390/su1108....