Influence of meteorological conditions on road accidents. A model Indexed by: for observations with excess zeros

• Autorzy: Borucka Anna , Pyza Dariusz

Identyfikatory

DOI

10.17531/ein.2021.3.20

• Języki publikacji: EN

Abstrakty

EN

Road accidents are one of the basic road safety determinants. Most research covers large territorial areas. The results of such research do not take into account the differences between individual regions, which often leads to incorrect results and their interpretation. What makes it difficult to conduct analyses in a narrow territorial area is the small number of observations. The narrowing of the research area means that the number of accidents in time units is often very low. There are many zero observations in the data sets, which may affect the reliability of the research results. Such data are usually aggregated, which leads to information loss. The authors have therefore applied a model that addresses such problems. They proposed a method that does not require data aggregation and allows for the analysis of sets with an excess of zero observations. The presented model can be implemented in different territorial areas.

Słowa kluczowe

EN

road accidents; excess zeros; road safety; meteorological conditions

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 3
• Strony: 586--592
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Borucka Anna

• Military University of Technology, Faculty of Security, Logistics and Management, ul. gen. Sylwestra Kaliskiego 2, 00 – 908 Warsaw, Poland

autor

Pyza Dariusz

• Warsaw University of Technology, Faculty of Transport, pl. Politechniki 1, 00-661 Warsaw, Poland

Bibliografia

• 1. Aga MA, Woldeamanuel BT, Tadesse M. Statistical modeling of number of human deaths per road traffic accident in Oromia region, Ethiopia. PLoS one 2020; 16(5) e0251492, https://doi.org/10.1371/journal.pone.0251492.
• 2. Al-Balbissi AH. Role of gender in road accidents. Traffic Injury Prevention 2003; 4(1): 64-73, https://doi.org/10.1080/15389580309857:
• 3. Ansari S, Akhdar F, Mandoorah M, Moutaery K. Causes and effects of road traffic accidents in Saudi Arabia. Public health 2000; 114(1): 37-39, https://doi.org/10.1038/sj.ph.1900610.
• 4. Ashraf I, Hur S, Shafiq M. Park Y. Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis. PLoS one 2019; 14(10), e0223473, https://doi.org/10.1371/journal.pone.0223473.
• 5. Borucka A, Kozłowski E, Oleszczuk P, Świderski A. Predictive analysis of the impact of the time of day on road accidents in Poland. Open Engineering 2020; 11(1): 142-150, https://doi.org/10.1515/eng-2021-0017.
• 6. Edwards JB. The temporal distribution of road accidents in adverse weather. Meteorological Applications: A Journal of Forecasting, Practical Applications, Training Techniques and Modelling 1999; 6(1): 59-68, https://doi.org/10.1017/S1350482799001139.
• 7. Elvik R, Mysen A. Incomplete accident reporting: meta-analysis of studies made in 13 countries. Transportation Research Record 1999; 1665(1), 133-140, https://doi.org/10.3141/1665-18.
• 8. Frej D, Ludwinek K. Analysis of road accidents in 2002-2019 on the example of Poland. The Archives of Automotive Engineering - Archiwum Motoryzacji 2020; 89(3): 5-18.
• 9. Golias J, Yannis G. Dealing with lack of exposure data in road accident analysis. In 12th 557 International Conference: Traffic Safety on Three Continents 2001, September; 558.
• 10. Guzek M, Lozia Z. Computing Methods in the Analysis of Road Accident Reconstruction Uncertainty. Archives of Computational Methods in Engineering 2020; 28: 2459-2476, https://doi.org/10.1007/s11831-020-09462-w.
• 11. Jacyna M, Merkisz J. Proecological approach to modelling traffic organization in national transport system. Archives of Transport 2014; 30(2): 31-41, https://doi.org/10.5604/08669546.1146975.
• 12. Jamroz K, Budzyński M, Romanowska A, Żukowska J, Oskarbski J, Kustra W. Experiences and challenges in fatality reduction on polish roads. Sustainability 2019; 11(4): 959, https://doi.org/10.3390/su11040959.
• 13. Kasin JA, Papastathopoulos I. A spatial Poisson hurdle model with application to wildfires. arXiv preprint arXiv:2007.00137, 2020.
• 14. Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159, 113600, https://doi.org/10.1016/j.eswa.2020.113600.
• 15. Lukusa MT, Phoa FKH. A Horvitz-type estimation on incomplete traffic accident data analyzed via a zero-inflated Poisson model. Accident Analysis & Prevention 2020; 134, 105235, https://doi.org/10.1016/j.aap.2019.07.011.
• 16. Martin JL, Gadegbeku B, Wu D, Viallon V, Laumon B. Cannabis, alcohol and fatal road accidents. PLoS one 2017; 12(11), e0187320, https://doi.org/10.1371/journal.pone.0187320.
• 17. Mitkow S, Świderski A. Regression Model In Road Transport Services. Business Logistics in Modern Management 2019; 19: 263-275
• 18. Pourhassan MR, Raissi S, Hafezalkotob A. A simulation approach on reliability assessment of complex system subject to stochastic degradation and random shock. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (2): 370-379, https://doi.org/10.17531/ein.2020.2.20.
• 19. Pyza D, Jachimowski R. Modelling of Parcels' Transport System. Proceedings of the International Scientific Conference Transport Means 2015; 22-23.
• 20. Pyza D, Jacyna-Gołda I, Gołda P, Gołebiowski P. Alternative Fuels and Their Impact on Reducing Pollution of the Natural Environment. Annual Set The Environment Protection 2018; 20: 819-836.
• 21. Rolison JJ, Regev S, Moutari S, Feeney A. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers' opinions, and road accident records. Accident Analysis & Prevention 2018; 115: 11-24, https://doi.org/10.1016/j.aap.2018.02.025.
• 22. Sagberg F. Road accidents caused by drivers falling asleep. Accident Analysis & Prevention 1999; 31(6): 639-649, https://doi.org/10.1016/S0001-4575(99)00023-8.
• 23. Singh SK. Road traffic accidents in India: issues and challenges. Transportation Research Procedia 2017; 25: 4708-4719, https://doi.org/10.1016/j.trpro.2017.05.484.
• 24. Świderski A, Borucka A, Skoczyński P. Characteristics and Assessment of the Road Safety Level in Poland with Multiple Regression Model, Transport Means - Proceedings of the International Conference 2018; Part I : 92-97.
• 25. Świderski A. Decision-Making Problems in the Standardization and Certification of Transport Services, Wydawnictwa Komunikacji i Łączności, 2019.
• 26. Świderski A. Modeling of transport processes in terms of seasonality of transport. Scientific Journal of Polish Naval Academy 2019; 216(1):103-116, https://doi.org/10.2478/sjpna-2019-0008.
• 27. Taylor MC, Lynam DA, Baruya A. The effects of drivers' speed on the frequency of road accidents. Crowthorne: Transport Research Laboratory, 2000.
• 28. Trépanier M, Leroux MH, de Marcellis-Warin N. Cross-analysis of hazmat road accidents using multiple databases. Accident Analysis & Prevention, 2009; 41(6): 1192-1198, https://doi.org/10.1016/j.aap.2008.05.010.
• 29. Wang C, Quddus MA, Ison SG. Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England. Accident Analysis & Prevention, 2009; 41(4): 798-808, https://doi.org/10.1016/j.aap.2009.04.002.
• 30. Zhang X, Yi N. Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data. Bioinformatics 2020; 36(8): 2345-2351, https://doi.org/10.1093/bioinformatics/btz973.
• 31. Zuur AF. Zero inflated models and generalized linear mixed models with R, 2012: 574.50182 Z8.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).