PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Application of the logistic regression model to assess the risk of death in road traffic accidents in the Mazowieckie voivodeship

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Mortality caused by road accidents is a significant problem for most countries, including Poland, where approximately 2,900 people die each year, and another 37,359 are injured. Research in this area has been conducted on a large scale. One of the most important elements is the evaluation of factors leading to fatalities in road accidents, which is also the goal of this article. The analysis was based on data on road accidents from the Mazowieckie Voivodeship, which is characterized by one of the highest mortality rates gathered for the period 2016-2018. Owing to the dichotomous form of the studied variable, logistic regression was used. Estimated model parameters and calculated odds ratios allowed to assess the effect of selected factors on road traffic mortality rate. As significant, the type of the perpetrator and the traffic participant, sex and age of the victim, road lighting, and the driver’s experience were selected. It was assessed that pedestrians are the group most exposed to death in a road accident, both as perpetrators and victims. It was also pointed out that the risk of death for women is 1.8 times higher than men. In addition, the effect of driving experience is also important, and the risk of death is 0.64 times lower for drivers with longer practice. It was also assessed that with each subsequent year of life, the risk of death in a road accident increased by 2%. Furthermore, according to incident site lighting, the study demonstrated that the risk of death was greatest when driving at night on an unlit road. The results obtained may support public safety and law enforcement authorities in carrying out preventive actions and also can be helpful in shaping the overall strategy on road safety.
Czasopismo
Rocznik
Strony
125--136
Opis fizyczny
Bibliogr. 34 poz.
Twórcy
autor
  • Military University of Technology, Faculty of Security, Logistics and Management, Kaliskiego 2, 00-908 Warsaw, Poland
  • Military University of Technology, Faculty of Security, Logistics and Management, Kaliskiego 2, 00-908 Warsaw, Poland
  • Motor Transport Institute, Jagiellońska 80, 03-301 Warsaw
Bibliografia
  • 1. Abdel-Aty, M. & Radwan, A.E. Modeling traffic accident occurrence and involvement. Accident
  • Analysis & Prevention. 2000. No. 32. P. 633-42.
  • 2. Ackaah, W. & Salifu, M. Crash prediction model for two-lane rural highways in the Ashanti region of Ghana. IATSS Research. 2011. No. 35. P. 34-40.
  • 3. Aguero-Valverde, J. Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates. Accident Analysis & Prevention. 2013. No. 50. P 289-97.
  • 4. Ahmed, L. A. Using logistic regression in determining the effective variables in traffic accidents. Applied Mathematical Sciences. 2017. Vol. 11. No. 42. P. 2047-2058.
  • 5. Al-ghamdi, A.S. Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis & Prevention. 2002. No. 34. P. 729-741.
  • 6. Anastasopoulos, P. Ch. & Mannering, F. L. A note on modeling vehicle accident frequencies with random-parameters. Accident Analysis and Prevention. 2009. No. 41. P. 153-159.
  • 7. Anastasopoulos, P.C. & Mannering, F. & Shankar V.N. & Haddock J.E. A study of factors affecting highway accident rates using the random-parameters tobit model. Accident Analysis and Prevention. 2012. No. 45. P. 628-633.
  • 8. Bijleveld, F.D. The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes. Accident Analysis and Prevention. 2005. No. 37. P. 591-600.
  • 9. Cafiso, S. & Di Graziano, A. & Di Silvestro, G. & La Cava, G. & Persaud, B. Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables. Accident Analysis and Prevention. 2010. No. 42. P. 1072-1079.
  • 10. Caliendo, C. & Guida, M. & Parisi, A. A crash-prediction model for multilane roads, Accident Analysis and Prevention. 2007. No. 39. P. 657-670.
  • 11. Chudy-Laskowska, K. & Pisula, T. Prognoza liczby wypadkow w Polsce. Logistyka. 2014. No. 6. P. 2710-2722.
  • 12. Council, F.M. & Harwood, D.W. & Hauer E. & Hughes, W.E. & Vogt, A. prediction of the expected safety performance of rural two-lane highways. Washington: Federal Highway Administration. 2000. 194 p.
  • 13. Deublein, M. & Schubert, M. & Adey, B.T. & Kohler, J. & Faber, M.H. Prediction of road accidents: A Bayesian hierarchical approach. Accident Analysis and Prevention. 2013. No. 51. P. 274-291.
  • 14. El-Basyounym, K. & Sayed, T. Accident prediction models with random corridor parameters. Accident Analysis and Prevention. 2009. No. 41. P. 1118-1123.
  • 15. Fernandes, A. & Neves, J. An approach to accidents modeling based on compounds road environments. Accident Analysis and Prevention. 2013. No. 53. P. 39-45.
  • 16. Guo, F. & Wang, X. & Abdel-Aty, M. Modeling signalized intersection safety with corridor-level spatial correlations. Accident Analysis and Prevention. 2010. No. 42. P. 84-92.
  • 17. Ivan, J.N. & Garder, P.E. & Deng, Z. & Zhang, C. The effect of segment characteristics on the severity of head-on crashes on two-lane rural highways. Connecticut: University of Connecticut, University of Maine. 2006. 69 p.
  • 18. Ivan, J.N. & Lord, D. & Washington, S.P. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention. 2005. No. 37. P. 35-46.
  • 19. Ivan, J.N. & Qin, X. & Ravishanker, N. Selecting exposure measures in crash rate prediction for two-lane highway segments. Accident Analysis and Prevention. 2004. No. 36. P. 183-191.
  • 20. Iyinam, A. & Iyinam, S. & Ergun, M. Analysis of relationship between highway safety and road geometric design elements: Turkish case. In: Traffic Forum. Dresden, 2003.
  • 21. Karacasu, M. & Ergul, B. & Altin Yavuz, A. Estimating the causes of traffic accidents using logistic regression and discriminant analysis. International Journal of Injury Control and Safety Promotion. 2014. No. 21. Vol. 4. P. 305-313.
  • 22. Khaniyev, T. & Baskir, M.B. & Gokpinar, F. & Mirzayev, F. Statistical distributions and reliability functions with type-2 fuzzy parameters. Eksploatacja i Niezawodność – Maintenance and Reliability. 2019. No. 21. Vol. 2. P. 268-274.
  • 23. Kozłowski, E. & Borucka, A. & Świderski, A. Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodność – Maintenance and Reliability. 2020. No. 22. Vol. 2. P. 192-200.
  • 24. Lee, J. & Mannering, F. Impact of roadside features on the frequency and severity of run-offroadway accidents: an empirical analysis. Accident Analysis and Prevention. 2002. No. 34. P. 149-161.
  • 25. Lemp, J.D. & Kockelman, K.M. & Unnikrishnan, A. Analysis of large truck crash severity using heteroskedastic ordered probit models. Accident Analysis and Prevention. 2011. No. 43. P. 370-380.
  • 26. Li, X. & Huang, H-Z. & Li, F. & Ren, L. Remaining useful life prediction model of the space station. Eksploatacja i Niezawodnosc – Maintenance and Reliability. 2019. No. 21. Vol. 3. P. 501-510.
  • 27. Ma, J. & Kockelman, K. M. & Damien, P. A multivariate Poisson-longnormal regresion model for prediction of crasch count by severity, using Bayesian methods. Accident Analysis and Prevention. 2008. No. 40. P. 964-975.
  • 28. Ma, Z. & Shao, C. & Ma, S. & Yue, H. Analysis ofthe Logistic Model for Accident Severity on Urban Road Environment, In: IV’09 IEEE Intelligent Vehicles Symposium. Xi’an, 2009.
  • 29. Mannering, F. & Venkataraman, S. & Woodrow, B. Statistical analysis of accident rural freeways. Accident Analysis and Prevention. 1996. No. 28. P. 391-401.
  • 30. Migut, G. Kreator regresji logistycznej. Kraków: Statsoft Polska. 2011. 13 p.
  • 31. Shakaya, R. & Marsani, A. Using logistic regression to estimate the influence of crash. Accident Analysis and Prevention. 2002. No. 34. Vol. 6. P. 729-741.
  • 32. Shankar, V.N. & Ulfarsson, G.F. & Pendyala, R.M. & Nebergall, M.B. Modeling crashes involving pedestrians and motorized traffic. Safety Science. 2003. No. 41. P. 627-640.
  • 33. Stanisz, A. Modele regresji logistycznej. Zastosowanie w medycynie, naukach przyrodniczych i społecznych. [In Polish: Logistic regression models. Application in medicine, natural and social sciences]. Kraków: StatSoft Polska. 2016. 708 p.
  • 34. Świderski, A. & Borucka, A. Mathematical Analysis of Factors Affecting the Road Safety in Selected Polish Region. In: Proceedings of the 22nd International Scientific Conference “Transport Means” part II. Kaunas, 2018.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7e556844-9277-4dd6-a36f-914120d03796
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.