Kamyon sürücüsünün dahil olduğu yaralanmalı kazalara etki eden faktörlerin incelenmesi

Bu çalışma, lojistik regresyon kullanarak Kano, Nijerya'da kamyon sürücülerinin karıştıkları yaralanmalı kazaların oluşumuna katkıda bulunan faktörleri tanımlamayı amaçlamaktadır. Çalışmada, 248 kamyon şoförü ile anket yapılmış ve yaralanmalı kazalara katkıları ile ilgili bilgiler toplanmıştır. Anketin sonucndau, sürücülerin %52.8'inin profesyonel kariyerlerinde en az bir kez yaralanmalı kaza geçirdiklari ve sürücülerin %80’inin 40 yaşın altında olduğu görülmüştür. Regresyon analizinden elde edilen sonuçlara göre; ortalama gidilen mesafe/hafta (p=0.0057), ortalama sürüş süresi/günü (p=0.0232), direksiyonda uyumak (p=0.0004) ve yardımcı sürücünün olması (p=0.0003) faktörlerinin sürücünün yaralanmalı trafik kazasına karışmasına istatistiksel olarak anlamlı katkılarda bulunduğu anlaşılmıştır. Gidilen ortalama mesafe / hafta, direksiyonda uyumak ve yardımcı sürücünün olması’nın OR değerleri sırasıyla 1.80, 3.61 ve 3.94 değerlerine sahiptir ve yaralanma kazaları artırıcı yönde etkilediği saptanmıştır. Bu model; kamyon sürücülerinin yaralanmalı trafik kazalarına karışmalarını incelemede başarılı bulunmuş olup, modelin kabul edilebilir bir güven seviyesinde çalıştığı görülmüştür (%70). Sürücüler arasında seminerler ve düzenli eğitimler tertip edilerek trafik güvenliği bilincinin arttırılması, kamyon sürücülerinin yaralanmalı trafik kazalarına karışmalarını azaltmaya yardımcı olacaktır.

Anahtar Kelimeler:

Kaza, Yaralanmalı, Lojistik regresyon, Kamyon sürücüsü

Investigation of the factors contributing to truck driver’s involvement in an injury accident

The study was aimed at identifying the factors contributing to truck driver’s involvement in an injury accident in Kano, Nigeria using Logistic regression. 248 truck drivers were interviewed using a questionnaire and relevant information on their involvement in injury accident was collected. The result of the survey shows that 52.8% of the drivers were involved in an injury accident at least once in their professional carrier and 80% of the drivers were below the age of 40years. Results from the regression analysis found the Average distance traveled/week (p=0.0057), average driving hours/day (p=0.0232), sleeping on the wheel (p=0.0004), and presence of co-driver (p=0.0003) to be statistically significant in contributing to driver’s involvement in an injury accident. Average distance traveled/week, sleeping on wheel and presence of co-driver were found to positively affect involvement in injury accident with OR values of 1.80, 3.61 and 3.94 respectively. The model was found to be good in classifying truck drivers’ involvement in injury accident with a reasonable level of accuracy (70%). Increased safety awareness among drivers by conducting seminars and organizing training sessions regularly will help reduce the involvement of truck drivers in accidents.

Keywords:

Accident, Injury, Logistic regression, Truck driver,

PDF

___

Murat Y, Şekerler A. “Modelling traffic accident data by clustering approaches”. Technical Journal of Turkish Chambers of Civil Engineers, 20(3), 4759-4777, 2009.
Schneider R, Ryznar R, Khattak A. “An accident waiting to happen: a spatial approach to proactive pedestrian planning”. Accident Analysis and Prevention, 36(2), 193-211, 2004.
Federal Road Safety Corps. “2016 Federal Road Safety Corps Annual Report”. Federal Road Safety Corps, Abuja, Nigeria, 2017.
Talmage J, Hudson T, Hegmann K, Thiese M. “Consensus criteria for screening commercial drivers for obstructive sleep apnea: evidence of efficacy”. Journal of Occupational and Environmental Medicine, 50(3), 324-329, 2008.
Chen G, Amandus H, Wu N. “Occupational fatalities among driver/sales workers and truck drivers in the united states, 2003-2008”. American Journal of Industrial Medicine, 57(7), 800-809, 2014.
Kircher K, Andersson J. “Truck drivers’ opinion on road safety in Tanzania-a questionnaire study”. Traffic Injury Prevention, 14(1), 103-111, 2013.
Catarino R, Spratley J, Catarino I, Lunet N, Pais-Clemente M. “Sleepiness and sleep-disordered breathing in truck drivers”. Sleeping and Breathing, 18(1), 59-68, 2014.
Safe Work Australia. "Work-Related Traumatic Injury Fatalities, Australia". Safe Work Australia, Canberra, Australia, 2016.
Meuleners L, Fraser M, Govorko M, Stevenson M. “Determinants of the occupational environment and heavy vehicle crashes in western Australia: A case-control study”. Accident Analysis and Prevention, 99, 452-458, 2017.
Edwards J, Davey J, Armstrong K. “Profiling contextual factors which influence safety in heavy vehicle industries”. Accident Analysis and Prevention, 73, 340-350, 2014.
Garrido R, Bastos A, De Almeida A, Elvas J. “Prediction of road accident severity using the ordered probit model”. 17th Meeting of the EURO Working Group on Transportation, (EWGT 2014), Sevilla, Spain, 2-4 July 2014.
Savolainen P, Mannering F, Lord D, Quddus M. “The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives”. Accident Analysis and Prevention, 43(5), 1666-1676, 2011.
Batool I, Hussain G, Kanwal N, Abid M. “Identifying the factors behind fatal and non-fatal road crashes: a case study of lahore, Pakistan”. International Journal of Injury Control and Safety Promotion, 25(4), 1-7, 2018.
Karacasu M, Ergül B, Yavuz A. “Estimating the causes of traffic accidents using logistic regression and discriminant analysis”. International Journal of Injury Control and Safety Promotion, 21(4), 305-312, 2014.
Tay R, Rifaat S, Chin H. “A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes”. Accident Analysis and Prevention, 40(4), 1330-1336, 2008.
Filtness A, Armstrong K, Watson A, Smith S. “Sleep-related Vehicle Crashes on Low-Speed Roads”. Accident Analysis and Prevention, 99, 279-286, 2017.
Bunn T, Slavova S, Rock P. “Association between commercial vehicle driver at-fault crashes involving sleepiness/fatigue and proximity to rest areas and truck stops”. Accident Analysis and Prevention, 126, 3-9, 2019.
Alavi S, Mohammadi M, Souri H, Kalhory SM, Jannatifard F, Sepahbodi G. “Personality, driving behavior and mental disorders factors s predictors of road traffic accidents based on logistic regression”. Iranian Journal of Medical Sciences, 42(1), 24-31, 2017.
Pour-Rouholamin M, Jalayer M, Zhou H, “Modelling single-vehicle, single-rider motorcycle crash injury severity: an ordinal logistic regression approach”. International Journal of Urban Sciences, 21(3), 344-363, 2017.
Umar I, Bashir S, Gora A. “Road traffic accident in kano Nigeria: a case study of kano metropolitan”. Scholars Journal of Engineering and Technology, 5(12), 691-697, 2017.
Bichi A, Muhammad M. Studies on Labour and Production in Kano. Editors: Muhammad M, Mukhtar M, Adamu Y, Jimoh A, Daraja Y. Land, Labour, and Economy in Kano, 1-13, Zaria, Kaduna State, Nigeria, Ahmadu Bello University Press Limited, 2014.
Barau A. The Great Attractions of Kano. Kano, Nigeria, Research and Documentation Directorate, Government House Kano, 2007.
Lihua D, Xinhua Z, Qing W, You’an Y. “Logistic regression for prediction and diagnosis of bacterial regrowth in water distribution system”. Transactions of Tianjin University, 15(5), 371-374, 2009.
Chen H, Cao L, Logan D. “Analysis of risk factors affecting the severity of intersection crashes by logistic regression”. Traffic Injury Prevention, 13(3), 300-307, 2012.
He W, Goodkind D, Kowal P. “An AGING WORLD: 2015 International Population Reports”. United States Census Bureau, Washington DC, 2016.
World-Population-Review. “Nigeria Population 2018” http://worldpopulationreview.com/countries/nigeria-population/ (26.06.2018).
Davidović J, Pešić D, Antić B. “Professional drivers’ fatigue as a problem of the modern era”. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 199-209, 2018.
Lee D. “A Comparison of Choice-based landscape preference models between British and Korean visitors to national parks”. Life Science Journal, 10(2), 2028-2036, 2013.