Efficacy of Drivers’ Fatigue on Road Accident in Selected Southwestern States of Nigeria

Aworemi, Joshua Remi; adegoke, Abdul-azeez ibraheem; Oyedokun, Akintunde Jonathan; Adewoye, J. O.

doi:10.5539/ibr.v3n3p225

Cited by 13 publications

(10 citation statements)

References 17 publications

(10 reference statements)

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“…Sleepiness is one of the most important causes of fatal road accidents 4 . A study was conducted in Norway on 29,600 drivers who had involved in an accident.…”

Section: Introductionmentioning

confidence: 99%

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Sleep quality in long haul truck drivers: A study on Iranian national data

Sadeghniiat-Haghighi

Yazdi

Kazemifar

2016

Chinese Journal of Traumatology

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PurposeIran has a high rate of road traffic accidents. Poor quality of sleep brings about loss of attention, which is an important cause of road traffic accidents particularly in monotonous roads. The causes of poor quality of sleep in occupational drivers are multifactorial. The objective of the present study was to assess the prevalence of poor sleep quality among occupational drivers with rotating work schedules and analyze its different risk factors.Methods2200 professional long-haul truck drivers who had been referred to the Occupational Health Clinic for routine education course were invited. We obtained data from eight provinces from various parts of Iran during 2012–2013. Data were collected using a questionnaire including questions about demographic and job characteristics. Pittsburg Sleep Quality Index (PSQI) was used to assess drivers' sleep quality.ResultsMean working (driving) time was (9.3 ± 2.5) hours daily and (55.5 ± 18.29) hours weekly. About 23.5% of the drivers reported history of smoking, 14.5% had low job satisfaction and 60% had irregular work schedule. 16.4% of drivers had an accidents leading to injury during the past five years. The mean PSQI score was 4.2 ± 2.7; 54% had a PSQI>5 (poor quality of sleep). Multivariate logistic regression showed that smoking, job satisfaction, history of accidents, shift work and work hours per day were the most important risk factors for poor sleep quality.ConclusionResults obtained from the current study showed a high prevalence of poor quality of sleep among professional drivers. It warrants more attention to this significant problem using some measures to improve working conditions in professional drivers, as well as health promotion interventions.

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“…Sleepiness is one of the most important causes of fatal road accidents 4 . A study was conducted in Norway on 29,600 drivers who had involved in an accident.…”

Section: Introductionmentioning

confidence: 99%

“…Sleepiness is one of the most important causes of fatal road accidents. 4 A study was conducted in Norway on 29,600 drivers who had involved in an accident. According to the data from this study, drowsiness was a contributing factor in 3.9% of all accidents.…”

Section: Introductionmentioning

confidence: 99%

Sleep quality in long haul truck drivers: A study on Iranian national data

Sadeghniiat-Haghighi

Yazdi

Kazemifar

2016

Chinese Journal of Traumatology

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“…Another possible contributing factor in the Namibian context is that HV drivers often drive long distances. Lack of quality sleep and severe fatigue are significantly associated with more frequent human errors (Aworemi et al 2010;Gastaldi et al 2014). Fatigue itself is difficult to measure and to prove as a contributory cause in crashes, but even so, should not be overlooked as a potentially important element in explaining the relationship between HV presence and high severity crashes on HORRs.…”

Section: Discussionmentioning

confidence: 99%

Traffic safety and the rural road environment: Assessing the impact of combined roadway conditions on crash incidence

Ambunda¹,

Sinclair²

2022

J. S. Afr. Inst. Civ. Eng.

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This study was designed to explore the combinational effect of the roadway and traffic characteristics of national rural roads on fatal and serious-injury crashes in Namibia. Namibia, like many other countries in Southern Africa, experiences high numbers of high-severity crashes, particularly on its rural road network. The study applied Generalised Poisson regression models to analyse the study dataset. The results indicate that high-severity crashes are almost always influenced by a combination of factors, not simply the presence of a single factor. For example, on higher-order roads, a higher proportion of heavy vehicles in the traffic stream had the highest absolute impact on high-severity crash levels, but this effect was heightened when hilliness, wider lanes, surfaced shoulders and higher operating speeds were also present at the same time. On lower-order roads a higher proportion of light vehicles in the traffic stream had the highest absolute impact on high-severity crashes, and again this was compounded in the presence of wider unpaved shoulder widths and higher operating speeds. Overall, wider lane widths and wider unpaved shoulder widths, especially when found in combination with each other, were linked to a subsequent increase in higher-severity crash levels on higher-order roads. The study shows conclusively that the development of safer roads relies not on simply implementing minimum safety standards for individual design elements, but on understanding the relationship between features of road design, traffic parameters and road safety, to recognise which coincidences of factors affect crash risks on different classes of road. This knowledge can result in more careful road design so that crash likelihood is reduced.

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“…Detailed study of the literature on accidents, regression models was carried out (Adanu and Jones, 2017;Afework and Sipos, 2020;Alkheder et al, 2017;Basu and Saha, 2017;Jovanis and Chang, 1986;Kumar and Jain, 2021;Roy et al, 2017;Sipos et al, 2021;Valli et al, 2005). Along with regression models, principal component analysis and artificial neural network was also studied (Alqatawna et al, 2021;Aworemi et al, 2010;McFadden et al, 2001;Xue and Weng, 2014;Yeole et al, 2022;Yuan et al, 2020;Zheng and Meng, 2011).…”

Section: Introductionmentioning

confidence: 99%

Accident Prediction Modeling for Indian Metro Cities

Naidu,

Kunisetti,

Babu

et al. 2023

Period. Polytech. Transp. Eng.

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Road accidents are one of the biggest concerns to the road safety of developing nations. In India, around 150,000 fatal accidents occur annually. Road accident prediction models help in accessing the factors responsible for and those that contribute more to accidents. Most of the prediction models focus on the parameters like road characteristics, traffic characteristics, driver characteristics, and road geometrics. In this study, we considered socio-economic and land-use parameters as input data for accident prediction modeling. The socio-economic and land-use variables data of 20 Indian metro cities were collected. The data were collected for a period of 5 years ranging from 2016 to 2020. A multiple linear regression model was developed between the total number of accidents that happened in the 20 metro cities and the socio-economic and land-use variables. ANN model was also developed to check its applicability to this study and the results obtained are satisfactory.

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Efficacy of Drivers’ Fatigue on Road Accident in Selected Southwestern States of Nigeria

Cited by 13 publications

References 17 publications

Sleep quality in long haul truck drivers: A study on Iranian national data

Sleep quality in long haul truck drivers: A study on Iranian national data

Traffic safety and the rural road environment: Assessing the impact of combined roadway conditions on crash incidence

Accident Prediction Modeling for Indian Metro Cities

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