Analysis of Road Traffic Accidents in Nigeria: A Case Study of Obinze/Nekede/Iheagwa Road in Imo State, Southeastern, Nigeria

Ohakwe, J.; Iwueze, Iheanyi S.; Chikezie, D. C.

doi:10.3923/ajaps.2011.166.175

Cited by 20 publications

(21 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is an indication that admission occurs mostly during the festive periods where most people travel to their native homes to celebrate with their love ones. Our finding corroborates the findings of [14] where it was reported that "the incidences of the RTA are on the increase and characterized by seasonal factors as can be seen from the high values of the seasonal indices for the months of January, February, May, June, October, November and December". However, our result is at variance with the findings in [15] and [16] where they claimed that highest number of RTA occurs during the rainy season, whereas our study suggested the peak is during the dry seasons.…”

Section: Discussionsupporting

confidence: 82%

Time Series Analysis of Admission in the Accident and Emergency Unit of University College Hospital, Ibadan, Southwestern Nigeria

Abiona¹,

Adebowale²,

Fagbamigbe³

2012

AJCAM

View full text Add to dashboard Cite

Mortality resulting from accidents and late admission of patients into modern health facility constitute a high proportion of all deaths in the developing countries. Information on patterns of admission of Accident and Emergency (AE) patients is valuable to caregivers in AE department in meeting patients' seasonal needs. This retrospective study used gender-classified records of 79990 patients admitted into the AE unit between 1995 and 2006 in University College Hospital (UCH), Ibadan. We examined seasonal variation using trigonometric regression and moving average models. There exists significant difference in number of admission between males ( � = 306.63, = 69.56) and females ( � = 248.85, = 65.27).The analysis further showed that patient's admission peaked in May and minimal in November. Seasonal index showed that the peak of number of patients admitted was observed in the last quarter of every year. This is an indication that admission occurs mostly during the festive periods where people travel home to celebrate with their love ones. The projected quarterly admissions for 2011 are (Q1=1488, Q2=1497, Q3=1632, Q4=1634) and for 2012 are (Q1=1490, Q2=1499, Q3=1634, Q4=1635). The hospital management should engage more caregivers and make available more resuscitating medical equipments during last quarter of each year and peak periods.

show abstract

Section: Discussionsupporting

confidence: 82%

Time Series Analysis of Admission in the Accident and Emergency Unit of University College Hospital, Ibadan, Southwestern Nigeria

Abiona¹,

Adebowale²,

Fagbamigbe³

2012

AJCAM

View full text Add to dashboard Cite

show abstract

“…These components were also recognised as variations that exist in the time-series data [10]. This method is called classical decomposition of time-series.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The decomposition of time-series is a statistical method that deconstructs a time-series into notional components [10]. Some research related to time-series decomposition has been studied recently [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Some research related to time-series decomposition has been studied recently [9][10][11][12][13][14]. Ohakwe et al [10] analysed the traffic road accidents by using the method of time-series decomposition and found out an upward trend and significant seasonal influences between vehicles crashes. Wu et al [11] studied the trend, detrending, and variability of nonlinear and nonstationary time-series by using the Empirical Mode Decomposition (EMD) method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time-series identification of fatigue strain data using decomposition method

Nopiah

Lennie

Nuawi³

et al. 2014

AIP Conference Proceedings

View full text Add to dashboard Cite

This paper presents the time-series identification a variable-amplitude (VA) strain signal on lower arm suspension component in terms of time-series component analysing and correlate to the fatigue damage properties. The identification technique was used is called classical decomposition method, to classify the strain data into trend, cyclical, seasonal and irregular components. The time history plot of a study case showed the fatigue data contains high and low amplitude events and has resulted the highest amplitude for a pavé, highway and campus are 224 μ, 321 μ and 619 μ, respectively. The trend pattern of a fatigue strain data is a nonstationary series in variance and mean, where a campus data produced highest slope of 31.210 -4 compared to the others. By observing the cyclic movement of the moving average plot, the fatigue strain data contained expansion, contraction and random background. The autocorrelation plot is weak in identifying seasonal pattern, but the autocorrelation coefficient, r 1 values are statistically significant and show a positive serial correlation. Based on residual plot in irregular analysis, the residuals pattern is considered random. As to correlate the fatigue characteristic and time-series component, it was found a campus data produced highest value of fatigue damage. This study discovered a slope of a linear trend pattern could be affected to the fatigue damage properties because the fatigue strain data are nonstationary, VA time-series data and have a random background. Thus, the findings of these characteristics are expected for a nonstationary signal.

show abstract

“…Road traffic accidents in Nigeria involve both commercial driven vehicles as well as private vehicles. An analysis of road traffic accidents in Imo state, Nigeria showed that minibuses, private cars and taxis were responsible for about 94% of incidents (Ohakwe et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Tire Safety Knowledge among Private and Commercial Motor Vehicle Drivers in Nigeria

Emelike¹,

Mbanaso²,

Akunneh-Wariso³

et al. 2019

RHS

View full text Add to dashboard Cite

It is estimated that road traffic crashes account for the death of about 1.25 million people each year, and it is the leading cause of death among youth between ages 15-29 years (WHO, 2016). Low and middle-income countries have about half of the world's vehicle, and yet account for 90% of the world's fatal accidents (WHO, 2016). Among the key risk factors for road accidents are speed, drink-driving, motorcycle helmets, seat-belts and child restraints, distracted driving (WHO, 2016). Tires maintenance or factors relating to the tire were not identified as risk factors by WHO. However, in Nigeria, it was estimated that between the years 2011 and 2015, tire bursts accounted for 7.8% of total causes of traffic crash in Nigeria (FRSC, 2016).Objective: This research aims to assess whether private vehicle drivers have better tire safety knowledge than commercial vehicle drivers in Nigeria. Methodology: The research employed a cross sectional study design on 454 motor vehicle drivers (commercial vehicle drivers n=233 and private vehicle drivers n=221). A simple, well-structuredquestionnaire was applied to them and data collected. Data analysis: The result was analyzed using Statistical Package for Social Sciences (SPSS).Result: The result showed an evidence supporting a relationship between driver's category and possession of tire safety knowledge. We conclude therefore that private vehicle drivers in Nigeria are more likely to have adequate tire safety knowledge than commercial vehicle drivers.

show abstract

Analysis of Road Traffic Accidents in Nigeria: A Case Study of Obinze/Nekede/Iheagwa Road in Imo State, Southeastern, Nigeria

Cited by 20 publications

References 1 publication

Time Series Analysis of Admission in the Accident and Emergency Unit of University College Hospital, Ibadan, Southwestern Nigeria

Time Series Analysis of Admission in the Accident and Emergency Unit of University College Hospital, Ibadan, Southwestern Nigeria

Time-series identification of fatigue strain data using decomposition method

Assessment of Tire Safety Knowledge among Private and Commercial Motor Vehicle Drivers in Nigeria

Contact Info

Product

Resources

About