Human Injury Causing Road Traffic Accident at Debre Markos Town

Introduction

A Road traffic accident (RTA) is a type of accident occurred at road when a Vehicle collides with another Vehicle, pedestrian, animal or geographical or architectural obstacle. The RTAs can result in injury, property damage and death. RTA is a cause of death for 1.2 million people worldwide each year, and injures about 4 times this number (WHO, 2004). In this study, a road traffic accident is defined as accident which took place on the road between two or more objects, one of which must be any kind of a moving Vehicle [1].

Road Traffic Accidents (RTAs) are increasing with rapid rate and presently these are one of the leading causes of death in developing countries, especially those in Africa comparing to motorized countries like North America. The morbidity and mortality burden in developing countries is rising due to a combination of factors, including rapid motorization, poor road and traffic infrastructure as well as the behavior of road users [2, 3]. However, in technologically advanced countries injuries are reducing [4, 5]. Due to lack of protection, drivers, passengers, and the involved pedestrians are more likely to sustain injury or fatality at the impact of traffic collision [6].

Human factors, including road user behavior and incapacitation, are the most common factors, accounting for more than 85% of all traffic crashes among them, the two key known contributing factors are speeding, and drinking and driving. Other contributing factors are unsafe vehicles, unsafe road design, and the related lack of effective law enforcement and safety regulations [7].

Shankar et al. (1995) explored the frequency of occurrence of highway accidents on the basis of a multivariate analysis (by negative binomial model) of road way geometries (e.g. horizontal and vertical alignments), weather and other seasonal effects [8]. The results of the analysis uncover important determinants of accident frequency.

Karlaftis and Golias (2002) studied the effects of road geometry and traffic volumes on rural roadway accident rates using Generalized linear modeling [9]. The results showed that although the importance of isolated variables differs between two-lane and multilane roads, ‘geometric design’ variables and ‘pavement condition’ variables are the two most important factors affecting accident rates.

The literature review shows that pedestrian crashes account for more than 40 percent of crashes in most of Africa countries. For example: pedestrians accounted for 55% & 46% of road traffic deaths in Mozambique & Ghana respectively [10, 11]. Pedestrian and passenger are victims account for more than half in Nigeria and Kenya [6, 12].

In Nigeria motor cyclist involved crashes are the second most common cause of road traffic injuries [6]. RTA resulted on injuries and death on productive family members and leads to poverty [13].

RTAs is increasing at alarm rate and become the second most common & accounted for 22.8% of all such incidents in Ethiopia. Among RTA causalities, 21.9% were drivers, 35.0% were passenger vehicle occupants and 36.0% were vulnerable road users including: motorcyclists (21.0%), pedestrians (12.1%) and cyclists (2.9%) [14, 15].

Pedestrians and passengers of commercial vehicles are the most vulnerable in Ethiopia. Factors like poor road network, absence of knowledge on road traffic safety, mixed traffic flow system, poor legislation and failure of enforcement, poor conditions of vehicles, poor emergency medical services, and absence of traffic accident compulsory insurance law have been identified as key determinants of the problem [16].

Amhara region accounted for 27.3% of the total road traffic accident related deaths in Ethiopia during the year 2008/9, it takes the highest share among all regions. Road traffic accident in Amhara region indicates that freight vehicles (51%) and passenger vehicles (34.5) are the main causes of accidents. And interstate highways takes 54.8% of the accidents occurred. Mainly pedestrians passengers were accounted for the largest part of road traffic deaths victims in the urban areas. This accident is mainly due to drivers problem of failure to give priority to pedestrians, failure to stay on the right side of the road, speeding, failure to maintain distance between vehicles and failure to yield the right of way for other vehicles [15]. This indicates the need to examine the cause of accidents on the region.

Recent report on Debre Markos town traffic accident indicated that there is an increase in accident and huge loss due to many factors. This enhances socio-economic instability in addition to physiological and physical damage. In response to this, road and transport authority office plan to work with other organizations, to have good behaved and well trained human resources, and based on information and studies developing awareness and work with the society, to achieve a gole of creating stable accident free transportation system. The approach is based on developing awareness of society on rules of road and transportation system, which helps the society to know and respect rules for the purpose of reducing car accident. In addition creating good behaved and well trained human resources is another input. It known that to reduce level and number of accident the influence of diver and related factors are unrepeatable hence much concern should have to give. However, the quality of drivers, cars, and road standard does not considered. As a result, this study attempts to investigate factors those contribute to road traffic accident involving human injuries, by considering literature suggested factors which are recorded by traffic police officers.

Main Text

Materials and Methods

Data Source

The Cross-sectional study is applied for the number of human injuries per Vehicle accident at Debre Markos town in a year from 2014 to 2019(1615 consecutive days). The data is taken from the record and report book of Debre Markos town traffic police. Accidents were recorded by the traffic police on daily basis for the purpose of reports and public service.

Missing Value Treatment

The data set is extracted from recorded document, and some values were incomplete or missed. So in ordered to treat such problem I preferred to use median value for categorical variable and exception maximization method for continuous variable.

Variables of the study

The response variable is number of human injuries per accident over a day. This includes the number of people dead, lightly injured or heavily injured due to a traffic accident. The considered predictors are Driver’s Age, Driver’s Education status, Driver’s experience, Vehicle type, Driver Vehicle Ownership, Vehicle year of service, Road type, Road division, Road condition, Problem with car, Weather condition, and accident time (day or night) [See Table S1].

Methods of data analysis

Variable selection

The analyses were started by considering the whole explanatory variables, and a significantly associated factor with a dependent variable is taken to check the causal effect on a response. Pearson chi-square is used to select which variable should be entered into reduced model. Finally, the log-likelihood of the full model and reduced model were compare to select the variables which stayed in the model.

Poisson Regression Model

In statistical analysis of the count of rare event, it is often assumed that the dependent variable follows a Poisson distribution, with the assumption of the mean (expected) count equal to its variance. In practice, the variance is much larger than the mean. This is often referred to as “over dispersion” with respect to the Poisson distribution [17].

The number of accidents occurred in a day follow a Poisson distribution with parameter λ, and for a given independent variables (x₁, x_2, ..., x_m ) the probability of an event occurred is modeled as:
 

Where the log of the mean occurrence (λ) is assumed to be a linear function of the independent variables. Which implies that λ is the exponential function of independent variables:

Result and Discussion

Descriptive Statistics

Descriptive statistics is used to compare and describe the magnitude of significantly associated factors using percentile. The result in Table S3 indicates that, explanatory variables which have significant association with number of human injure in road traffic accident are weather condition, driver’s experience, Vehicle year of service; Road division, Driver Vehicle Ownership, and accident time (day or night).

As the result indicated in Table S3, most of the accident were occurred in road type asphalt 78.8% comparing to the accident occurred on sandy road (10%) and cobble road (11.2%). Higher accident were encountered by Small Vehicles (Minibus, Bajaj, taxi and Toyota) (43.75%), followed by auto track (27.5%), Wagon and related (12.5%), Motorbike (8.75%), and Buses (7.5%). Most of the accident were occurred at day (85%) than at night (15%); during coldest season (47.5%) than hot season (46.25%) and rainy season (6.25%).

Frequently, higher accident were occurred in one way road (55%) than two way road; straight road (50%) than curved (16.2%) & slopping (33.3%); on suitable/comfortable road (72.5%) than not suitable road (27.5%), this result is supported by previous studies [3, 7, & 16].

The influence of education on car accident indicated that, a diver educated preparatory school takes the largest portion (28.75%), followed by educated with secondary school (23.75), MSc (15%), elementary education (12.5%), BSc (10%), & basic education (10%). The accident occurred by diver problem (56.2%) takes the larger proportion compared to technical problem (43.8%), this result is supported by previous studies [7 & 15]. Vehicle owner ship were also considered as a source of accident and it indicates that, employed diver takes the larger portion of accident (58.8%) than owners (41.2%).

Inferential Statistics

Poisson Regression is fitted for the full model and the reduced model by using only significantly associated explanatory variables (six variables). The result in Table S4 indicates that, even if the full model is significant, only two variables from 13 are significant. Whereas the result in Table 1 indicates that, from six significantly associated variables 3 variables (with one additional variable) have statistically significant causal effect on Vehicle accident.

Hence, fitting by using significantly associated 6 variables gave a better model comparing to the full model. As a result the Poisson model for Vehicle accident is as follow: Log (λ) = β0+ β1X1+ β2X2+ β3X3 = 0.818 + 0.091X1 - 0.022X2 + 0.102X3

Where X₁ is Vehicle year of service, X₂ is driver experience, X₃ is road division.

Based on the fitted model above: The odds of number of vehicle accident for one year older vehicle is 1.095 times higher. So as a vehicle becoming older the accident will increase. Older vehicles are more likely to cause an accident comparing to a new one. The odds of number of vehicle accident for one more year experienced drivers is 0.022 times lower. Un-experienced drivers are more likely to cause an accident comparing to a experienced drivers. The odds of vehicle accident is higher for one directional road (1.107 times) as compared to odds of two directional road [18-24].

                           Table 1: Poisson Regression Model using significantly associated variable

Conclusions

Among the candidate variables, Pearson Chi-Square method identified weather condition, driver’s experience, light condition, vehicle year of service, road division, and deriver-vehicle relationship as significantly associated variables. Furthermore, the percentile of associated variable indicated that, coldest season (47.5%), high year of driver’s experience, low Vehicle year of service, one way Road division (55%), employed diver (58.8%), and day accident time (85%) takes the larger portion of accident.

The result from Poisson regression reveals that driver experience, road division, and Vehicle year of service are the significantly causing factors which affect the number of human injuries. In order to reduce the number injury in Debre Markos Town, for effective and safe traffic management, the concerned transportation authorities can consider the above mentioned predictors as potential causes of accidents in their order of importance in order to take preventive measures. Specifically, controlling new driver, removing old Vehicles, and controlling one way road (or building two-way road) can help to reduce human injuries Vehicle accidents.

Limitation

This study is a cross-sectional study which does not consider the spatial and temporal analysis of the accident so further study on this aspect is advisable.

Abbreviations

RTA Road traffic accident

WHO World Health Organization

RTI Road traffic injuries

GLM Generalized Linear Model

Declarations

Ethics approval and consent to participate

The data is secondary data design for public use and report by Debre Markos town traffic police office, and any researcher can take and do a research. And formal ethical ethical approval is not required.

Consent for Publication

Author proves consent of publication for this research.

Availability of data and material

All important data and material are available. It is a free access data any researcher can take and make a study for the improvement of public service.

Competing interests

The author declare no competing interests regarding this paper.

Funding

No Funding available

Authors’ contributions

The research is performed by Wubetie, H.T. The author read and approved the final manuscript.

Acknowledgements

The author forwards his heartfelt gratitude to anonymous reviewers for their careful reading of the manuscript and their helpful comments that improve the presentation of this work.

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Appendix

Table S1: List of Variables