Analyzing road traffic accidents through identification and prioritization of accident-prone areas on the dembecha to injibara highway segment in amhara region, ethiopia

RTA severity and factors on the study road

Further discussion about road traffic accident scenarios, along with tabulation and a graph based on the collected data, is presented below.

As indicated in Table 1, there were 228, 91, 39, and 79 instances of fatal, serious, slight, and property damage, respectively, recorded during the study period. It was observed that fatality was the most prevalent among the various types of road traffic accident (RTA) severities.

Figure 3 illustrates an annual increase in the total number of road traffic accidents. Specifically, in 2018, 2019, 2020, and 2021, the severities of road traffic accidents were 92, 96, 113, and 136, respectively.

Table 2 shows that 85.13% of road accidents were caused by male drivers. Among these, approximately 43.02% (188) were fatal accidents, 16.9% (74) were serious injuries, 8.9% (39) were slight injuries, and 16.25% (71) were property damages. In contrast, the casualties from road traffic accidents involving female drivers were minimal, ranging from 0 to 3.66% in terms of RTA severity.

Table 3 provides a breakdown of the age distribution of drivers and their impact on the levels of road traffic accidents (RTAs). It indicates that approximately 3.66%, 54.23%, 21.74%, and 2.29% of reported driver casualties were aged under 18, 18–30, 31–50, and over 50 years, respectively. Among driver accident victims, those under 18 were the least affected age group, likely due to the minimum age requirement of 18 for acquiring a driver’s license. The study concluded that the number of drivers who died or sustained serious or light injuries decreased as the drivers’ age increased beyond 50 years, and the highest proportions of drivers involved in accidents fell between the ages of 18 and 50 as illustrated in Table 5. This pattern suggests that older drivers are more conscientious about traffic safety compared to those under 50. It is conceivable that older drivers, having more experience, are inclined to take greater responsibility than their younger counterparts. The causes of death among road users (drivers, pedestrians, and passengers) by age and sex on the study road are discussed below.

As can be seen in Table 4 and the results depicted in Fig. 4 above display the distribution of road traffic accident (RTA) victims by age group and sex. The data indicate that pedestrians are more susceptible to traffic accidents than passengers and drivers. Pedestrians aged 18–30 and 31–50 years accounted for the highest percentage of accident victims among age groups under 7, 7–13, 14–17, and over 51 years. Drivers, Pedestrians, and Passenger Casualties by Age and Sex.

Drivers, Pedestrians and Passengers’ Casualties in this Road section by Age.

Overall, road accident victims (drivers, pedestrians, and passengers) in the 18–30 age group were the most affected during the period studied, followed by those in the 31–50 age group. Conversely, road users under 7 years old were the least common victims of RTAs on this road section. Additionally, as depicted in Table 4, the analysis of road user RTA severity indicates that male passengers outnumber female passengers at all severity levels. Alarmingly, the most affected road users were in the younger productive age groups during this period, specifically those aged 18 to 30 years, followed by those aged 31 to 50 years, as shown in Fig. 3. The trends of RTAs in all three categories deaths, serious injuries, and slight injuries for both sexes in the 18–30 and 31–50 age categories increased, as indicated in Table 4; Fig. 4 above.

Figure 5 and the corresponding statements below illustrate the effect of the days of the week on traffic accident occurrence.

RTAs (%) vs. Day of the Week.

Analysis of RTA severity on the study road by day of the week revealed accidents occurring on all days. Notably, there were a significant number of accidents, 23.34% and 15.56%, on Saturday and Thursday, respectively; however, Sunday had a relatively low number of RTAs.

In general, the analysis confirmed that more accidents occurred on Thursday and Saturday. These days are market days, resulting in increased vehicular, passenger, and pedestrian movements (refer to Fig. 5 above).

Driver experience is a significant factor influencing road traffic accident (RTA) severity. Therefore, based on the accumulated experiences of drivers, the factors and effects on RTAs are discussed below. Table 5 below presents RTA severity based on driver experience.

According to Table 5, high levels of road traffic accidents (RTAs) occurred across all severity levels when drivers had 2–5 years of experience (29.52%). This suggests that young drivers with moderate experience may drive irresponsibly and carelessly due to a lack of concern and overconfidence that doesn’t take into account the consequences of RTAs. As a result, young drivers are frequently involved in collisions. In contrast, older drivers with extensive experience, who have family responsibilities and drive their own vehicles, tend to drive more cautiously, reducing their risk of collision.

The causes of RTA severity can lead to increased severity for a particular road segment. Table 6 below presents the causes of RTAs and their severity.

Based on the results presented in Table 6, the main contributors to road traffic accident (RTA) severity include speeding, failure to yield to pedestrians, overtaking, overloading, failure to yield to other drivers, pedestrians being unaware of vehicle movements, and vehicles driving on the wrong side of the road. These factors resulted in 239, 92, 19, 19, 18, 9, and 9 accidents, respectively, totaling 437 crashes.

In contrast, less significant factors in this study area include alcohol consumption, improper turning, drowsiness or fatigue, road defects, and passing on inclines or curves. However, issues such as addiction, ignoring traffic lights, improper thinking, ignoring stop signs, and poor lighting were not found to contribute to RTAs in this segment. Despite alcohol addiction, drowsiness, and fatigue being frequently cited as major causes of RTA severity in other research, this study did not identify alcohol addiction as a primary cause of RTAs.

Overall, drivers who exceed speed limits and disregard traffic regulations are primarily responsible for serious accidents along the studied road. It is crucial for traffic authorities to strictly enforce speed limits and traffic rules, and to penalize violators, in order to mitigate the severity of RTAs on this road.

The geometric features of roads play a significant role in determining the severity and frequency of accidents in a given area. Figure 6 below illustrates the number of accidents based on road geometry.

RTAs vs. Characters of Road Geometry.

In the discussion of accident severity by road geometry, as illustrated in Fig. 6, reports that the highest levels of accident severity were observed on straight road segments. These segments were associated with fatalities, serious injuries, slight injuries, and property damage. The finding supports that over speeding was a predominant factor contributing to the high severity of accidents on these straight segments.

Additionally, the findings on Fig. 6 indicate that slight upgrades, slight reverse curves, and Juntel downhill roads have a medium impact on road traffic accidents. These road features were associated with a moderate level of severity in accidents. Conversely, other road characteristics, including highly steep upgrades, straight uphill and downhill segments, strictly reverse curves, and Juntel uphill roads, did not show a significant effect on accident severity in the study area.

Prioritization and identification of black spot locations on the study road

Identification of black spot locations

General

The following locations were selected for investigation based on their history of accidents in the Amhara region along the major highway segment from Dembecha to Injibara: Dembecha and its surroundings, Finote Selam city and its surroundings, Burie, Banja, Guagusa Shikudad, and Injibara city.

Data on various factors contributing to accidents were collected from these sites, and accident records were obtained from the police stations of Finote Selam city and Injibara city to validate the study.

To compare results and validate the effectiveness of the quality control/critical crash rate factor method Hamburger¹⁵., the study considered the number of different types of road traffic accidents (RTAs) (fatal injuries, serious injuries, minor injuries, property damage) and the length of the black spot section. A location was identified as an accident black spot if it showed an abnormal crash frequency rate compared to other locations. This method also used exposure data such as traffic volume and road section length to determine if the critical accident rate at a particular location was significantly higher than the average for each factor. Accident rate calculations and black spot identification were carried out using the quality control/critical crash rate factor method Hamburger et al¹⁵. The following two steps were taken:

1. I.

   Determination of accident location:

The accident location was determined based on exposure data such as traffic volume, with the length of the road section considered at a rate per million vehicle kilometers (Uf), calculated as follows:

For Junctions:

For Road Sections:

where

Uf = Injury accidents per million vehicle-km;

U = Number of reported injury accidents during period n;

n = Number of years;

L = Section length (km).

1. II.

   Calculating the Critical Crash Rate:

The critical crash rate is determined using the following formula, which provides the data for calculating the critical accident rate (Rc). This method assumes that crashes follow a Poisson distribution.

where

Rc = Critical Accident Rate (accidents per million vehicles or accidents per million vehicle-km);

Ra = Average crash rate;

MEV = Millions of vehicle (km) during the analysis period;

where

Rj = Accident rate at site j (acc/Mveh-km);

Ra = average accident rate (acc/Mveh-km);

Fj = Accident frequency at site j;

Y = Period of analysis (year);

L = Section length at site j (km).

For Road Sections

\(\:\text{R}\text{c}=\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{C}\text{r}\text{a}\text{s}\text{h}\:\text{R}\text{a}\text{t}\text{e}+\left[\left(\text{k}\right)\sqrt{\frac{\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{C}\text{r}\text{a}\text{s}\text{h}\:\text{R}\text{a}\text{t}\text{e}}{365\text{*}\text{Y}\text{*}\left(\text{A}\text{A}\text{D}\text{T}\right)\text{*}\frac{\text{L}\text{j}}{100000}}}\right]\bigg[\frac{1}{2\left(365\text{*}\text{Y}\text{*}\right(\text{A}\text{A}\text{D}\text{T})\text{*}\text{L}\text{j}/1000000}\bigg]\) 

For Junctions

\(\:\text{R}\text{c}=\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{C}\text{r}\text{a}\text{s}\text{h}\:\text{R}\text{a}\text{t}\text{e}+\left[\left(\text{k}\right)\sqrt{\frac{\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{C}\text{r}\text{a}\text{s}\text{h}\:\text{R}\text{a}\text{t}\text{e}}{365\text{*}\text{Y}\text{*}\left(\text{A}\text{A}\text{D}\text{T}\right)/1000000}}\right]\bigg[\frac{1}{2\left(365\text{*}\text{Y}\text{*}\right(\text{A}\text{A}\text{D}\text{T})/1000000}\bigg]\) 

where

AADT = average annual daily traffic for the spot (for an intersection, the sum of the volumes on all approaches);

Y = Number of years analyzed;

L = Length of the segment in kilometers (for intersection L is 1);

K = Confidence level (95% confidence interval, k = 1.645).

1. III.

   Comparing the location’s crash rate with the critical crash rate:

If the crash rate exceeds the critical crash rate, the location is classified as an accident black spot. The quality control method is used to identify black spot segments on roads by calculating the accident frequencies of all spot segments over a specific period. According to this method, a location is classified as a black spot if its safety parameter (i.e., crash rate) exceeds a critical value. The accident spots are then ranked based on the severity calculated using the TRL method.

In this study, the researcher utilized the accident frequency data from Table 7 and applied quality control methods to identify black spot segments on the road. The accident frequencies for each spot segment were calculated for the individual accident rate, average accident rate, and critical crash rate over the specified period (See Table 7 below). Based on the analysis values in Table 7, locations where the individual crash rate exceeded the critical crash rate, such as Burie, Banja, and near Finote-Selam, were considered black spot locations.

Black spot locations were identified based on the table above. The findings indicated that Finote Selam, Burie, and Banja were affected by accident black spots.

Prioritization of black spot locations

To prioritize accident-prone areas, the researcher considers both the weights of accident factors and the types of accident severity.

In the study area, various factors contributing to road traffic accidents were identified. Table 8 below provides an overview of these factors along with the corresponding accident record locations.

Based on Table 8, the factors contributing to road traffic accidents were considered, and weightages were assigned to each factor. To determine the overall impact of these factors on each road section, the final weightage for each factor was assigned and is detailed in Table 9 below.

The ultimate weight assigned to each road link is determined by summing all individual weights and then normalizing this sum using the maximum possible weight, which is 90 in this scenario. The calculation is performed as follows:

Using the factors and their associated locations detailed in Table 8, along with the specific weights assigned to these factors as shown in Table 9, the total weight for each road segment was calculated using the formula above. The results of these calculations are summarized in Table 10 below.

Consequently, road links with higher final weights are deemed less prone to accidents, whereas those with lower final weights are considered more accident-prone. The classification of roads based on accident occurrence, according to these final weights, adheres to the scheme detailed in Table 11 below.

Therefore, based on the factor weights utilized in the analysis of road traffic accidents, as shown in Table 10, and the prioritization scheme for accident-prone level weightages outlined in Table 11, the locations of Burie, Around Finote Selam, and Banja were identified with the lowest weights of 31, 34, and 37, respectively. These results suggest that these areas are highly susceptible to accidents, as they fall within the high accident-prone level range of 0 to 40. In comparison, the remaining locations are categorized as moderately prone to accidents, with a medium accident-prone level ranging from 40 to 60.

Another method for prioritizing black spot sites involves determining the ratio of accident costs to the degree of accident severity using the TRL (2000) method²⁴. For fatal accidents, a weight of 5 is assigned, for serious injuries a weight of 3, and for light injuries and property damage weights of 2 and 1, respectively. The formula for estimating the ranking of sites is as follows:

where

P = Priority value;

W = Total number of property damages;

X = Total number of light injuries;

Y = Total number of serious injuries;

Z = Total number of deadly injuries;

D = the total distance traveled by the black spot section in kilometers.

To rank these locations based on the severity of road traffic accidents (RTAs), the researcher utilized the ratios of accident costs by degree of severity established by the TRL. As a result, as shown in Table 12, each segment was assigned a rank ranging from 1 to 8. The locations of Burie, around Finote Selam, Banja (around Injibara), Finote Selam, Injibara, Machakel, and Guagusa Shikudad, as well as near Dembecha and Dembecha towns, were ranked from 1 to 8, respectively (refer to Table 12 below).

The analysis results from the table indicate that the Burie section was ranked first among accident black spot locations, followed by areas around Finote Selam, Banja, Finote Selam, Injibara, Dembecha, Guagusa Shikudad, and around Dembecha.

Therefore, by focusing on high-risk accident areas using the importance of key factors that leads to traffic accidents and the severity of traffic incidents, significant understandings about road accidents can be gained. This is particularly advantageous when the assigned weights for varying levels of accident severity types and associated weights of factors incident seriousness are uniform. As a result, the findings from the analysis become more trustworthy and insightful.