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Journal of Transport and Supply Chain Management
ISSN: (Online) 1995-5235, (Print) 2310-8789
Page 1 of 8 Original Research
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Author:
Thuso Mphela1
Aliaon:
1Department of Management,
University of Botswana,
Gaborone, Botswana
Corresponding author:
Thuso Mphela,
mphelat@ub.ac.bw
Dates:
Received: 01 Apr. 2020
Accepted: 14 June 2020
Published: 16 Sept. 2020
How to cite this arcle:
Mphela, T., 2020, ‘Causes of
road accidents in Botswana:
An econometric model’,
Journal of Transport and
Supply Chain Management
14(0), a509. hps://doi.org/
10.4102/jtscm.v14i0.509
Copyright:
© 2020. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License.
Introducon
Road traffic accidents are becoming a serious socio-economic challenge of the 21st century, causing
loss of life and property. Unfortunately, with a share in excess of 90% of the global accident count,
developing economies are the hardest hit compared to their developed counterparts (World Bank
2017), whilst facing the innumerable challenges of poverty and poor healthcare. Botswana is no
exception. Globally, the annual road traffic carnage is estimated to have grown from 1.2 million to
1.3 million (World Health Organization [WHO] 2015). The World Bank (2017) reports that
approximately 20–50 million people are seriously injured annually. In Botswana, the period under
review has recorded minimum and maximum annual road fatalities of 377 and 497, respectively.
The rate of 23.6 road fatalities per 100 000 recorded in 2015 showed little progress since 2011, when
a rate of 23.9 was recorded, and the WHO Decade of Action for Road Safety 2011–2020 was adopted
with a view to cut fatalities in half by 2020 (Government of Botswana 2018b; World Bank 2017). In
the member nations of the Organisation for Economic Co-operation and Development (OECD), the
average road accident fatality rate is 8 per 100 000 people (World Bank 2017). In 2017, 142 out of 444
fatalities in Botswana were pedestrians. Generally, the fatality rates in Botswana have been steady
since 2005, characterised by small spikes around an average of 430 (see Table 1). A significant piece
of legislation in the Road Traffic Act (Government of Botswana 2008), whose major change was the
increase of road traffic fines (some saw increases of over 200%), was amended in 2008 with a view
to curbing road accidents, among other reasons. Unfortunately, there has been no evidence that this
brought any significant changes (Mphela 2011).
In its annual report, Statistics Botswana opines that the surge in the motor vehicle population has
increased the chances of road accidents and fatalities (Government of Botswana 2018a). Moyer,
Background: Road traffic accidents claim over 1.3 million lives annually around the globe and
remain a key socio-economic challenge today. At 20.1 per 100 000, Botswana’s fatality rate is
higher than the global average of 17.4. Previous studies on the causes of road crashes in
Botswana have not explored statistical causality. This study is thus grounded on the theory of
causality.
Objectives: This study sought to determine the causes of road traffic accidents and fatalities in
Botswana. For this purpose, the article discusses the accident count model based on Botswana
data.
Method: The study used road accident data from 2008 to 2017. Econometric modelling on
Gretl was used to compute two ordinary least squares (OLS) regression models. Manual
elimination of insignificant variables was performed through the iterations.
Results: Both models are statistically significant at p ≤ 0.001, but the accident count model,
with an adjusted R2 value of 0.99 against 0.83, is more robust and has a better predictive power
as opposed to the fatalities model. At the individual variable level, the analysis shows mixed
results.
Conclusion: The study contends that increased exposure and night-time travel increase road
crashes, whilst expansion of road infrastructure is inversely related to road accidents. An
increase in both population density and exposure leads to increased fatalities. Regulating the
importation of used vehicles and investment in rail transport is a potential policy panacea for
developing economies. Future studies should investigate the causes of pedestrian fatalities
and night accidents.
Keywords: road traffic accidents; accident modelling; econometric modelling; forecasting;
exposure; night-time travel; road infrastructure.
Causes of road accidents in Botswana:
An econometric model
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Eshbaugh and Rettig (2017) posit that the population of
motorised vehicles will continue to grow globally for the next
few decades into 2050, with the exception of OECD countries.
A previous study on road traffic accident causes in Botswana
by Mupimpila (2008) concluded that the causes of accidents
included speeding, drunken driving and congestion, whilst a
study Pego (2009) attributed the majority of accidents to
human/driver behaviour and, to a lesser degree, vehicle
factors and the roadway environment. Pego’s study was
done at city level rather than national. However, neither
research employs statistical causal methods of analysis.
Moyer et al. (2017) lament the lack of research on the causes
and impact of road traffic accidents in many developing
countries. This is an anomaly considering the high level of
accidents and fatalities that occur in these countries.
This study sought to investigate the causes of accidents in
Botswana using accident incidence statistical records from
2008 to 2017. At the time of analysis, reliable data were only
available until 2017. Production of the annual traffic
statistics by Statistics Botswana tend to take time, as
collection of data is done by the traffic police before the
cleaning and validation process, resulting in a delay of at
least 1 year. Two accident severity models – accident count
and fatalities – are computed using ordinary least squares
(OLS) regression modelling. Modelling the causes of
accidents allows us to determine the major contributors and
identify opportunities for interventions (Benner 1979;
Hakkert & Braimaister 2002). In line with the theory of
causality, the study hypothesises that road traffic accidents
and fatalities can be modelled and predicted with high
levels of certainty. Specifically, it posits that accidents and
fatalities can be explained by vehicle characteristics, the
amount of travel and the time of travel. Further, the
interaction between the dependent and explanatory
variables will differ between the two models.
Literature review
The causes of road traffic accidents are complex phenomena
that researchers have to confront (Rolison et al. 2018). This is
because there is a plethora of factors interacting simultaneously.
However, the bulk of accident occurrences are blamed on
driver behaviour (Luke & Heyns 2014; Mphela 2011). The
World Health Organization (2004) opines that the use of
the word ‘accident’ has somehow been understood to
be synonymous with inevitability and unpredictability,
rendering systems helpless in managing the eventual
outcomes of crashes. It indicates that accidents are bound to
occur, and there is nothing we can do about it. It is also critical
to note that methodological choices and the jurisdiction of
accidents tend to lead to different conclusions on the causes of
road traffic accidents. Perrels et al. (2015) argue that weather
is a critical component in dealing with road accidents. A study
by Parvareh et al. (2018) found that air temperatures had an
influence on pedestrian and motorcycle accidents in Iran. In
Catalonia (Spain), Basagana et al. (2015) found that the risk of
road crashes associated with driver performance factors
increased by almost 3% during heat waves.
Exposure
Vehicular mileage or amount of travel is commonly used to
quantify exposure (Lloyd & Forster 2014; Naqvi, Quddus &
Enoch 2020; Shen et al. 2020). WHO (2004) argues that the
risk for one journey may be small but that the amount of risk
accumulates with each trip. There is evidence that the risk of
death from traffic injuries increases with the amount of
exposure (Pulido et al. 2016). Naqvi et al. (2020) investigated
the effect of fuel prices in Britain. Their study found that the
number of fatal crashes decreased by 0.4% for every 1%
increase in fuel prices. They concluded that fuel prices
mediate fatal crashes by reducing exposure as a result of less
travel and moderated driver behaviour like speed reduction.
Lloyd and Forster (2014) argued that the use of vehicular
mileage does not allow for disaggregation, and therefore to
overcome this, they simulated both vehicle type and road
types to estimate traffic flows. In order to deal with the
limitation of disaggregation of travel distance, Shen et al.
(2020) used the length of driving time to measure exposure,
arguing that it is a more reliable measure of exposure.
Populaon
The concept of exposure discussed above implies that the
rate of accidents is likely to increase with the amount of
travel. WHO and countries use a common globally accepted
epidemiological standard where accident counts and
fatalities are expressed as a function of population, for
example, fatalities per 10 000 or 100 000 people. This standard
helps us to compare the severity of the challenge between
countries and regions. In comparing accident prevalence in
districts across China, Ding et al. (2017) found that places
with higher population densities recorded higher accidents
per unit of population. Road accident literature normally
disaggregates demographic data to determine the level of
risk experienced by different groups of society. However, in
this study aggregate population is used.
Vehicle characteriscs
At a disaggregated level, the number of cars on the road has
a significant impact on road traffic accidents. Ashraf et al.
(2019) concluded that the rate of increase in the vehicle
population in South Korea exceeded the supply of roads,
leading to increased road accidents. Vehicle characteristic
definitions tend to vary from country to country depending
on the method of recording accidents. Torrao, Coelho and
Rouphail (2012) used the make, model, wheelbase, size,
weight, engine size, age of car and type of fuel the vehicle
used in their road accident study in Portugal. The study
found that weight differentials in accidents involving more
than one vehicle played a significant role in the severity of
the accident. Heavier vehicles tended to protect the occupants
better than lighter ones, whose occupants were likely to
suffer more severe injuries or fatalities when heavier vehicles
were involved. Whereas newer vehicles tended to be safer in
single-vehicle crashes, this advantage was lost when more
than one vehicle was involved. Cioca and Ivascu (2017)
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found that the top four vehicle types that are highly likely to
be involved in road accidents in Romania are cars (65%),
intervention vehicles (55%), auto trailers (50%) and lorries/
trucks (35%). In South Korea, passenger cars (68%) and
trucks (13%) account for higher shares of road crashes
(Ashraf et al. 2019).
Road infrastructure
Road quality and supply not only are critical for facilitating
efficient movement but are also instrumental in the
management of road safety. In Romania, 60% of road traffic
accidents were caused by road alignment (Cioca & Ivascu
2017). This was further corroborated by Cristian and Lucian
(2019), who concluded that road design has a significant
bearing on road safety. Their study, also done in Romania,
revealed that the construction of sidewalks, expanding the
number of lanes in certain sections of the road and elevating
pedestrian crossings reduced the occurrence of road traffic
accidents. Levulytė et al. (2017) analysed pedestrian accidents
in Europe too, and they identified pedestrian crossing design
as a contributor to road traffic accidents. A study by Rahimi
et al. (2020) found a significant influence of road engineering
on single-vehicle truck crashes in Iran. Even though
differences were observed between accident rates on different
road types, the regression model computed by Ashraf et al.
(2019) revealed that road type has little impact on road
accidents. On the contrary, Štefko, Kubák and Bačík (2014)
found expressways to have a significant impact on the
reduction of road accidents that caused death and injury in
the Slovak Republic. In South Korea, Ashraf et al. (2019)
concluded that a lower rate of road surface expansion against
a higher rate of vehicle population increase was a major
explanator of increased road accidents.
Time of travel
The time of travel is normally expressed in two categories,
daytime or night-time. Ashraf et al. (2019) posit that whereas
road accidents occur almost equally during the night and
day, more fatalities are recorded during night-time travel.
John and Shaiba (2019) concluded that accident rates peak
late at night, whilst during the day most accidents happen at
traffic peak hours with little space between vehicles in Dubai.
A study in China by Wang et al. (2019) found that accidents
that occur late in the night tended to be more fatal than those
occurring during the day. Closer to home, in Botswana, South
Africa and Namibia, there is evidence that fatal accidents
tend to happen in the night-time. This could be explained by
challenges of visibility (twilight/dawn conditions are more
challenging), but there is also a chance that this occurs when
human policing is low (Mphela 2011), a time when reckless
driving tends to be prevalent. The latter argument is further
supported by Ashraf et al. (2019), who opined that night-time
accident occurrence in South Korea was explained by other
road use factors besides visibility challenges.
In conclusion, increased exposure leads to an increase in the
risk of accidents occurring, whilst densely populated areas
are likely to experience more traffic accidents than less
densely populated ones. Different vehicle characteristics
affect road accidents in different ways. Firstly, passenger cars
are leading contributors to accident statistics, whilst heavier
vehicles are likely to increase the severity of accidents if they
are involved. On the other hand, increased road supply may
lead to a decrease in road accidents. When it comes to the
time of travel, more fatal accidents are likely to occur at night.
Research methods and design
This longitudinal study employs the determinant variable
theory of road traffic accident investigation (Benner 1979).
The objective is to determine a causal relationship between
the different factors, whether population, environmental,
economic, vehicular characteristic or pure chance and
accident counts. Sets of data used in such models are not
always ideal but are normally influenced by availability and
quality. For example, Shen et al. (2020) used data on the
length of travel time instead of the travel distance, because
the former was available in disaggregated form. For this
purpose, the study uses the TRafikk, ULykker og Skadegrad
(traffic, accidents and injury degree [TRULS]) model,
developed under the Demand Routière, les Accidents et leur
Gravité (DRAG) family of models (Fridstrøm 1999). The
TRULS model takes a multilayered approach, resulting in
multiple equations that measure direct effect instead of a mix
of both direct and indirect effects at the same time. For
purposes of this article, the researcher focused specifically on
the accident and severity models that measure the causes of
accidents and fatalities, whilst testing the ability of the
models to predict their occurrence.
Discussion of variables and data
Road traffic accident statistics are riddled with challenges
regarding the precision and availability of data (Štefko et al.
2014), owing to a lack of resources that results in under-
reporting (Rolison et al. 2018) and to weaknesses in data-
capturing instruments. Pulido et al. (2016) posit that there is
a dire need for data on factors that measure direct exposure
in many countries. Sometimes, authorities charged with
recording data use different criteria for the recording of
accident data. A study in Botswana by Pego (2009) found
that traffic police used two different forms to record accident
data, leading to inconsistencies. It is for this reason that
Rolison et al. (2018) recommended a continuous review of
police accident data forms. For example, a discussion with
traffic police revealed that in Botswana, statistics on
fatalities normally register lives lost on the accident spot.
There are efforts to follow accident cases beyond this
approach, so that if an eventual death is determined to be
caused by the accident, then it can be recorded in the road
fatality statistics. Minor crashes are likely not to be reported
(Fridstrøm 1999), thereby distorting the data on accident
occurrence, which technically translates to the probability
of severity. It would seem that challenges with road traffic
accident data are not new. Benner (1979) attributes this to
the varying interests (difficult to reconcile) of different
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stakeholders, like the police, insurers and litigators, among
others, for the same incident.
This study uses panel data retrieved from various sources.
The majority came from Statistics Botswana annual reports,
augmented by data from the Southern African Customs
Union and the World Bank. The accident data was further
corroborated with the Motor Vehicle Accident Fund Botswana
statistics for purposes of consistency. Detailed data on
accident characteristics could only be sourced from the
annual Botswana Transport and Infrastructure Statistics
Reports from 2008 to 2017. Much of this data is supplied by
the Botswana Police Service traffic division to Statistics
Botswana. The data summary given in Table 1 is computed
from the annual Botswana Transport and Infrastructure
Statistics Reports (Government of Botswana 2018b), except
for variables on fuel imports.
Exposure is considered the most important variable in
explaining severity (Fridstrøm 1999). Normally, this variable
is measured by the vehicle mileage or amount of travel by
foot for pedestrians (Hakkert & Braimaister 2002), which
translate to the amount of activity on the roads. The last time
Botswana attempted to measure this through traffic counts
was in 2008. Consequently, the researcher used country level
data on fuel imports as a proxy variable. This is not in any
way a perfect measure, but it is a plausible alternative given
that fuel consumption is directly proportional to vehicle
kilometres, holding everything else constant. Three variables
were created, one where the absolute Botswana pula (BWP)
import value was used, one where this value was controlled
for exchange rates and one representing the volume of
imports. The latter variable was created by adjusting fuel
imports for currency and price fluctuations to prevent
hoarding effects. Botswana is a net importer of fuel; therefore,
the United States dollar/Botswana pula (USD/BWP)
exchange rate and the price per barrel in USD were used.
To give context, the BWP averaged P8.37, with a minimum
of P6.16 and a maximum of P11.30, to the USD over the
10 years. On the other hand, crude oil prices were as
volatile, averaging $76.06, with minimum and maximum
prices of $43.58 and $99.67, respectively. A lot of this
behaviour followed the global economic meltdown of 2008.
Data on fuel imports was retrieved from the International
Merchandise Trade statistics reports from Statistics Botswana
(Government of Botswana 2014, 2015, 2016, 2018a, 2019a,
2019b) and augmented with sources from the Southern
African Customs Union (SACU Secretariat 2013). Statistics
Botswana personnel were also consulted to fill gaps in the
statistical data.
The accident count data were categorised into daylight
accidents or night accidents, representing the actual
occurrence of road accidents during the day or night,
respectively. Botswana generally has longer days than
nights, except in winter, when the shortest day lasts for just
above 10 hours and 36 minutes. For purposes of this paper,
daylight was from 06:00 to 18:00. Botswana, to a large
extent, has a unique context in this regard because of its
larger presence of roaming animals, especially along
unfenced roads – increasing the chances of accidents at
night. In Fridstrøm’s model, this was a dummy variable,
whereas in this case, it is the actual count of accidents
occurring during the two parts of the day.
Data on the road network (measured in kilometres) prior to
2011 is rather unreliable because the Transport and
Infrastructure Statistics report records only the road
infrastructure under central government maintenance,
leaving out roads under the care of local governments. Taking
the cue that the network remained constant over more than
5 years before 2011, a decision was made to equate 2009 to
2010, and only 2008 was varied because records showed an
increase in 30 km from 2008 to 2009. This is likely to pose
challenges regarding the variable significance.
Vehicle type in Botswana is categorised into five kinds: cars
(passenger cars), light duty vehicles (LDVs), trucks, buses
and tankers/horses. In the conceptual framework, these are
referred to as ‘vehicle characteristics’. Tankers and horses are
grouped for statistical reporting with no reasons given,
despite the two being different configurations.
Data analysis
The study built two different aggregated models to determine
the explanatory factor of a number of different variables on
accident occurrence and fatalities at a macro level. Using
10-year secondary data, an econometric model was built to
determine the causes of accidents and fatalities. After the
data were cleaned, Statistical Package for Social Sciences
(SPSS) version 26 was used to run regression curve estimates
of all independent variables against fatalities and accidents as
dependent variables. Only the fuel imports variable returned a
significant relationship with fatalities. When the same exercise
was carried out on accidents as a dependent variable, only
road infrastructure did not register a significant relationship.
This initial evaluation of variables is critical in visualising the
singular effects of explanatory variables independent of
TABLE 1: Botswana road trac accident summary stascs (2008–2017).
Variable Mean Median SD Min. Max.
Accident count 18 244 17 894 1222.5 16 641 20 415
Fatalies 430.7 427.5 35.51 377 483
Fuel imports (million BWP) 6155.40 6131.57 1744.24 3309.47 8718.05
Fuel imports (million USD) 763.08 773.82 214.82 439.17 1038.90
Fuel imports (volume,
thousands of litres) 10 010 10 640 2184 4406 11 820
Road infrastructure (km) 24 450.03 24 158.82 6782.29 18 012 31 746.70
Populaon (thousands) 2035.3 2086.5 191.93 1755 2264
Cars 226 200.9 232 632.5 69 112.44 120 783 328 572
LDVs 102 445.8 103 009 7487.37 88 547 111 129
Trucks 25 193.2 23 924 8554.49 15 321 46 729
Buses 13 965.7 14 456 4247.40 4541 19 624
Tankers/horses 2768.6 2851.5 431.66 1892 3208
Daylight accidents 10 936.6 10 718.5 881.37 9895 12 558
Night accidents 7307.1 7248.5 372.16 6719 7931
BWP, Botswana pula; USD, United States dollar; LDVs, light duty vehicles; SD, standard
deviaon.
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others. Both OLS regression models were run on Gretl
software (Cottrell & Lucchetti 2018), first treating all
variables as explanatory variables. After every run, the
least significant variable (using p-values) was manually
removed and the model rerun until all the variables in the
model were significant. A counter process, where eliminated
variables were added back into the model, was also
performed to validate the model. Further, a number of
treatments/transformations were also done on the data,
and different models were run to explore non-linear
relationships. For example, among the transformations
done were logs and percentage changes. However, these did
not improve the quality of the regression models. Contrary
to Conto and Ferreira’s (2011) assertion, simple OLS models
provided a better fit than log-linear models using this data.
Fridstrøm (1999), whose work this study is largely based
on, also used logs. Therefore, the two econometric models
computed in this article follow the formula given in
Equation (1):
yt = β1 + β2xt + et t = 1, 2, … , T [Eqn 1]
The formula allows us to measure the temporal effects of the
different factors. Therefore, we are able to determine the
implications of the explanators on the dependent variables
over time.
Ethical consideraon
This study was primarily based on publicly available
secondary data with no human subjects involved. Further,
the study did not pose any harm to any person’s character,
business or organisation. However, the researcher takes
personal responsibility for the outcome of the results.
Results and discussion
Two accident severity regression models are presented and
discussed next. These were the best model results after
numerous runs.
Accident count model
The accident count model is presented in Table 2a and b.
Seven variables returned significant outcomes when
regressed against road accident counts. The model also
has a higher explanatory power, with an R2 value of 0.999.
Therefore, it explains 99% of the variation in the accident
count. Further, an adjusted R2 value of 0.998 demonstrated
the strength of the model. Even with relatively few
observations for a regression model, a test for normality of
errors failed to reject the null hypothesis. Therefore, the
errors were normally distributed, proving there is a
statistically significant relationship between the dependent
variable and its explanators. An alternative model to the
model presented next was computed, substituting fuel
imports (BWP) with fuel imports (USD). The result was a
good model with the same R2 value but a weaker adjusted
R2 value.
The variables tankers/horses (p ≤ 0.001) and night accidents
(p ≤ 0.01) had a particularly significant influence on the
accident counts, whereas fuel imports (BWP) (p ≤ 0.01) and fuel
imports (volume) (p ≤ 0.001) had coefficients that were weak
but statistically different from 0. The latter’s effect on accident
count is consistent with the literature. There are three
variables whose coefficients had unexpected signs. Fuel
imports (BWP), trucks and tankers/horses all had negative
coefficients. In terms of road accident literature, all these
variables are known to grow in the same direction as accident
counts. In other words, they have been known to exhibit a
positive relationship with accident counts. Growth and
improvement in road infrastructure have a decreasing effect
on road accident counts. It is worth noting the significant
impact of the night accidents variable on the accident count.
Fatalies model
In the fatalities model in Table 3a and b, the regression model
returned three fewer significant independent variables. As
opposed to the accidents model, it excluded the variable
night accidents, tankers/horses and trucks. The output indicated
fuel imports (USD), population and cars as significant
explanatory variables at a significance level of p ≤ 0.001,
whereas fuel imports (volume) was significant at p ≤ 0.01. An
R2 value of 0.91 is testament of a very strong model, explaining
91% of the variation in the fatalities. However, population had
a stronger coefficient than all the independent variables.
Whereas the other three variables were significant in the
TABLE 2b: Summary stascs for accident count model.
Stasc Value
Mean dependent var 18 243.70
SD dependent var 1222.471
Sum squared resid 4698.409
SE of regression 48.46859
R20.999651
Adjusted R20.998428
F (7, 2) 817.6147
p-value (F) 0.001222
Log-likelihood −44.95136
Akaike criterion 105.9027
Schwarz criterion 108.3234
Hannan–Quinn 103.2472
Rho −0.715964
Durbin–Watson 3.395981
Note: Test for normality of residual: Null hypothesis = error is normally distributed. Test
stasc: Chi-square(2) = 1.26522, with p-value = 0.531203.
SD, standard deviaon; SE, standard error; var, variable; resid, residual.
TABLE 2a: Accident count ordinary least squares model.
Variable Coecient SE t-rao p
Constant 14 099.6 1290.20 10.93 0.0083**
Fuel imports (BWP) −2.87261e-07 3.15369e-08 −9.109 0.0118*
Trucks −0.0620868 0.00474262 −13.09 0.0058**
Tankers/horses −1.13762 0.0841655 −13.52 0.0054**
Cars 0.00899768 0.000928700 9.688 0.0105*
Night accidents 1.21536 0.143785 8.453 0.0137*
Road infrastructure −0.0579558 0.00751920 −7.708 0.0164*
Fuel imports (volume) 0.000112590 9.58153e-06 11.75 0.0072**
*, Stascal signicance = p ≤ 0.01; **, Stascal signicance = p ≤ 0.001.
BWP, Botswana pula; SE, standard error.
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model, they had minimal explanatory power on fatality
counts. Added to this, the coefficient signs of cars and fuel
imports (volumes) were expected to be positive instead of
negative.
The model was also tested for the normality of errors using
the chi-square test. The result was an insignificant p-value,
which means that the hypothesis that errors were normally
distributed could not be rejected. Consequently, the model
proves that fatality rates are associated with the amount of
activity on the national roads.
The accident count model (R2 = 0.99) is more robust
statistically – it has a better explanatory and forecasting
power than the fatalities model (R2 = 0.90). The latter has an
adjusted R2 value of 0.83, whereas that of the former is 0.99.
This is despite the fatalities model having three fewer
explanatory variables. Both the variables LDVs and buses
were insignificant in both models. Findings on the buses
variable are particularly inconsistent with those of Romilly
(1999), whose study found increased use of buses to have a
negative effect on road crashes.
The two models, however significant, present rather
surprising outputs considering the expected effects of the
different individual variables on dependent variables. To
start with, the accident count variable is insignificant in
explaining road fatalities. Logically, one expects that the
number of accidents would have a bearing on fatality counts.
However, Wang et al. (2019) observed a contrasting rise of
accidents against declining fatalities. This could be explained
by other variables like improved safety of cars, which were
not accounted for in this model. Regarding accident count,
Fridstrøm (1999) opines that it can be just an act of
randomness, with neither particular pattern nor explanation.
For example, you can record 10 accidents that lead to no
single fatality but have a single accident occurrence that
results in 10 fatalities, demonstrating the level of uncertainty
with which fatalities can be observed.
In the fatalities model, population was the strongest
determinant of fatalities, followed by fuel imports (USD), even
though the latter recorded an inverse relationship. This
supports the argument that the chances of road fatalities
increase with population growth. On the other hand, the fuel
imports (volume) variable recorded a logically positive
coefficient, even though the effect was quite negligible. In
both models, the fuel spend variables (fuel imports [USD] and
fuel imports [BWP]) exhibited inverse relationships. The
unexpected effect of these two variables might have suffered
from hoarding effects of oil prices and exchange rates.
Therefore, we can conclude that fuel imports (volume) is a
better proxy variable for exposure.
The cars variable is the only explanator that reported different
coefficient signs in each model. In the accident count model,
the coefficient was positive; however, in both models the
strength of the coefficients was almost the same. However,
the coefficient in the accident count model is consistent with
the literature, as an increase of cars on the roads increases the
chances of accident occurrence. In the accident count model,
the trucks variable exhibited an inverse relationship. This is
contrary to the findings of Cioca and Ivascu (2017) and
Ashraf et al. (2019), who found that trucks are among the
leading vehicle types contributing to road accidents.
However, this study did not find evidence of trucks’
contribution to accident severity. The time of day has a
significant bearing on the accident count. Specifically, the
night accidents variable was the largest explanator in the
accident count model. This confirms the theory that more
accidents are likely to occur at night (John & Shaiba 2019)
because of decreased visibility and in some instances
weakened policing. However, the variable was insignificant
in the fatalities model, inconsistent with findings by Wang
et al. (2019) and Ashraf et al. (2019). In Botswana, this
resonates with the reality of roaming animals, which are
notorious for causing many accidents, especially at night.
The mere expansion of road infrastructure can be instrumental
in reducing road crashes. In the accident count model, the
road infrastructure variable displayed an inverse relationship,
consistent with the findings of Štefko et al. (2014).
Conclusion and recommendaons
The objective of this study was to determine the causes of
road traffic accidents and fatalities in Botswana. Overall,
statistics on accidents and fatalities have been contained in
the last decade, with little variation from year to year.
However, fatalities remain high proportional to country
population, a common occurrence in developing economies.
Considering the results of the computed OLS models, the
TABLE 3a: Fatalies ordinary least squares model.
Variable Coecient SE t-rao p
Constant −525.314 194.601 −2.699 0.0428*
Cars −0.00236305 0.000409773 −5.767 0.0022**
Populaon 0.782475 0.144478 5.416 0.0029**
Fuel imports (volume) 8.10179e-06 2.87515e-06 2.818 0.0372*
Fuel imports (USD) −0.239997 0.0362785 −6.615 0.0012**
*, Stascal signicance p ≤ 0.01; **, Stascal signicance p ≤ 0.001.
SE, standard error; USD, United States dollar.
TABLE 3b: Summary stascs for fatalies model.
Stasc Value
Mean dependent var 430.7000
SD dependent var 35.50602
Sum squared resid 1043.016
SE of regression 14.44311
R20.908073
Adjusted R20.834531
F(4, 5) 12.34770
p-value (F) 0.008379
Log-likelihood −37.42582
Akaike criterion 84.85164
Schwarz criterion 86.36457
Hannan–Quinn 83.19197
Rho −0.508915
Durbin–Watson 2.934763
Note: Test for normality of residual: Null hypothesis = error is normally distributed. Test
stasc: chi-square(2) = 1.81406 with p = 0.403722.
SD, standard deviaon; SE, standard error; var, variable; resid, residual.
Page 7 of 8 Original Research
hp://www.jtscm.co.za Open Access
accident count model is more robust than the fatalities model.
The models also presented unexpected outcomes, especially
on the direction of the relationship between the explanatory
and dependent variables. The most significant observation
here is that the two phenomena (accident count and fatalities)
behave differently. The accident count model has seven
explanatory variables against the four of the fatalities model.
Fuel imports (value) and cars were the only two variables
common in both models. In both models, no other variable
matched the night accidents in explanatory power. This
implies that driving at night increases the chances of accidents
more than any other factor. There is no doubt from the results
that an increase in the stock of cars in the country has resulted
in an increase in road activity, leading to an increased road
accident count, as evidenced by the positive coefficient of fuel
imports (volume).
The study’s findings and interpretation of results provided
sufficient evidence to suggest that exposure remains a critical
explanator of both accident count and severity – that as travel
distance increases, the risk of road crashes and fatalities
occurring cumulatively increases. Further, an increase in the
number of passenger cars increases the occurrence of
accidents. On the other hand, expansion of road infrastructure
is likely to lead to a decrease in the number of accidents,
particularly if the rate of expansion is not overtaken by the
increase in vehicle population. In the Botswanan context,
accidents are more likely to occur at night than during the
day. However, the model does not support the assertion that
fatalities are more likely to occur during the night-time.
Improvements in the collection of road traffic data, both in
accuracy and in terms of capturing variables that are not
currently measured, like vehicle kilometres, which are key
in the determination of exposure, are vital. Poor data in one
or more variables may distort the aggregate models,
misinforming interventions in the process. For this purpose,
Rolison et al. (2018) propose continuous updating of
accident forms to capture data on all road accident causes.
From a policy perspective, regulations on the management
of car population and road activity should be considered.
Specifically, importation of used vehicles should be heavily
taxed. Investment in both passenger and freight rail
transport by developing countries like Botswana would
ease the burden of congestion on the roads (Saruchera 2017),
thereby decreasing exposure and consequently reducing
accidents. Whilst there is a need to understand fully the
causes of night accidents, at first approximation, there is
need for improved policing.
Limitaons and future research
Where data are involved, there are bound to be limitations.
There were challenges in collating data from different sources.
However, there were attempts by the researcher to corroborate
data from different sources. Proxy variables were used where
data was not available on natural variables, which may
challenge the accuracy of the models. Ideally, the preference is
to measure any phenomenon as it occurs in its purest form.
However, the results demonstrate that the improvisation of
using the volume of fuel imports proved worthwhile. Because
of limitations of data, these results are rather indicative and
must be interpreted with caution. Future research should
investigate the causes of pedestrian fatalities, considering they
account for a significant portion of the total fatalities.
Considering that night accidents have the highest positive
coefficient in the accident count model, there is a need to
investigate extensively the causes of night accidents specifically.
Acknowledgements
The author acknowledges Christopher J. Savage for being a
reliable mentor and sounding board for his ideas in putting
this article together. The author is forever indebted to his
wife, Onalenna Segaetsho Mphela, who always reads and
edits his work and provides the moral support he needs.
Compeng interests
The author declares that no competing interests exist.
Author’s contribuons
I declare that I am the sole author of this research article.
Funding informaon
This research received no specific grant from any funding
agency in the public, commercial or not-for-profit sectors.
Data availability statement
Data sharing is not applicable to this article as no new data
were created or analysed in this study.
Disclaimer
The views and opinions expressed in this article are those of
the author and do not necessarily reflect the official policy or
position of any affiliated agency of the author.
References
Ashraf, I., Hur, S., Shaq, M. & Park, Y., 2019, ‘Catastrophic factors involved in road
accidents: Underlying causes and descripve analysis’, PLoS One 14(10),
e0223473. hps://doi.org/10.1371/journal.pone.0223473
Basagana, X., Escalera-Antezana, J.P., Dadvand, P., Llatje, O., Barrera-Gomez, J.,
Cunillera, J. et al., 2015, ‘High ambient temperatures and risk of motor vehicle
crashes in Catalonia, Spain (2000–2011): A me-series analysis’, Environmental
Health Perspecves 123(12), 1309–1316. hps://doi.org/10.1289/ehp.1409223
Benner, L. Jr., 1979, ‘Crash theories and the implicaons for research,’ American
Associaon of Automove Medicine Quarterly Journal 1(1), viewed 03 May 2020,
from hp://www.ludwigbenner.org/papers/AAAM.html.
Cioca, L. & Ivascu, L., 2017, ‘Risk indicators and road accident analysis for the period
2012–2016’, Sustainability 9(9), 1530. hps://doi.org/10.3390/su9091530
Conto, A. & Ferreira, S., 2011, ‘A note on modeling road accident frequency: A exible
elascity model’, Accident Analysis & Prevenon 43(6), 2104–2111. hps://doi.
org/10.1016/j.aap.2011.05.033
Corell, A. & Lucche, R.A., 2018, Gretl 2018c, computer soware, viewed 29 March
2020, from hp://gretl.sourceforge.net.
Crisan, D. & Lucian, T., 2019, ‘Consideraons on the role of modernizing the road
infrastructure in the prevenon of road accidents’, MATEC Web of Conferences
290(1), 06004. hps://doi.org/10.1051/matecconf/201929006004
Ding, Y., Zhou, J., Yang, J. & Laamme, L., 2017, ‘Demographic and regional
characteriscs of road trac injury deaths in Jiangsu Province, China’, Journal of
Public Health 39(3), e79–e87. hps://doi.org/10.1093/pubmed/fdw058
Page 8 of 8 Original Research
hp://www.jtscm.co.za Open Access
Fridstrøm, L. 1999, ‘An econometric model of car ownership, road use, accidents, and
their severity (Essay 3)’, in Econometric models of road use, accidents, and road
investment decisions, Volume II, Instute of Transport and Economics, pp. 1–292
Oslo, Norway, viewed 19 March 2019, from hps://www.toi.no/geile.
php?mmleid=6030.
Government of Botswana, 2008, Road Trac Act 27 of 2008, Government of
Botswana, Gaborone.
Government of Botswana, 2014, Internaonal merchandise trade stascs annual
report – 2011, Stascs Botswana, viewed 25 February 2020, from hp://www.
statsbots.org.bw/sites/default/files/publications/Botswana%20%20
Internaonal%20Trade%20Stascs%20%20Annual%20Report%202011.pdf.
Government of Botswana, 2015, Internaonal merchandise trade stascs annual
report – 2015, Stascs Botswana, viewed 25 February 2020, from hp://www.
statsbots.org.bw/sites/default/files/publications/IMTS%20ANNUAL%20
REPORT%202015.pdf.
Government of Botswana, 2016, Internaonal merchandise trade stascs annual
report – 2012, Stascs Botswana, viewed 25 February 2020, from hp://www.
statsbots.org.bw/sites/default/files/publications/IMTS%20Annual%20
Report%202012.pdf.
Government of Botswana, 2018a, Transport and infrastructure stascs report –
2017, Stascs Botswana, viewed 02 May 2020, from hp://www.statsbots.org.
bw/sites/default/files/publications/2017%20Transport%20%20
Infrastructure%20Stascs%20Report.pdf.
Government of Botswana, 2018b, Internaonal merchandise trade stascs monthly
digest – December 2017, Stascs Botswana, viewed 25 February 2020, from
http://www.statsbots.org.bw/sites/default/files/publications/IMTS%20
DECEMBER%202017.pdf.
Government of Botswana, 2019a, Internaonal merchandise trade stascs annual
report – 2013/2014, Stascs Botswana, viewed 25 February 2020, from hp://
www.statsbots.org.bw/sites/default/files/International%20Merchandise%20
Trade%20Stascs%20Annual%20Report%202013-14.pdf.
Government of Botswana, 2019b, Internaonal merchandise trade stascs annual
report – 2016, Stascs Botswana, viewed 25 February 2020, from hp://www.
statsbots.org.bw/sites/default/files/publications/IMTS%20Annual%20
Report%202016_0.pdf.
Hakkert, A.S. & Braimaister, L., 2002, The uses of exposure and risk in road safety
studies, viewed 03 May 2020, from hps://www.swov.nl/sites/default/les/
publicaes/rapport/r-2002-12.pdf.
John, M. & Shaiba, H., 2019, ‘Apriori-based algorithm for Dubai road accident
analysis’, Procedia Computer Science 163(1), 218–227. hps://doi.org/10.1016/
j.procs.2019.12.103
Levulytė, L., Baranyai, D., Sokolovskij, E. & Török, Á., 2017, ‘Pedestrians’ role in road
accidents’, Internaonal Journal for Trac & Transport Engineering 7(3), 328–341.
Lloyd, L.K. & Forster, J.J., 2014, ‘Modelling trends in road accident frequency –
Bayesian inference for rates with uncertain exposure’, Computaonal Stascs
and Data Analysis 73(1), 189–204. hps://doi.org/10.1016/j.csda.2013.10.020
Luke, R. & Heyns, G.J., 2014, ‘Reducing risky driver behaviour through the
implementaon of a driver risk management system’, Journal of Transport and
Supply Chain Management 8(1), Art. #146. hps://doi.org/10.4102/jtscm.v8i1.146
Moyer, J.D., Eshbaugh, M. & Reg, J., 2017, ‘Cost analysis of global road trac death
prevenon: Forecasts to 2050’, Development Policy Review 35(6), 745–757.
hps://doi.org/10.1111/dpr.12263
Mphela, T., 2011, ‘The impact of trac law enforcement on road accident fatalies in
Botswana’, Journal of Transport and Supply Chain Management 5(1), 264–277.
hps://doi.org/10.4102/jtscm.v5i1.77
Mupimpila, C., 2008, ‘Aspects of road safety in Botswana’, Development Southern
Africa 25(4), 425–435. hps://doi.org/10.1080/03768350802318506
Naqvi, N.K., Quddus, M.A. & Enoch, M.P., 2020, ‘Do higher fuel prices help reduce
road trac accidents?’, Accident Analysis & Prevenon 35(1), 105353. hps://doi.
org/10.1016/j.aap.2019.105353
Parvareh, M., Karimi, A., Rezaei, S., Woldemichael, A., Nili, S., Nouri, B. et al., 2018,
‘Assessment and predicon of road accident injuries trend using me-series
models in Kurdistan’, Burns & Trauma 6(1), 9. hps://doi.org/10.1186/s41038-
018-0111-6
Pego, M., 2009, ‘Analysis of trac accidents in Gaborone, Botswana’, Master of Arts
Dissertaon, University of Stellenbosch.
Perrels, A., Votsis, A., Nurmi, V. & Pilli-Sihvola, K., 2015, ‘Weather condions, weather
informaon and car crashes’, ISPRS Internaonal Journal of Geo-Informaon 4(1),
2681–2703. hps://doi.org/10.3390/ijgi4042681
Pulido, J., Barrio, G., Hoyos, J., Jiménez-Mejías, E., Marn-Rodríguez, M.M., Houwing,
S. et al., 2016, ‘The role of exposure on dierences in driver death rates by gender
and age: Results of a quasi-induced method on crash data in Spain’, Accident
Analysis & Prevenon 94(1), 162–167. hps://doi.org/10.1016/j.aap.2016.05.020
Rahimi, E., Shamshiripour, A., Samimi, A. & Mohammadian, A.K., 2020, ‘Invesgang
the injury severity of single-vehicle truck crashes in a developing country’,
Accident Analysis & Prevenon 137(1), 105444. hps://doi.org/10.1016/
j.aap.2020.105444
Rolison, J.J., Regev, S., Moutari, S. & Feeney, A., 2018, ‘What are the factors that
contribute to road accidents? An assessment of law enforcement views, ordinary
drivers’ opinions, and road accident records’, Accident Analysis & Prevenon
115(1), 11–24. hps://doi.org/10.1016/j.aap.2018.02.025
Romilly, P., 1999, ‘Substuon of bus for car travel in urban Britain: An economic
evaluaon of bus and car exhaust emission and other costs’, Transportaon
Research Part D: Transport and Environment 4(2), 109–125. hps://doi.
org/10.1016/S1361-9209(98)00028-5
SACU Secretariat, 2013, Merchandise trade stascs, Southern African Customs
Union, Namibia, viewed 10 December 2018, from hp://www.sacu.int/docs/
reports/2014/SACU-Merchandise-Trade-Stascs-2013.pdf.
Saruchera, F., 2017, ‘Rail freight transportaon concerns of developing economies:
A Namibian perspecve’, Journal of Transport and Supply Chain Management
11(1), a288. hps://doi.org/10.4102/jtscm.v11i0.288
Shen, S., Benede, M.H., Zhao, S., Wei, L. & Zhu, M., 2020, ‘Comparing distance and
me as driving exposure measures to evaluate fatal crash risk raos’, Accident
Analysis & Prevenon 142(1), 105576. hps://doi.org/10.1016/j.
aap.2020.105576
Šteo, R., Kubák, M. & Bačík, R., 2014, ‘Determinants of the road trac accident rate
in Slovak Republic’, Applied Mechanics & Materials 708(1), 130, viewed 23 April
2020, from hp://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=
100088242&site=eds-.
Torrao, G., Coelho, M. & Rouphail, N., 2012, ‘Eect of vehicle characteriscs on crash
severity: Portuguese experience’, Injury Prevenon 18. hps://doi.org/10.1136/
injuryprev-2012-040590u.41
Wang, D., Liu, Q., Ma, L., Zhang, Y. & Cong, H., 2019, ‘Road trac accident severity
analysis: A census-based study in China’, Journal of Safety Research 70(1), 135–147.
hp://doi.org/10.1136/injuryprev-2012-040590u.41
World Bank, 2017, The high toll of trac injuries: Unacceptable and preventable,
viewed 02 May 2020, from hps://openknowledge.worldbank.org/bitstream/
handle/10986/29129/HighTollofTracInjuries.pdf?sequence=5&isAllowed=y.
World Health Organizaon, 2004, World report on road trac injury prevenon, WHO,
Geneva.
World Health Organizaon, 2015, Global status report on road safety 2015, viewed
27 May 2020, from hp://www.who.int/violence_injury_prevenon/road_
safety_status/2015/en/.
