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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.
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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
Aliaon:
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 arcle:
Mphela, T., 2020, ‘Causes of
road accidents in Botswana:
An econometric model’,
Journal of Transport and
Supply Chain Management
14(0), a509. hps://doi.org/
10.4102/jtscm.v14i0.509
Copyright:
© 2020. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License.
Introducon
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.
Populaon
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 characteriscs
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 trac accident summary stascs (2008–2017).
Variable Mean Median SD Min. Max.
Accident count 18 244 17 894 1222.5 16 641 20 415
Fatalies 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
Populaon (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
deviaon.
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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 consideraon
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.
Fatalies 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 stascs for accident count model.
Stasc 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
stasc: Chi-square(2) = 1.26522, with p-value = 0.531203.
SD, standard deviaon; SE, standard error; var, variable; resid, residual.
TABLE 2a: Accident count ordinary least squares model.
Variable Coecient SE t-rao 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**
*, Stascal signicance = p ≤ 0.01; **, Stascal signicance = 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 recommendaons
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: Fatalies ordinary least squares model.
Variable Coecient SE t-rao p
Constant −525.314 194.601 −2.699 0.0428*
Cars −0.00236305 0.000409773 −5.767 0.0022**
Populaon 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**
*, Stascal signicance p ≤ 0.01; **, Stascal signicance p ≤ 0.001.
SE, standard error; USD, United States dollar.
TABLE 3b: Summary stascs for fatalies model.
Stasc 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
stasc: chi-square(2) = 1.81406 with p = 0.403722.
SD, standard deviaon; SE, standard error; var, variable; resid, residual.
Page 7 of 8 Original Research
hp://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.
Limitaons 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.
Compeng interests
The author declares that no competing interests exist.
Author’s contribuons
I declare that I am the sole author of this research article.
Funding informaon
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.
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Road traffic accidents (RTAs) are the major cause of deaths worldwide, which imposes a substantial economic burden on the society. RTAs are triggered by several factors that can manifest individually or in association resulting in discernible patterns. This paper employs data mining approach, namely association rule mining algorithm, to discover latent relationships between various factors triggering RTAs in Botswana. The findings show that most traffic accidents in Botswana occur in Kweneng, Central and South East districts. Of these, 69% are minor, 22% serious and 9% fatal accidents. Furthermore, these accidents occur often on Saturdays and Fridays, with about 20% and 15% casualties involved, respectively. The drivers are mostly young males who are recurrently under the influence of alcohol or drugs. Furthermore, 99% of RTAs drivers hold valid driver’s licences and 61% often collide with pedestrians crossing the road in daylight. The primary critical factors accounting to RTAs are alcohol/drugs, driver age, driver licence validity, weather, road curvature and road surface type, which relate to the driver attribute class and the accident attribute class congruently. The findings reveal that there are several strong relationships among the factors, such as the driver licence validity, driver alcohol/drugs, victim, weather, accident severity and road condition. These factors, show positive correlations in their co-occurrences in unique traffic accidents events in Botswana. Findings of the study are projected to provide useful information on understanding the main critical factors triggering road accidents in Botswana, in addition to supporting management to identify and implement accident prevention mechanisms.KeywordsTraffic accident dataAssociation rule miningFP-Growth algorithmVisualisationData miningKnowledge discovery
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Background: Numerous studies working on the association between the human development index (HDI) and road traffic death rate (RTDR) , merely focused on developed countries, have not thus far reflected on the relationship between the HDI components and RTDR. Therefore, this study aimed to analyze the trends of RTDR and their association with the HDI and its components from 2007 to 2016. Methods: The RTDR data for 131 countries were imported into the unconditional latent growth model (LGM) to assess the trends of RTDR between 2007 and 2016. The impact of the HDI and its components (viz. education, income and life expectancy [LE viz]) on the trajectory of RTDR was also evaluated using the conditional LGM. Classification and regression trees (CARTs) were then applied to classify the countries based on the HDI and its components into downward or upward trends. The importance of the HDI components in the CART models was further extracted through random forests (RFs). Results: The results of the unconditional LGM indicated an overall decreasing trend in RTDR. A total number of 106 and 25 countries also had a downward and upward trend of RTDR, respectively. Based on the conditional LGM, the outcomes revealed the negative effect of the HDI and its components on the model parameters. The accuracy of the CARTs was at least by 85%, indicating its high classification performance. According to the findings of RFs, LE was the most crucial variable in the CART models. Conclusion: Overall, RTDR is concluded to be multifactorial. As this study showed, the HDI and its LE component are the main determinants of RTDR at the global level. In addition, legislative factors and sociocultural context of countries are assumed as important issues that should be considered by policy-makers to control RTD.
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The number of vehicle ownership keeps increasing with the fast development of China's society and economy. Consequently, there are surging road traffic accidents, which seriously endanger the safety of people's lives and property. Hence, the present work aims to reduce the probability of traffic accidents and improve the efficiency of driver risk identification. The driver is taken as the research sample. Driver's physiological and Pleasure Arousal Dominance (PAD) data are collected using virtual driving equipment. Recurrent Neural Network (RNN) is applied to identify multiple physiological signals. Support Vector Machine (SVM) is utilized to classify the data. The performance of the proposed model is validated through data analysis of multiple volunteers. Results demonstrate that the PAD physiological signals can improve the rate of drivers recognizing risks. In the meantime, these signals help to understand the driver's emotions, thereby identifying the risks faced by the driver. By combining SVM and RNN, the accuracy of risk identification reaches 91.44%, which to some extent proves the effectiveness of the proposed model. The results have vital reference value for reducing traffic accidents and ensuring driving safety.
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Across the globe, urban areas experience the phenomena of rising road-congestion, air pollution and car accidents. These are just a few popular quantified effects that arise due to rapid, uncoordinated urbanization on a car-centric city layout. There is an urgent need to consider new concepts of urban mobility development to combat these negative effects. Car-free mobility is one notion adopted in diverse formats by numerous cities to create a more inclusive, just, healthy and sustainable urban life. The focus of this thesis is to ex- amine whether a car-free mobility concept is applicable to the Maun Science Park, Bot- swana. Therefore, the idea of car-free mobility, its positive aspects as well as its con- straints, are described first. This illustrates the complexity of urban transport planning as it is intertwined with urban land-use, political vision and people’s perceptions and behav- iors. Secondly, examples and strategies on how to change existing structures are pre- sented. Following this, the smart developments in the field of sustainable urban mobility are considered to provide an insight into their assets and drawbacks. Then the local mo- bility conditions are examined before the car-free concept is exemplarily applied to the Maun Science Park via scenario construction. These scenarios give a first vision of how a car-free concept can be applied to the MSP and additionally provide a starting point for future strategic planning as well as inspiration for other cities to follow along.
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With the increased usage of private vehicles, the number of road accidents have shown an increasing trend. In this study, we applied data mining techniques to analyze the traffic accident data pertaining to Dubai for the year 2017. The data for analysis is obtained from the official open data portal of United Arab Emirates. Apriori algorithm has been employed to mine frequent itemsets. Studies have been conducted to identify the major causes and trends associated with road accidents. Further experiments have been carried out to analyze the major causes of accidents during peak accident times and weekdays/weekends. It has been observed that majority of accidents involve vehicle hitting another vehicle due to inadequate space between vehicles. Another finding was that youth were involved in the majority of accidents. The results show that accidents’ peak time was during late night and that the majority of drivers were found to be intoxicated. Studies conducted show that during weekend’s peak accident time, majority of the drivers were intoxicated, while during weekday’s peak accident time, the maximum number of accidents occurred due to lack of enough space between vehicles. On the basis of the results obtained, certain recommendations which aid in reducing the accident rate have been put forward.
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South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens’ relief since in our daily lives road accidents are getting more diverse.
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The realizing and improvement of road infrastructure, of modern road networks provides normal, safe and pleasant road traffic conditions and also help prevent road accidents. The road network, with its constructive characteristics, has to offer optimal conditions for the movement of vehicles, pedestrians and other categories of participants in the road traffic. Starting from the case study of a road sector with heavy road traffic, the current paper analyzes the increase in road safety in Romanian localities along European and national roads through the implementation of specific measures such as setting up sidewalks, installing New Jersey median barriers, expanding the road sectors with 2+1 lanes, replacing normal pedestrian crossings with elevated crossings or with pedestrian crossing with mid-road waiting areas etc.
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Backgro und: Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. Methods: A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. Results: A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants’ accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Conclusion: Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.
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What are the main contributing factors to road accidents? Factors such as inexperience, lack of skill, and risk-taking behaviors have been associated with the collisions of young drivers. In contrast, visual, cognitive, and mobility impairment have been associated with the collisions of older drivers. We investigated the main causes of road accidents by drawing on multiple sources: expert views of police officers, lay views of the driving public, and official road accident records. In Studies 1 and 2, police officers and the public were asked about the typical causes of road traffic collisions using hypothetical accident scenarios. In Study 3, we investigated whether the views of police officers and the public about accident causation influence their recall accuracy for factors reported to contribute to hypothetical road accidents. The results show that both expert views of police officers and lay views of the driving public closely approximated the typical factors associated with the collisions of young and older drivers, as determined from official accident records. The results also reveal potential underreporting of factors in existing accident records, identifying possible inadequacies in law enforcement practices for investigating driver distraction, drug and alcohol impairment, and uncorrected or defective eyesight. Our investigation also highlights a need for accident report forms to be continuously reviewed and updated to ensure that contributing factor lists reflect the full range of factors that contribute to road accidents. Finally, the views held by police officers and the public on accident causation influenced their memory recall of factors involved in hypothetical scenarios. These findings indicate that delay in completing accident report forms should be minimised, possibly by use of mobile reporting devices at the accident scene.
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Road accidents are a major societal issue for every country. The purpose of this paper is to assess the number of traffic and road accidents depending on a series of variables (collision mode, road configuration, conditions of occurrence, road category, type of vehicle involved, personal factors, and length of time of the driving license) in Romania from 2012-2016. The analysis of the road accident trend identifies the causes of accidents, road safety performance indicators, and risk indicators. Having these identified data, a framework is proposed for improving the road safety system and reducing accidents. The Romanian Police, the National Institute of Statistics (NIS) in Romania, and the European Commission provided the data used for this analysis. The data obtained from these databases are analysed and evaluated according to a series of variables. This paper will outline an informative image of road accidents and establish a framework for reducing their effects in road transport. As a result of the analysis, we have seen that the combination of vehicles and personal factors influences the number of traffic and road accidents.
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Background The use of an appropriate driving exposure measure is essential to calculate traffic crash rates and risks. Commonly used exposure measures include driving distance and the number of licensed drivers. These measures have some limitations, including the unavailability of disaggregated estimates for consecutive years, low data quality, and the failure to represent the driving population when the crash occurred. However, the length of driving time, available annually from the American Time Use Survey (ATUS), can be disaggregated by age, gender, time-of-day, and day-of week, and addresses the temporal discontinuity limitation of driving distance on the United States (U.S.) national scale. Objectives The objective of this study is to determine if the length of driving time as a driving exposure measure is comparable to driving distance by comparing distance-based and time-based fatal crash risk ratios by driver age category, gender, time-of-day, and day-of-week. Methods The 2016–2017 National Household Travel Survey (NHTS) provided driving distance, and 2016–2017 Fatality Analysis Reporting System provided the number of drivers in fatal crashes. The distributions of driving distance and length of driving time by driver age category (16−24, 25−44, 45−64, and 65 years or older), gender, time-of-day, day-of-week were compared. Two negative binomial regression models were used to compute the distance-based and time-based fatal crash risk ratios. Results The distributions of driving-distance were not different from the length-of-driving-time distributions by driver age category, gender, time-of-day, and day-of-week. Driving distance and the length of driving time provide similar fatal crash risk ratio estimates. Conclusions The length of driving time can be an alternative to driving distance as a measure of driving exposure. The primary advantage of driving time over driving distance is that, starting from 2003, the disaggregated estimates of the length of driving time are available from ATUS over consecutive years, curtailing the discontinuity limitation of driving distance. Furthermore, the length of driving time is related to drivers’ perceived risks about their driving conditions and as a result, may be a better exposure measure than driving distance in comparing crash risks between drivers whose likelihood of traveling in hazardous driving conditions (e.g., nighttime) varies substantially.
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Road traffic accidents have decreased in most developed nations over the last decade. This has been attributed to improvements in vehicle and road design, medical technology and care, and driver education and training. Recent evidence however indicates that fuel price changes also have a significant impact on road traffic accidents through other mediating factors such as reductions in driver exposure through less car travel and more fuel-efficient driving e.g. speed reduction on high-speed roads. So far though, no study has examined the effects of changing fuel prices on road traffic accidents in a country such as Great Britain where fuel prices are kept artificially high for public policy reasons. Consequently, this study was designed to quantify the effects of fuel price on road traffic accident frequency through changes and adjustments in travel behaviour. For this purpose, weekly fuel prices (between 2005-2015) have been used to study the effects on road traffic accidents using the Prais-Winsten model of first order autoregressive (AR1) and the Box and Jenkins seasonal autoregressive integrated moving average models (SARIMA). The study found that with every 1% increase in fuel price there is a 0.4% reduction in the number of fatal road traffic accidents. In light of this, one concern raised was that recent UK government plans to phase out petrol and diesel vehicles by 2040 may also risk a rise in fatal road traffic accidents, and hence this will need to be addressed.
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Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.