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.
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.
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.
Both models are statistically significant at
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.
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
Botswana road traffic accident summary statistics (2008–2017).
Variable | Mean | Median | SD | Min. | Max. |
---|---|---|---|---|---|
Accident count | 18 244 | 17 894 | 1222.5 | 16 641 | 20 415 |
Fatalities | 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 |
Population (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 deviation.
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
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
The causes of road traffic accidents are complex phenomena that researchers have to confront (Rolison et al.
Vehicular mileage or amount of travel is commonly used to quantify exposure (Lloyd & Forster
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. (
At a disaggregated level, the number of cars on the road has a significant impact on road traffic accidents. Ashraf et al. (
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
The time of travel is normally expressed in two categories, daytime or night-time. Ashraf et al. (
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.
This longitudinal study employs the determinant variable theory of road traffic accident investigation (Benner
Road traffic accident statistics are riddled with challenges regarding the precision and availability of data (Štefko et al.
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
The accident count data were categorised into
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.
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
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.
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.
Two accident severity regression models are presented and discussed next. These were the best model results after numerous runs.
The accident count model is presented in
Accident count ordinary least squares model.
Variable | Coefficient | SE | ||
---|---|---|---|---|
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 |
, Statistical significance =
, Statistical significance =
BWP, Botswana pula; SE, standard error.
Summary statistics for accident count model.
Statistic | Value |
---|---|
Mean dependent var | 18 243.70 |
SD dependent var | 1222.471 |
Sum squared resid | 4698.409 |
SE of regression | 48.46859 |
0.999651 | |
Adjusted |
0.998428 |
817.6147 | |
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 statistic: Chi-square(2) = 1.26522, with
SD, standard deviation; SE, standard error; var, variable; resid, residual.
The variables
In the fatalities model in
Fatalities ordinary least squares model.
Variable | Coefficient | SE | ||
---|---|---|---|---|
Constant | −525.314 | 194.601 | −2.699 | 0.0428 |
Cars | −0.00236305 | 0.000409773 | −5.767 | 0.0022 |
Population | 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 |
, Statistical significance
, Statistical significance
SE, standard error; USD, United States dollar.
Summary statistics for fatalities model.
Statistic | Value |
---|---|
Mean dependent var | 430.7000 |
SD dependent var | 35.50602 |
Sum squared resid | 1043.016 |
SE of regression | 14.44311 |
0.908073 | |
Adjusted |
0.834531 |
12.34770 | |
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 statistic: chi-square(2) = 1.81406 with
SD, standard deviation; SE, standard error; var, variable; resid, residual.
The model was also tested for the normality of errors using the chi-square test. The result was an insignificant
The accident count model (
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
In the fatalities model,
The
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 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.
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. (
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.
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.
The author declares that no competing interests exist.
I declare that I am the sole author of this research article.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data sharing is not applicable to this article as no new data were created or analysed in this study.
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.