Research consultancy

TIME SERIES ANALYSIS OF ROAD ACCIDENTS IN UGANDA

FROM 2010 TO 2016

CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the study

Everybody travels whether it is to be work, play shop or do business. All raw materials must be conveyed from the land to the place of manufacturing or usage, and all goods must be moved from factory to the market place and from the staff to the customer. Transport is the means by which those activities occur; it is the cement that binds the together the communities and their activities (Somboonyanon, 2003).

Most investigations have revealed that 70% to 80% of all traffic accidents are due to human error. The term human error however is often controversial for. It doesn’t satisfactory describe that large number of injuries and deaths that occurs on the road as the result of driving errors while abilities to do so are impaired by alcohol or drugs, lack of experience, lack at distribution of attention (Kamuhanda & Schmidt, 2009).

The annual cost of road crashes is in excess of US $500 billion, and in the developing world the estimated cost is about US $65 billion each year. Due to the scarcity of costing data for African countries, it is difficult to make a precise cost of road crashes in Sub-Saharan Africa. The current estimate of costs of crashes in the continent is US$ 3.7 billion per year, of which South Africa alone accounts for 2 billion. However, the estimated costs as a percentage of the national Gross National Product (GNP) in most African countries range from about 0.8% in Ethiopia and 1% in South Africa to 2.3% in Zambia and 2.7% in Botswana to almost 5% in Kenya (WHO, 2010).

A recent Global Road Safety Project (GRSP) study shows that about 10 per cent of global road deaths in 1999 took place in Sub-Saharan Africa where only 4 per cent of global vehicles are registered. Conversely, in the entire developed world, with 60 per cent of all globally registered vehicles, only 14 per cent of road deaths occurred. However, given the widely recognized problem of under-reporting of road deaths in Africa (like the rest of the developing world); the true figures are likely to be much higher, as the police. reported road fatalities represent only the tip of the injury pyramid. According to this GRSP study, the adjusted true estimate of total road deaths for all Sub-Saharan African countries for the year 2000, based on the police department’s records, ranges between 68,500 and 82,200. However, the estimated fatality figure of 190,191 for Sub-Saharan Africa presented in the 2004 World Report, based on health care data, is much higher, and reflects the magnitude of under-reporting in police statistics (Ben maamar, Ellis, & Dunkerley, 2002; CrossRoads, 2013).

Traffic injuries and fatalities numbers are growing , making Ugandan roads unsafe. Uganda has the highest instances of international traffic injuries and fatalities rating with 190-deaths per 10,000 vehicles (Castillo-Manzano, Castro-Nuño, & Fageda, 2013; Krug, 2012; Raffo, Bliss, Shotten, Sleet, & Blanchard, 2013; Sleet, Baldwin, Dellinger, & Dinh-Zarr, 2011).

The World Health Organization (WHO; 2009) reported 2,838 fatalities for the period 2006 to 2007. The two figures indicated that the traffic injuries and fatalities in Uganda are high undermining road safety in urban transportation.

According to the WHO, Ethiopia has the highest rate of fatalities per vehicle in the world. Uganda ranks second in road fatality rates in the world behind Ethiopia. Emergency medical systems are often poor and injury prevention programmes are rarely available, (Roehler et al., 2013).

A high percentage of persons and vehicles involved in the road crashes affect human capital and business assets (Demyttenaere et al., 2009). The lack of institutional capacity to maintain, rehabilitate, and reconstruct roads within the RMS leads to poorly maintained roads, missing or incorrect road signs and markings, and poor vertical and horizontal alignments, which render road users prone to accidents (Misra et al., 2003).

The increasing road accidents and traffic jams are caused by weak road management systems and a lack of urban transport regulator. Increasing road accidents and traffic jams within the Kampala business district of Uganda cost businesses 23,813 person-hours per day (Kamuhanda & Schmidt, 2009).

Road accidents cause (a) damage and destroy business assets and human capital, (b) increase stress to health facilities, and (c) death of family members and societal and communal settings without strategies to stem the causes (Osoba, 2012). The Uganda Police (2010) indicated that there were 2,954-traffic fatalities in Uganda. According to the Uganda Bureau of Statistics (UBOS; 2010 ), road crashes increased by 30% from 11,758 in 2008 to 22,699 in 2009, and about 33,900 vehicles were involved in road crashes during 2010. There are increasing traffic fatalities and destruction of vehicles involved in traffic crashes (Ministry of Works and Transport [MOWT], 2011).

1.2 Statement of the problem

The road traffic collisions in the business district of Kampala, Uganda cost the Uganda economy 2.9% of the GDP with 2,954 traffic fatalities  reported in 2010 (WHO, 2013). Road crashes, injuries, and deaths cost Uganda US $101 million each year (WHO, 2013).

The current weak transport, regulatory agencies, and the poorly maintained roads contribute to traffic congestion and road accidents and, consequently, affect the business and economy of Uganda (Kiggundu, 2007; Sietchiping, Permezel, & Ngomsi, 2012). Roads are among the conduits for transporting goods and services (Cornish & Mugova, 2014; Dewar, 2011; Gollin & Rogerson, 2010; Uganda National Road Fund [UNRF], 2010b).

The roads sector in Uganda is some of the most funded institutions in Uganda, however despite of the investment by the government the roads accidents in Uganda are some of the highest in Uganda, this study therefore intends to investigate into trends of road transport analysis in Uganda

1.3 purpose of the study

The study intends to examine the trends in road accidents in Uganda from 2006-2015.

1.4 Objectives of the study

1. To determine the distribution of accidents in Kampala.
2. To compare accidents prevalence by residence and region.

• To forecast accident prevalence.

1.5 Hypothesis of the study

1. Ho_1: There is no trend for accidents prevalence in Kampala.
2. Ho_2: There is no seasonality for accidents prevalence.

1.6 Scope of the study

1.6.1 Content scope.

The study will specifically provide in-depth examination on trends in road accidents in Uganda.

1.6.2 Time scope.

The study will be carried out from February to August 2017.

1.7 Significance of the study

The findings of the study will be beneficial in the following ways;

1. The study will help policy makers have information regarding the challenges that is brought about in the country as a result of road accidents
2. The study will also help academicians have information on the causes of road accidents.

• The study will help the government officials have knowledge and understand how to mitigate the risks that they face in road accidents.

CHAPTER TWO

LITERATURE REVIEW

2.1 Causes of road accidents

The increasing road accidents and traffic jams are caused by weak road management systems and a lack of urban transport regulator. Increasing road accidents and traffic jams within the Kampala business district of Uganda cost businesses 23,813 person-hours per day (Kamuhanda & Schmidt, 2009). Road accidents cause (a) damage and destroy business assets and human capital, (b) increase stress to health facilities, and (c) death of family members and societal and communal settings without strategies to stem the causes (Osoba, 2012). The Uganda Police (2010) indicated that there were 2,954-traffic fatalities in Uganda. According to the Uganda Bureau of Statistics (UBOS; 2010 ), road crashes increased by 30% from 11,758 in 2008 to 22,699 in 2009, and about 33,900 vehicles were involved in road crashes during 2010. There are increasing traffic fatalities and destruction of vehicles involved in traffic crashes (Ministry of Works and Transport [MOWT], 2011).

The leaders of developing countries lack adequate technical resources and expertise to build safer roads, leading to poorly constructed roads. Few trained professionals are available, and those who are present may lack sufficient road management and road safety knowledge, may be unable to use an appropriate interdisciplinary approach, or are not familiar with recent developments and techniques (Roehler et al., 2013; Transport Research Laboratory, 1991). The existing institutional capability does not adequately cope with road construction and urban transportation demands.

A high percentage of persons and vehicles involved in the road crashes affect human capital and business assets (Demyttenaere et al., 2009). The lack of institutional capacity to maintain, rehabilitate, and reconstruct roads within the RMS leads to poorly maintained roads, missing or incorrect road signs and markings, and poor vertical and horizontal alignments, which render road users prone to accidents (Misra et al., 2003;MOWT, 2011). The increasing road accidents and traffic jams phenomenon increases the competition at regional and global levels for cities’ capital investment. Table 1 shows the road fatalities of different road users in Kampala between 2007 and 2010.

Road Traffic Fatalities in Kampala between 2007 and 2010

Note. Source: Uganda Police – Ministry of Internal Affairs

Within the urban transport sector, analyses of supply chain management, detailed value chain, general forces matrix, or Porter’s industry forces are seldom performed (Benmaamar et al., 2002; Carlan, Rosca, & Rosca, 2014). Benmaamar et al. (2002) cited barriers to entry in the Porter’s industry forces analysis, economics, and government in the external general industry forces analysis and supply chain management. The lack of (a) robust urban transport regulator, (b) requirement of minimum amount of capital investment in TOTS, (c) expertise knowledge on urban transport management, and (d) size vehicle fleet encourage new entrants to urban transportation business. The lack of TOTS expertise knowledge as a barrier of entry to urban transport business tends to increase informal entrepreneurship. Informal entrepreneurs thrive on business duplication without differentiation leading to high competition in the market. Informal entrepreneurship is prevalent in Uganda. The driver for informal entrepreneurship is social capital (Amu, Offei-Ansah, & Gavor, 2012; Da Felice & Martucci, 2012; Szerb et al., 2007).

Considering the barriers to entry, transport operations, and transport services, business management style encourages starters without the necessary expert knowledge, economies of scale, cost advantages, and technology to enter the TOTS business. The time and cost for new entrants moving into the urban transport market are not a formidable barrier to operate in the transportation sector. The practice of encouraging new entrants has allowed lapses in the process of issuing driver license and vehicle operating license, reflected in the form of traffic injuries and fatalities because cars and buses are not mechanically worthy because drivers have not been adequately trained to learn how to drive (Madeley, 2004; Ramessur, Seetanah, & Rojid, 2010).

There is no proper business analysis regarding the acquisition of vehicles, drivers, mechanics, spare parts, transport governing regulations, maintenance workshops, and taxes on the transportation business done in Uganda as it hinges on informal entrepreneurship (Benmaamar et al., 2002). Road accidents, which lead to a loss of business assets, entrepreneurship, and transportation services, are the result of a lack of proper business analysis. Furthermore, political leadership that favors foot hawkers, open-air markets, and motorcyclists operating without safety gear lead to increased frequencies of road accidents. These political decisions contribute to traffic congestion and high passenger travel time (Mahmud, Gope, & Chowdhury, 2012; Santosa, 2011).

The Ugandan economy and state had steady growth in between 1986 and 2001 following a peaceful and stable state, enabling growing investment in TOTS (Demyttenaere et al., 2009; Mulengani, 2009). The concentration of TOTS’ investments within the Kampala business district resulted in traffic congestion. Traffic congestion is counterproductive because the time for employees to be productive for the businesses diminishes, increasing employee turnover (Adler, Alfred, Kornbluth, & Sher, 2012; Barrett-Gaines, 2005; Huggins, 2012). Kiggundu (2007) asserted that commuters in Kampala lose 23,813 person-hours per day due to traffic jams. There is a loss of businesses profitability due to late arrival of employees to start work.

2.2 Different ways of reducing roads accidents

Kiggundu and Mukiibi (2012) attributed inadequate parking space, dilapidated and neglected road network, lagging public transport, and inadequate local funding to traffic congestion within Kampala. Recommendations are to use overhead or underground parking as well as improved public transport services to de-congest the city. To achieve the de-congesting of the city with traffic, the private and public developers should invest in constructing car parking facilities and bus terminals.

Kobusingye, Guwatudde, and Lett (2001) considered traffic accidents as a major killer along with others such as malaria, tuberculosis, and human immune virus/acquired immune deficiency syndrome (HIV/AIDS). The goal of the study was to show that the leading causes of fatalities and disabilities in the city were due to road traffic. The study findings contrast with a similar study conducted in the United States in a similar setting, in which traffic crashes and fatalities were due to speeding on rural interstate roads (Friedman, Hedeker, & Richter, 2009). The particular contrast is attributable to the different geographical locations and level of growth in the two countries. Differences and contrasts in transportation are due to current policies, behavioral orientation of citizens, and sustainability (Bamberg et al., 2011; Barr et al., 2010; Holden & Linnerud, 2011; Prillwitz & Barr, 2011; Xenias & Whitmarsh, 2013).

Mutto, Kobusingye and Lett (2002) evaluated the effect of an overpass on pedestrian injuries in the study area to ascertain the perceptions of the participants. The concern was that traffic accidents continued to occur at the overpass area notwithstanding the existence of an overpass as a safety provision. The goal of the study was to show that male participants had a mindset of being brave and strong enough to walk through traffic as compared to the female participants. The safety issue was not paramount to the men.

Benmaamar et al. (2002) conducted a study on TOTS of the public transport particularly minibuses within the study area and other two locations in the east and west of Uganda. The purpose of the study was to show that cars are more than eight years old without proper routine maintenance and with drivers possessing insufficient training kills. Benmaamar et al. recommended a review of the vehicle importation policy and adoption of private-public partnerships in TOTS.

The Benmaamar et al. (2002) study recommendations provided the foundation for this current study. The current study builds upon the identified gap in the literature as lack of strategy management review. The strategy management analysis on the routine maintenance of cars, driver training under the road safety and car importation policy within TOTS is lacking.

2.3 Difference in roads accidents by regions

Road management scholars estimated that road maintenance costs are 10% of the vehicle operation cost (VOC), whereas road users incur 90% road transport costs (Rheinberger, 2011; Thriscutt, 2010b). Luyimbazi (2007) analyzed and published that the existing road maintenance status in comparison to the desired state would create the VOC saving of U.S. $69.93 million, equivalent to 1.5% of national GDP. The shortfallon road maintenance status exhibited as the created VOC saving transcends as an extra cost of U.S. $251.45 per motorist annually.

The leaders of the MFP&ED pay for road construction and maintenance with the assistance of various development partners such as the World Bank, European Union, and Africa Development Bank through the National Road Fund. Locally, money generated from taxes levied upon the road users in the form of vehicle operating licenses and fueland lubricants used to run the cars. The opinion of the practitioners of road management is that the heavy road users should pay more and have their consent on expenditure of the funds they pay (Thriscutt, 2010c; Yunus, & Hassan, 2010). The aid from development partners is in grants and loans that attract substantial interest rates that are not sustainable.

CHAPTER THREE

METHODOLOGY

3.0  Introduction

This section presents a detailed description on how the study will be carried out and collecting the necessary data for the study. It therefore covers the research design, study area, data sources, data processing, data analysis techniques and anticipated limitations of the study.

3.1 Research Design

The study shall use quantitative methods of research so as to obtain the viable data and this shall include structured secondary data in the records of central police station Kampala.

3.2 Data Sources.

Secondary data will be obtained from the data base, records, publications and journals in the Central Police Station Kampala.

3.3 Data processing and Data analysis techniques

The process of data processing will involve editing in order to check for errors and omissions and coding to reduce the data to a meaningful pattern of responses. Model specification and soft wares employed in the tabulation and processing of the findings will be done in order to prepare data, analyze and compile a research report.

The study will use time series analysis and descriptive statistics will be used to describe the information got from the field this will be inform of graphs and tables

Data Analysis will involve applying statistical techniques on it for easy presentation. It will include the interpretation of research findings in the light of the research questions, and objectives to determine if the results are consistent with those research questions.

3.4 Descriptive analysis.

Time series analysis

By the nature of data which is the time series

The analysis however will concentrate on trend and seasonality of maternal prevalence

Assuming a multiplicative model, then 𝑌𝑡=𝑇𝑡∗𝑆𝑡

Where 𝑌𝑡 is the mortality series, 𝑇𝑡 is Trend and 𝑆𝑡 is the seasons.

This employs ARIMA modeling and it includes the following data exploration techniques.

a. Graphical presentation

This will involve plotting the series 𝑌𝑡 against time t.

b. Non parametric tests for trend

Run’s test: The runs test (Bradley, 1968) can be used to decide if a data set is from a random process.

A run is defined as a series of increasing values or a series of decreasing values. The number of increasing, or decreasing, values is the length of the run. In a random data set, the probability that the (i+1)^th value is larger or smaller than the i^th value follows a binomial distribution, which forms the basis of the runs test. Testing procedure

Ho: The Road accidents series is stationary

Ha: The Road accidents series is non-stationary.

Test statistic

Where m=number of pluses Decision rule is at α=0.05

The researcher will reject Ho if Z>𝑍_∝/2 i.e. if the computed Z statistic is greater than the notable value and then conclude with (1-α)*100% confidence, the series has trend.

Test for seasonality

In this research, the researcher will use the Kruskal-Wallis test which is an alternative for the parametric one-way analysis of variance test, if there are two or more independent groups to compare (Siegel & Castellan 1988).

The test is described as below; Ho: the series has no seasonality Ha: the series has seasonality

Test statistics, H to compare with  (Chi square)

n_i is the number of observations in the i^th season N is the total number of specific seasons

Ri= 𝑟𝑎𝑛𝑘 (𝑦𝑖) Yi is the specific season for time t. Critical region

Reject Ho if

3.5 Autoregressive Integrated Moving Average (ARIMA)

This is also known as the Box-Jenkins model. This methodology will be used to forecast the Road accidents. The model is based on the assumption that the time series involved are stationary. Stationary will first be checked and if not found, the series will be differenced d times to make it stationary and then the Autoregressive Moving Average (ARMA) (p, q) will be applied. The ARIMA procedure provides a comprehensive set of tools for univariate time series model identification, parameter estimation, and forecasting, and it offers great flexibility in the kinds of ARIMA models that can be analyzed. The ARIMA procedure supports seasonal, subset, and factored ARIMA models; intervention or interrupted time series models; multiple regression analysis with ARMA errors; and rational transfer function models of any complexity. The Box-Jenkins methodology has four steps that will be followed when forecasting maternal prevalence;

Identification.0 This involves finding out the values of p, d, and q

where;

p is the number of autoregressive terms

d is the number of times the series is differenced

q is the number of moving average terms

The identification here will be done basing on the correlogram plot obtained. Where both autocorrelation and partial correlation cuts of at a certain point, we conclude that the data follows an autoregressive model. The order p, of the ARIMA model is obtained by identifying the number of lags moving in the same direction. In case the series was non stationary, the number of times we difference the series to obtain stationarity is the value of d.

Estimation. This involves estimation of the parameters of the Autoregressive and Moving average terms in the model. The nonlinear estimation will be used.

Diagnostic checking. Having chosen a particular ARIMA model, and having estimated its parameters, we now examine whether the chosen model fits the data reasonably well. The simple

test of the chosen model will be done to see if the residuals estimated from this model are white noise. If they are, we can accept the particular fit and if not, the model will have to be started over.

Forecasting. Exponential smoothing methods will be used for making forecasts. While exponential smoothing methods do not make any assumptions about correlations between successive values of the time series, in some cases you can make a better predictive model by taking correlations in the data into account. Autoregressive Integrated Moving Average (ARIMA) models include an explicit statistical model for the irregular component of a time series that allows for non-zero autocorrelations in the irregular component.

The forecast for the year 2018 will be done by regressing maternal mortality prevalence against time

The residence and region will be analyzed using the ANOVA test by regressing Road accidents prevalence (dependent) on the dummies for place of residence and dummies for region using SPSS since residence and region are both categorical independent variables.

   Y_t =β_o+β₁D_R+β₂D_C+β₃D_N+β₄D_E

Where Yt is the road accidents prevalence at the time in a given region and residence

D_R is a dummy for rural , D_R =1 if Rural , 0 other wise

D_C is a dummy for central, D_c=1 if central, 0 otherwise

D_N is a dummy for north , D_N= 1 if North , 0 otherwise

D_E is a dummy for East, D_E=1 if East, 0 otherwise

3.6 Ethical considerations

The researcher will begin data collection by explaining the purpose of the research, which basically meant to help decision makers of Central police station. Respondents will be informed that the purpose of the information shall be strictly for academic purposes only and the information provided will be treated with highest level of confidentiality.