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ASTATISTICALANALYSISOFROADTRAFFICACCIDENTSINDIBRUGARHCITY,ASSAM,INDIA

ARTICLE·JANUARY2011

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AjitGoswami

IndianCouncilofMedicalResearch

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Dr.RipunjoySonowal

DibrugarhUniversity

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ISSN 1941-689X

A STATISTICAL ANALYSIS OF ROAD TRAFFIC ACCIDENTS IN

DIBRUGARH CITY, ASSAM, INDIA

Ajit Goswami* and Ripunjoy Sonowal

Division of Epidemiology and Nutrition, Regional Medical Research Centre,

(Indian Council of Medical Research), N.E. Region, Dibrugarh, Assam, India

Email: agoswami09@gmail.com; ripunjoy_rmrcicmr@yahoo.co.in

ABSTRACT

Background: Road traffic accidents (RTAs) have turned out to be a huge global public health and development problem. Assam one of the federal states in North East India has a high rate of accidents and deaths in relation to number of vehicles on the road. Realizing the need to establish baseline information on RTAs, the present study was conducted in Dibrugarh city under the jurisdiction of Dibrugarh Police Station, Assam, India.

Methods: Complete RTA data of the year 2009 from case dairies and police records of Dibrugarh Police station were studied. SPSS (13.0) and Ky-plot software with bivariate comparisons were used. Data interpretation was done using Degree of freedom, Chi–square

test for goodness of fit, 2χ – test for independence of attributes and Kruskal-Wallis test.

Results: Human characteristics (rush and negligence) make 95.38% of the total RTAs. 60% of the accidents were recorded during day time (6 AM to 6 PM). The peak time was between 12 PM to 6 PM (38.46%). The highest numbers of accidents (32.30%) were observed in the heavy rainy season during the months of July – September.

Conclusion: The fewer data on accident reports at police stations are an indicative of lack of awareness of accidents reporting. RTAs are preventable and in order to combat the problem there needs to be close coordination and collaboration, using a holistic and integrated approach, across many sectors and many disciplines.

Key words: RTAs, Degree of freedom, Chi–square test, Kruskal-Wallis test, Dibrugarh

INTRODUCTION:

Accident is an event, occurring suddenly, unexpectedly and inadvertently under

unforeseen circumstances. Road traffic accidents can be defined as “An accident that

occurred on a way or street open to public traffic; resulted in one or more persons being

killed or injured, and at least one moving vehicle was involved. Thus, RTA is collisions

between vehicles; between vehicles and pedestrians; between vehicles and animals; or

between vehicles and geographical or architectural obstacles” [1]. Road traffic accidents

(RTAs) have turned out to be a huge global public health and development problem killing

almost 1.2 million people a year and injuring or disabling between 20-50 million people more.

* Corresponding author

2

The statistical profile reflects that in 2002, an estimated 1.2 million people were killed and

50 million injured in road-traffic crashes worldwide, costing the global community about

US $ 518 billion [2]. The International Federation of Red Cross and Red Crescent Societies

have described the situation as “a worsening global disaster destroying lives and livelihoods,

hampering development and leaving millions in greater vulnerability” [3]. Without appropriate

action, RTAs and its resultant injuries are predicted to escalate dramatically as a global

burden of disease by 2020 [4], particularly in rapidly-motorizing countries. In the developing

world, current trends in population growth, industrialization and urbanization are putting

heavy pressure on the transport network in general and on road system in particular. Some of

the unwanted side-effects of this growth in traffic, such as congestion and noise are

immediately obvious to the individual citizen. Others, such as the growing number of deaths

and injuries from RTAs are apparent only through aggregated statistics. Compared to the

cause of death more commonly associated with the developing countries, deaths from road

accidents are by no means insignificant. This reveals a serious and growing problem, with

absolute fatality and casualty figures rising rapidly in the majority of developing countries

and with death rates considerably higher than in the developed world [5]. Around 85% of all

road traffic-related deaths in the world occur in developing countries [6]. Among the

developed countries like the United States of America, Japan and the United Kingdom traffic-

related death rate (per 100,000 inhabitants per year) stood at 12.3, 3.85 and 3.59 respectively

in the year 2009 [7, 8, 9]

. In addition, Road Traffic Injuries (RTIs) accounts for 30 to 86% of

the trauma admissions to hospitals in low income and middle income countries [2]. Reasons

for high burden in road traffic-related deaths and injuries in developing countries are

primarily due to Growth in motor vehicle numbers, Poor enforcement of traffic safety

regulations, Inadequacy of public health infrastructure, Poor access to health services, etc. in

comparison to the developed nations [10]. Apart from the humanitarian aspect of the problem,

traffic accidents and injuries in these countries incur an annual loss of $ 65 billion to

$100 billion annually. These costs include both loss of income and the burden placed on

families to care for their injured relatives.

India has a high incidence of serious road accidents. According to official statistics

0.11 million deaths occurred in India due to road traffic accidents in 2006, which is nearly

10% of the total road traffic deaths in the world [11]. A large percentage of injuries go

unreported due to lack of a systematic injury information system. Moreover, the situation in

India is worsening as RTAs and RTIs have been increasing over the past twenty years. This is

3

partly due to the increase in the number of vehicles on the road and partly due to the absence

of a coordinated official policy to control the problem. Assam one of the federal states in

North East India has a high rate of accidents and deaths in relation to number of vehicles on

the road. According to the National Crime Records Bureau (NCRB) of India [12]

, the rate of

accidental deaths per 1000 vehicles in Assam stood at 1.9 in the year 2008. Whereas, the

same for the Indian states of Arunachal Pradesh and Sikkim was 5.7 and 3.6 respectively. The

report of the Economic Commission for Europe 2007 [13] shows that, the rate of accidental

deaths per 1000 vehicles in Liechtenstein was 19.0 followed by France at 15.8 in the year

2003. During the year 2008 a total of 4262 road accidents took place in Assam and 1721

people lost their lives [14]. This ever expanding epidemic is likely to take a heavy burden on

the quality of life and socio-economic growth of the state.

Many research studies incorporating different aspect of RTAs have been carried out

by different workers at different times across the globe [15, 16, 17, 18, 19, 20]. However, such

epidemiological studies from North East India are very much scanty. Moreover, to our

knowledge till date there is no such study ever conducted in Assam. Therefore, realizing the

need to establish baseline information on RTAs, the present study was conducted in

Dibrugarh city (HQ of Dibrugarh district) under the jurisdiction of Dibrugarh Police Station,

Assam, India with the following objectives:

• To find whether the accidents are uniformly distributed over the year or not i.e.

whether all the accidents occur with equal frequency.

• The seasonal distribution of accidents.

• The hourly distribution of accidents.

• To find whether all the causes are equally responsible for causing accident.

Research hypotheses

(1) Accidents are uniformly distributed over the year.

(2) Occurrence of accidents is uniform throughout the seasons.

(3) Occurrence of accidents is uniform throughout the hours of the day.

(4) All the causes are equally responsible for causing accidents.

4

MATERIALS AND METHODS:

Study Area:

Dibrugarh, the Tea City of India is situated on the banks of the River Brahmaputra, in

Upper Assam, India and is the gateway to the three tea producing districts of Dibrugarh,

Tinsukia and Sivasagar. Dibrugarh district has the world's largest area covered by tea

gardens. The entire district is surrounded by tea plantations and has many tea factories, most

of the tea gardens are more than 100 years old. The district HQ is located at Dibrugarh city.

The city is well linked by roads, railway, airway and waterway. Earlier transportation within

the city was largely by buses, rickshaws and auto rickshaws. But, recently three and four

wheeled vehicles locally known as "Vikram, Garuda, Magic, etc." have come up in a big way

to help in both within-the-city and also short distance outside the city transportation.

Nature of data:

The present study is based on secondary source of data i.e. from the case diaries and

police records of the accident cases in Dibrugarh Police station. The official records were

available from 2001 – 2010. Simple random sampling technique was applied that resulted in

the selection of the year 2009. Complete enumeration of data for one calendar year (January

- December’ 2009) was done. Besides, other relevant information’s were collected from the

concerned officials through interviews and personal discussions. It will be pertinent to note

here that, police records mention only the number of accidents occurred and not the type or

nature of accidents i.e. severity of injuries, fatalities or any other medico-legal records.

Statistical Analysis:

Data were analyzed using Statistical Package for Social Sciences (SPSS) version 13.0

and Ky-plot software with bivariate comparisons. P value below 0.05 was considered as

statistically significant.

Data Interpretation:

The following methods were applied to analyze the data -

a) Degree of freedom (d.f): The number of independent variants which make up the

statistic (e.g. 2χ ) is known as the degree of freedom (d.f.) and is usually denotes by υ (the

letter ‘Nu’ of the Greek alphabet).

The number of degree of freedom, in general, is the total number of observational less

the number of independent constraints imposed on the observations. For example, if k is the

5

number of independent constraint in a set of data of n observations then υ= (n-k). Thus in a

set of n observations usually, the degree of freedom for 2χ are (n-1), one d.f. being lost

because of the linear constraint i i

i i

O E N= =∑ ∑ , on the frequencies. If ‘r’ independent

linear constraint one imposed on the cell frequencies, then the d.f. reduced by ‘r’.

In addition, if any of the population parameter(s) is calculated from the given data and

used for computing the expected frequencies then in applying 2χ -test of goodness of fit, we

have to subtract one d.f. for each parameter calculated. Thus if‘s’ is the number of population

parameters estimated from the sample observation (n in number), then the required number of

degree of freedom for 2χ test is (n-s-1).

If any one or more of the theoretical frequencies are less than 5 then in applying 2χ -

test we have also to subtract the degrees of freedom lost in pooling these frequencies with the

preceding or succeeding frequency. In a (r × s) contingency table, in calculating the expected

frequencies, the row totals, the column totals remain fixed. The fixation of ‘r’ column totals

and ‘s’ row totals imposes (r+s) constraints on the cell frequencies [21]. But since

1 1

( ) ( )r s

i j

i j

A B N= =

= =∑ ∑

the total number of independent constraints is only (r+s-1) (Table 1). Further, since the total

number of the cell frequencies is r+s, the required number of degrees of freedom is

( 1) ( 1)( 1)v rs r s r s= − + − = − −

b) Chi – square test for goodness of Fit: Formulated by Prof. Karl Pearson in 1900, it is a

very powerful test for testing significance of the discrepancy between theory and experiment.

It enables us to find if the deviation of the experiment from theory is just by chance or is it

really due to the inadequacy of the theory to fit the observed data.

If Oi, (i= 1, 2,. . . n) is a set of observed (experimental) frequencies and Ei (i=1, 2, . . .,

n) is the corresponding set of expected (theoretical or hypothetical) frequencies, then Karl

Pearson’s Chi-square statistic given by

( )22

1

ni i

i i

O E

Eχ

=

− =

∑ … (1.1) 1 1

n n

i i

i i

O E= =

=

∑ ∑

follows Chi-square distribution with (n-1) d.f.

6

c) 2χ – test for independence of attributes: The 2χ - test of independence of attributes is

also applied to test the independence of season and accident, and also to test the

independence of hours of the day and the accident. The test is as follows -

Let us consider two attributes A and B, A divided into r classes A1, A2,. . ., Ar and B

divided into s classes B1,B2,…,Bs. Such a classification in which attributes are divided into

more than two classes is known as manifold classification. The various cell frequencies can

be expressed in the following table known as r×s manifold contingency table where (Ai) is

the number of person possessing the attributes Ai, (i=1, 2,…, r), (Bj) is the number of person

possessing the attribute Bj (j=1,2,…,s) and (AjBj) is the number of person possessing both the

attributes Ai and Bj [ i= 1, 2, …, r; j = 1, 2, …, s]. Also,

( ) ( )1 1

r s

i j

i j

A B N= =

= =∑ ∑ is the total frequency.

Table 1: r × s Contingency Table

B A A1 A2 . . . Ai . . . Ar Total

B1 (A1B1 ) (A2B1 ) . . . (AiBi ) . . . (ArB1 ) (B1)

B2 (A1B2 ) (A2B2 ) . . . (AiB2 ) . . . (ArB2 ) (B2)

.

.

.

Bj (A1Bj ) (A2Bj ) . . . (AiBj ) . . . (ArBj ) (Bj)

.

.

.

Bs (A1Bs ) (A2Bs ) . . . (AiBs ) . . . (ArBs ) (Bs)

Total (A1) (A2) . . . (Ai) . . . (Ar) N

The problem is to test if two attributes A and B under consideration are independent or not.

Under the null hypothesis that the attributes are independent, the theoretical cell

frequencies are calculated as follows:

P [Ai] = probability that a person possesses the attribute Ai

= ( )

; 1, 2 , . . . ,iA

i rN

=

P [Bj] = probability that possesses the attribute Bj

= ( )

; 1, 2 , . . . ,jB

j sN

=

P [Ai Bj] = probability that a person possesses the attributes Ai and Bj = P (Ai) P (Bj)

7

(By compound probability theorem, since the attributes Ai and Bj are independent, under the null hypothesis)

( ) ( )( )[ ] , 1, 2,..., ; 1, 2,...,

ji

i j

BAP A B i r j s

N N= = =

(AiBj) 0 = Expected number of persons possessing both the attributes Ai and Bj

=

( )( ).

i j

i j

A BN P A B

N =

( ) ( )( )( )

0, 1, 2,..., ; 1, 2,...,

i j

i j

A BAB i r j s

N= = =

By using this formula we can find out expected frequencies for each of the cell

frequencies (AiBj) (i =1, 2,…, r; j=1, 2,…,s) under the null hypothesis of independence of

attributes. The exact test for the independence of attributes is very complicated but a fair

degree of approximation is given, for large samples (large N), by the chi-square test of

goodness of fit, viz.

( ) ( ){ }( )

2

2

1 1

r s i j i j o

i j i j o

A B A B

A Bχ

= =

− =

∑ ∑

Which is distributed as a 2χ - variate with (r-1) (s-1) d.f. Reject the null hypothesis if

calculated 2χ > tabulated 2χ at 5 % level of significance.

d) Kruskal-Wallis Test (H-Statistic):

This non-parametric test for k (k ≥ 2) independent random samples of possibly

different sizes is the non-parametric counterpart of Analysis of Variance (ANOVA). Here the

null hypothesis to be tested is that all of k - population distribution functions are identical.

To compute H-statistic, we first combine all the k-samples, then rank together all the

observations from the smallest to the largest such that rank ‘1’ goes to the smallest and rank

nk to the largest one where N= (n1+n2+…+ nk). The sum of all N ranks is ( )12

N N + . The

ith sample of size ni is expected to have rank-sum ( 1).

2

inN N

N

+ if H0 is true. The observed

value of the rank-sum for the ith sample is denoted by Ri. Then the Kruskal-Wallis statistic H

is defined as the weighted sum of squares of deviations of Ri from the expected value (under

H0)( 1)

2

jn N +, i.e. as

( )2

1

112 1

( 1) 2

k

i

i i

N NH R

N N n=

+= −

+ ∑

An equivalent form of H, which is simpler for calculations, is

8

2

1

123( 1)

( 1)

ki

i i

RH N

N N n=

= − ++ ∑

Kruskal has shown that if no. ni is small, then H is distributed asymptotically as a chi-

square with d.f. = (k-1). The rejection at the level α is given by2

,( 1)kH αχ −> . When k is

small, say k = 3, and the ni are also small, the 2χ - approximation is not good. For such cases

exact probabilities have also been tabulated.

When ties occur, a correction has to be made. The corrected H-statistic is defined as

( )3

3

11

c

HH

t

N N

= − −

−

∑

Where t = no. of observations tied for a given rank in each sample group.

RESULTS:

a) Chi-square Test for Goodness of Fit -

Table 2: Calculation for Chi-Square

Months Observed

frequency

Expected

frequency

d.f. Critical 2χ Calculated 2χ

January 8 5.416

February 1 5.416

March 4 5.416

April 8 5.416

May 5 5.416

June 5 5.416

July 7 5.416

August 8 5.416

September 6 5.416

October 4 5.416

November 3 5.416

December 6 5.416

Total 65 65

11

19.675

9.762

Table 2 shows a calculated 2χ - value of 9.762 for 11 d.f. and a critical 2χ - value of

19.675 at 0.05 alpha level. Since the calculated 2χ - value is less than the critical

2χ - value,

the No. 1 null hypothesis i.e. accidents are uniformly distributed over the year is accepted.

Thus, it indicates that accidents have a uniform distribution over a year.

9

b) Chi-Square Test for Independent of the occurrence of accident and the season -

Table 3: Association between the Occurrence of Accidents and the Season

Number of accident

Time or Month

= 0

> = 1

Total

January - March

1st Quarter

77 (74.18)

13 (15.81)

90

April - June

2nd Quarter

73 (74.79)

18 (16.51)

91

July - September

3rd Quarter

71 (75.62)

21 (16.51)

92

October - December

4th Quarter

79 (75.86)

13 (16.17)

92

Total 300 65 365

d.f. 3

Calculated 2χ 3.0387

Critical 2χ 7.815

* Expected frequency is in parentheses

Table 3 indicates a calculated 2χ - value of 3.0387 and a critical 2χ - value of 7.815 at

0.05 alpha level. Since the calculated 2χ - value is less than the critical

2χ - value the null

hypothesis No. 2 i.e. Occurrence of accidents is uniform throughout the seasons is accepted.

Hence, the number of accidents does not depend on the seasons. Moreover, there is lack of

enough evidence to indicate such a seasonal pattern.

c) Chi-square Test for Goodness of Fit -

Table 4: Calculation for Chi-Square

Hours Observed

frequency

Expected

frequency

d.f. Critical 2χ Calculated

2χ

6.00 AM - 12.00 PM 15 16.25

12.00 PM - 6.00 PM 25 16.25

6.00 PM - 12.00 AM 21 16.25

12.00 AM - 6.00 AM 4 16.25

3

7.815

15.43

Total 65 65

Table 4 shows a calculated 2χ - value of 15.43 for 3 d.f. and a critical 2χ - value of

7.815 at 0.05 alpha level. Since the calculated 2χ - value is greater than the critical

10

2χ - value, the No. 3 null hypothesis i.e. accidents are uniformly distributed over the hours of

the day is rejected. Thus, it indicates that accidents are not uniformly distributed over the

hours of the day.

Kruskal-Wallis Test -

Table 5: Quarter wise distribution of causes of accidents

Quarter Rush and Negligence Bad Weather Defect of Light

1st Quarter 12 1 0

2nd Quarter 17 0 1

3rd Quarter 21 0 0

4th Quarter 12 0 1

Using Ky-plot package, we get the following calculation –

X Rank Y Rank Z Rank

12

12

17

21

9.5

9.5

11

12

0

0

0

1

3

3

3

7

0

0

1

1

3

3

7

7

Table 6: Calculation of Kruskal- Wallis Statistic using Ky-plot software

X Y Z Total

N

Mean

Variance

Median

Rank Sum

Rank Mean

4

15.5

19

14.5

42

10.5

4

0.25

0.25

0

16

4

4

0.5

0.333

0.5

20

5

12

5.416

60.81

1

78

6.5

2χ 8.26

d.f. 2

p - value 0.016

Table 6 indicates a calculated 2χ - value of 8.26 and the p-value of 0.016 at

0.05 alpha level. Since the p - value is less than 0.05, the 4th null hypothesis i.e. all the causes

are equally responsible for causing accidents is rejected. The result shows that there is a

difference in occurrence of accidents due to different causes.

11

DISCUSSION AND CONCLUSION:

Analysis of qualitative data gathered during the present study summarizes two

principle factors viz. human and environment as joint significant contributor to the

occurrence of RTAs in Dibrugarh city. Human characteristics (rush and negligence) make the

highest contribution (95.38%; table 5) to the road traffic accidents in the study area. Using

binomial distribution (human errors, not human errors) the 95 percent Confidence Interval

(CI) = 89% to 100%. The environmental factors are related to bad weather and poor road

condition. In the present study, the highest number of accidents (32.30%; table 3) were

observed in the peak rainy season during the months of July - September and the maximum

number of victims were also highest compared to other seasons of the year. The present study

recorded more than 60% of the accidents during day time (6 AM to 6 PM). These times

coincide with the period when people are more active and mobile. The peak time was

between 12 PM to 6 PM (38.46%; table 4). These hours are the busiest as there is heavy rush

of commuters from schools, offices, factories, business places, etc. Between 6 PM to 12 AM

also a high number of RTAs were observed (32.30%; table 4). During this time period the

roads are opened for heavy vehicular movements.

Data on road traffic accidents in Dibrugarh city are very poor. Police records are the

only source of information but, many accident cases are never reported while others are

settled privately. The fewer data on accident reports at police stations are an indicative of lack

of awareness of accidents reporting. Based on police data it is not possible to make routine

analysis and therefore it is impossible to implement safety measures. RTAs are preventable

and in order to combat the problem there needs to be close coordination and collaboration,

using a holistic and integrated approach, across many sectors and many disciplines.

Appendix

Some related information and observations:

Apart from the findings of the present study, the authors would like to share some

additional observations and personal comments related to the causes of road accidents. But,

the findings lack statistical figures as these were not included under the study objectives.

Major human factors that contribute to the potency of accident causation also include alcohol

or drug intake, indecisiveness, fatigue, distraction and confusion. Similar observations of

increased use of alcohol were also made by Mishra et.al. (1984) [22] and Gururaj (2001) [23].

In addition, in most of the cases the driver are found to be inexperience, risk taker, impulsive,

12

aggressive and casual and don’t know the road signals. It is seen that most of the bikers,

particularly the young boys drive at high speed without wearing safety helmet. Another

important factor that has been noticed in highway accidents in and around the study area is

that most of drivers usually the truck driver never use dipper at night which creates problems

to the vehicle coming from opposite direction.

The condition of most of the traffic markings on the roads of Dibrugarh city are in a

very pathetic state. The speed breakers itself become the cause of many accidents due to no

markings on them. In winter the cause of many accidents is fog which diminished the

visibility and hamper driving. This observation is further corroborated by similar findings of

Dixey (1999) [24], Asogwa (1992) [25] and Ogunsaya (1991) [26]. During rainy season certain

kinds of stone aggregate become smooth under the constant wearing action of vehicle tyres

leading to poor wet-weather traction. This results in vehicle crashes by increasing braking

distances or contributing to loss of control. Moreover, if the pavement is insufficiently sloped

or poorly drained, standing water on the surface can also lead to wet-weather crashes due to

hydroplaning. Again heavy rain fall creates cracks on the roads which gradually become big

holes, thus making traffic mobility difficult and sometimes cause road accidents.

All these problems become more serious since Dibrugarh and Assam at large are

slowly becoming highly motorized places. Here the same road space is used by modern cars

and buses, along with locally available vehicles for public transport (3-wheeled scooter taxis

and rickshaws), scooters, motorcycles and bicycles. In fine, the following recommendations

are suggested in order to minimize the problem:

1. The role of law enforcing authority is solely important. The use of safety helmet and belt

must be enforced as well as strict rules regarding license issuing, ban of using mobile

phone while driving, etc.

2. The Government and NGOs must create public awareness to prevent road accidents by

organizing seminars, road dramas, plays, etc. Moreover, mass Behaviour modification

and education regarding road safety should be imparted from the school level.

3. The marking of dividers and speed breakers must be done. Immediate steps to remove all

the aged and damaged vehicles off from the roads will definitely be a welcoming step.

13

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