Integrated Mathematics I.A ( Internal Assessment) Carlos Gonsalves Guyana

Caribbean Advanced Proficiency Examination Integrated Mathematics

Topic : Fatal traffic accidents occurring along urban centers Name of Center: Saint Stanislaus College Center Code: 090047 Name of Candidate: Carlos John Gonsalves Registration Number: 0900470267 Territory: Guyana Year of exam: 2017 Teacher: Mrs. Greenich

ACKNOWLEDGEMENTS ………………………………………………………………..1 PROJECT TITLE …………………………………………………………………………..2 INTRODUCTION……………………………………………………………………………3 METHODOLOGY……...……………………………………………………………………5 ORGANIZATION OF DATA…….…………………………………………………………7 PRESENTATION OF FINDINGS……………………………………………………….....10 ANALYSIS OF DATA ………...……………………………………………………………..15 INTERRETATION OF FINDINGS……………………………………………..…………..19 RECCOMMENDATIONS…………………………………………………………………..21 CONCLUSIONS……………………………………………………………………………...22 BIBLIOGRAPHY…………………………………………………………………….……...23

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Acknowledgements The researcher would like to express heartfelt gratitude to the ones that rendered assistance during the completion of this Internal assessment, namely, God, his family and his Caribbean Studies teacher, miss Roxanne La Fleur

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Project Title “An examination of motor traffic accidents occurring in Urban centers and adjoining Roadways in Guyana”

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Guyana, being a developing country, is known to have a high volume of motor traffic, especially in the country’s capital Georgetown and other urban areas. Within the past few decades the volume and varieties of motor transport, both private and commercial, has skyrocketed. The urban centers of Guyana houses a large portion of the country’s population, and hence, congestion, driver error or recklessness, slack legislation and enforcement often leads to a great deal of accidents which cause damage to life, limb and property. The selection of this topic is rooted in several reasons, including the continued rise of fatal road traffics accidents, the continuous apparent disregard for road laws by some drivers and a personal interest in the topic since the researcher is, himself a daily driver or passenger in a motor vehicle on the roadways of Guyana. The researcher employed the use of quantitative data as the preferred data to be used in this project. “it emphasizes objective measurements and the statistical, mathematical, or numerical analysis if data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.”1 Quantitative data were used for secondary sources of data. The researcher aims to: 

To find the probability that a person traveling along the highways between settlements would be a fatality in a traffic accident.

1

Babbie, Earl R. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS. 2nd edition. London: SAGE Publications, 2010.

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

To determine the average number of road fatalities over the past nine years and the dominant causes of these accidents.



To determine what category of road user is in the most danger of becoming a fatality.

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For this project, the research employed was non experimental. This means that it “is the label given to a study when a researcher cannot control, manipulate or alter the predictor variable or subjects, but instead, relies on interpretation, observation or interactions to come to a conclusion. Typically, this means the non-experimental researcher must rely on correlations, surveys or case studies, and cannot demonstrate a true cause-and-effect relationship. Non-experimental research tends to have a high level of external validity, meaning it can be generalized to a larger population.”2 For the purpose of this project, the researcher employs statistical data retrieved from official books of the Guyana police force traffic division to prove if fatal traffic accidents have been on the rise.

In conducting the study utilizing preexisting statistical data, the researcher utilized fatal accidents occurring on roadways in or between urbanized areas, these included the towns (excluding Lethem) and capital city of Georgetown. This was used as vast majority of Guyanese only travel along these roads and these are the roads where majority of accidents occur. The ‘population’ being studied included all users of roadways in the study area. With reference to geographic urban population breakdown provided by the official census of Guyana 2012, the target population stands at 608,528 Guyanese or 76.1 percent of the total population of Guyana.

This method was

employed as it gave all persons equal consideration.

2

http://study.com/academy/lesson/non-experimental-and-experimental-research-differences-advantagesdisadvantages.html

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To accurately determine the major causes of fatal road traffic accidents, the researcher employed the use of statistical data as the data collection method. The statistical data played a vital role in this project since it effectively serves for accurately capturing the various elements of each fatal accident including age of fatalities, causes, category of road users that account for fatalities etc. With statistical data, the data to be easily categorized and tabulated. The data was collected at the Traffic police headquarters at evelery , Georgetown Guyana. This was done on the 28th of January, 2017. As a Data Coding technique, The researcher utilized the built in tools on Microsoft excel to present the raw data into a structured format in a spreadsheet so as it could be easily interpreted. The data was further tabulated and presented into frequency tables for preservation purposes, the database was saved and encrypted utilizing Kaspersky security software

During the completion of this project, several challenges were faced. Firstly, the collection of the official police statistics was limited to three years prior to the current date as previous could not be found at the time of the researcher’s visit. Secondly, the data cited prior to 2014 was extracted from news sources which was based on the official police records that were missing, but no form of corroboration existed for the researcher to verify these statistics first hand.

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Figure 1: Road fatalities for the period 2008 - 2016 YEAR

TALLY

2008

|||| |||| |||| |||| |||| |||| ||||

FREQUENCY 110

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| 2009

|||| |||| |||| |||| |||| |||| ||||

116

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| | 2010

|||| |||| |||| |||| |||| |||| ||||

115

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| 2011

|||| |||| |||| |||| |||| |||| ||||

103

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| ||| 2012

|||| |||| |||| |||| |||| |||| ||||

110

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| 2013

|||| |||| |||| |||| |||| |||| ||||

112

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| ||

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2014

146

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |

2015

126

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |

2016

128

|||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||

Figure 2: Road user fatalities for the period 2008 - 2016 Road user

2009

2010

2011 2012

2013

2014

2015

2016

Total per category

Driver

21

9

17

20

17

16

21

19

140

Passengers

19

21

19

15

22

23

32

27

178

Pedestrians

42

40

38

34

39

60

42

38

333

Motor

15

24

12

19

18

26

16

23

153

other

19

21

17

22

16

21

15

21

152

Total

116

115

103

110

112

146

126

128

956

Cyclists

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Figure 3: Age of driver involved in fatal accidents from 2014 - 2016 Age of drivers

2014

2015

2016

16-25 26-35 36-45 46-55 55 plus Total

41 49 35 23 32 180

30 49 27 15 14 135

34 54 28 24 21 161

Total per category 105 152 90 62 67 476

Figure 4: fatal areas for the years 2014 – 2016 Areas East Bank Demerara Georgetown East coast Demerara West bank demerara

Deaths in 2014 53

Deaths in 2015 34

Deaths in 2016 68

Total fatalities 155

16 7

18 13

10 1

44 21

7

18

11

36

West coast demerara Essequibo Berbice Total

16

9

11

36

14 29 146

9 25 126

12 15 128

35 69 400

Figure 5: Causes for fatal accidents over the past 3 years Causes of fatal accidents

Number of fatalities in 2014

Number of fatalities in 2015

Speeding DUI Inattentiveness Failure to comply to signs Breach of traffic light Error of judgement

87 10 33 5

Total

51 17 35 1

Number of fatalities in 2016 67 17 26 1

0

1

4

5

0

1

2

3

205 44 94 7

358 9

Figure 6: Bar graph depicting the fatality for the period 2008 - 2016 128 2016 126

2015

146

2014 112

2013 2012

110

2011

103

2010

115

2009

116

2008 110 0

20

40

60

80

100

120

140

160

Source: Newspaper articles Figure 6 illustrates the trends of traffic fatalities over a nine year period. We can see the figure fluctuating between the years 2008 – 2011 with, 110,116,115 and 103 fatalities respectively, it then starts to climb from 2012 – 2016 with 110, 112, 146, 126 and 128 fatalities respectively.

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Figure 7: Line graph showing fatalities for categories of road users for the years 2009- 2016 Drivers

Passengers

Pedestrians

Motor cyclists

Other

70

60 60

Number of fatalities

50

42

42

40

39

38

40

38

34

32

30 21 20

21

19

15 10

19

12

23

22

17

18 19

17 9

22

20

21

19

27

26

24

16

17

23 21

16

21

21

16

15 15

19

0 2009

2010

2011

2012

2013

2014

2015

2016

Year

Figure 3 shows the trends for the various category of road user fatalities, we can see that pedestrians remain consistently higher that other road user fatalities with a total of 333 total fatalities, The remaining four categories fluctuate over the years very similarly, with passengers being the second most Affected road user with 178 fatalities followed closely by motorcyclists and other road users ( bicycles, persons being towed on bicycles, persons on back of motor cycles) with 153 and 152 deaths each and lastly by drivers with 140 fatalities over the examined period.

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Figure 8: Ages of drivers involved in fatal accidents for the period 2014-2016 60 54 49 49

Number of Fatal accidents

50 41 40

35

34

32

30

27 28

30

2014 24

23

2015 21

20 15

14

46-55

55 plus

2016

10

0 16-25

26-35

36-45 Age range of drivers

Figure 8 depicts the breakdown of the ages of drivers involved in fatal accidents. The x axis denotes the age categories while the y axis denotes the number of drivers for each corresponding age category. The 26-35 range possess the highest figures with 49,49 and 54 for each respective year followed by the 16-25 category with 41,30 and 34 accidents respectively

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Figure 9: Stacked bar graph depicting fatalities according to geographic boundary

1

YEAR

2016

68

2015

34

2014

10

18

53

13

16

11

18

7

7

11

9

16

12

9

14

15

25

29

NUMBER OF DEATHS

East Bank Demerara

Georgetown

East Coast Demerara

West Bank Demerara

West coast Demerara

Essequibo

Berbice Figure 9 illustrates the number of fatalities for each corresponding geographic boundary for the years 2014-2016 according to official police records. For 2014 we can see the most dangerous areas in Descending order are the East Bank of Demerara (53), Berbice (29), Georgetown and the West Coast of Demerara ( 16 each) and Essequibo and the East coast and west bank of Demerara (7 each) .

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Figure 10: Causes of fatal accidents for the period 2014-2016

2%

2%

1%

26%

Speeding DUI Inattentiveness 57%

Failure to comply to Signs Breach of Traffic lights

12%

Error of Judgement

Figure 10 shows the Causes of fatal accidents over the period 2014-2016 as a percentage of the total number of accidents, which is 358. Speeding accounted for 205 fatalities in total with 87 in 2014, 51 in 2015 and 67 in 2016. DUI accounted for 44, with 10 in 2014, 17 in 2015 and 17 in 2016. Inattentiveness summed up to 94, with 33, 35 and 26 for the period 2014-2016 while failure to comply to signs resulted in 7 total fatalities, 5 in 2014 and 1 each in 2015 and 2016. Breach of traffic lights accounted for 5 fatalities, with none in 2014, 1 in 2015 and 4 in 2016 while 3 accidents resulted from error of judgement with 1 in 2015 and 2 in 2016.

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Measures of Central Tendency 

Mean

The researcher intends to calculate the average number of fatalities in traffic accidents over the past nine years using the formula:

= 110 + 116 + 115 + 103 + 110 + 112 + 146 + 126 + 128 9 = 118.4 This shows that an average of 118 persons died each year while using the roadways of the country for the period 2008-2016. Following the same formula the following average number of fatalities for the period 2009 – 2016 were established 

Drivers = 140/8 = 17.5 Fatalities



Passengers = 178/8 = 22.3 Fatalities



Pedestrians = 333/8 = 41.6 Fatalities



Motor Cyclists = 153/8 =19.1 Fatalities



Other road users = 152/8 =19 Fatalities

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Measures of Relative Position 

Probability-

The researcher intends to calculate the probability of a random road user (both general and specified categories) being a fatality in a road accident using the following formula: Number of specified outcomes Number of possible outcomes For all Calculations on measures of relative position the sample space is 608,528. Hence the probability of a road user being a road traffic fatality is 1’066 608,528 = 0.001751768 = 0.0018 =

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.

5000 

Probability of a driver becoming a fatality

= 140 608528 = 0.000230063 =0.00023 = 23 100000 

Probability of a passenger becoming a fatality

= 178 608528 = 0.000292509 = 0.00029 = 29 100000 16



Probability of a pedestrian becoming a fatality

= 333 608528 = 0.000547222 = 0.00055 = 11 20000 

Probability of a motor Cyclist becoming a Fatality 153 608528 = 0.000251426 = 0.00025 =1 4000



Probability of ‘other’ road users becoming a fatality 152 608528 = 0.000249783 = 0.00025 =1 4000

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Measures of Variability 

Variance and Standard Deviation

The researcher intends to calculate the Variance and Standard deviation of the Ages of drivers involved in fatal accidents. Variance = 105 + 152 + 90 + 62 + 67 5 = 476 5 = 95.2 =95

σ2= (105 – 95)2 + (152 – 95)2 + (90 – 95)2 + (62- 95)2 + (67 – 95)2 5 = 102 + 572 -52 - 332 – 282 5 = 100 + 3249 + (-25) + (-1089) + (-784) 5 =

1451 5

= 290.2 = 290 Hence, the Standard Deviation: σ = √𝟐𝟗𝟎 = 17.029 = 17

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After conducting the research, a great deal of information was provided for each of the objectives. The data was analyzed using three distinct statistical tools, namely measures of Central Tendency, Measures of relative position and measures of variability In relation to objective one, determining the probability of a person traversing the roadways between urban settlements becoming a fatality in a road accident, the measure of relative position was used. The researcher totaled the amount of fatalities and divided the figure by the target population, namely, the proportion of Guyana’s population that resides within and between the major towns and city (608,528) Collectively, it was discovered that a random road traveler, at any given time has a 0.0018 % chance of dying in a road accident, or a 9 in 5000 chance, which a breakdown of the probabilities for each category of road users indicate that the chance of dying in a road accident is very slim since it does not exceed 0.00055 or 11 in 20000. Objective two dealt with the average number of road fatalities per year and the dominant causes for these fatalities. This was calculated utilizing the measure of Central Tendency where the total fatality figure was found and then divided by the period of years the data was collected over. Which translated to 1066 fatalities divided by 9 years for an average of 118 fatalities per year. The mean for each category of road user was also specified. In relation to the causes of these accidents, data was provided for both the immediate cause and the human aspect for 3 years prior. The primary causes of the accidents were speeding, driving under the influence of alcohol and inattentiveness. Which accounted for 343 out of 358 accidents (96%). These causes are often interdependent. This evidence is supported by the corresponding age ranges of drivers involved in fatal accidents with the three youngest categories of drivers accounting for 73% of fatal accidents between 2014 -2016.

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The anomaly present in the data that seems to not the causes of fatal accidents with the age of the drivers would be that drivers above age 46 account for a total of 129 fatal accidents, however alcoholism and inattentiveness would also be higher in this age range. The sole primary cause can be equated to speeding which would be the major contributing factor towards accidents. Upon determining the causes, the third objective was addressed, which was the determining of the category of road user most susceptible to harm. In terms of the drivers involved, a measure of variability was employed to determine the variance and standard deviation of the amount of fatal accidents each category of driver is involved in . The average number of accidents was calculated at 95 per category, using the population formula for standard deviation, the standard deviation was calculated to be 17. Therefore the most susceptible category of drivers were ages 26-35 which had more than 3 standard deviations above the mean. In relation to which category of road user is most vulnerable, Pedestrians were identified as most vulnerable as they accounted for 35 % of total fatalities. This is so, since many pedestrians and drivers alike ignore safety and road use protocols.

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The completion of this research project solidified the view that several new measures needed to be put into place to help curb the high number of road traffic deaths in Guyana. 

Firstly, the researcher would recommend the implementation of automated security barriers that rise with red light, restricting vehicles from passing and lower with a green light, allowing them to past, hence making it incredibly difficult to breach traffic lights.

In addition, cars should be required to have a mandatory first aid kit, so as to aid in the event of a crash. 

Secondly, a database with all number plates and registered owners should be created and implemented alongside with high powered cameras at fixed points along high traffic and high danger roads capable of motion tracking to identify speeding vehicles. This can be implemented with a automated software that automatically fines or issues the vehicle owner a speeding ticket.



New legislation should be introduced that affixes stricter penalties to those behind the wheel in the event of an error or recklessness which includes but is not limited to treating speeding as attempted murder.

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In conclusion, one can safely assert, due to the facts derived from the data that the probability of a random road user dying in a road accident is very small (0.0018% chance) this small margin of risk is also reflected in the probabilities for different road users. It can also be vividly seen that the average number of fatal road accidents is 118 per year while the individual category with the highest average fatalities were pedestrians with 42 per year. These accidents and subsequent deaths were primarily caused by young drivers 16 – 35 who were primarily speeding, under the influence of alcohol or inattentive. Also, it can be seen that the user in the most danger of becoming a fatality, with regard to drivers, the most susceptible to harm were drivers aged 16 – 35, while pedestrians were the most heavily affected road users.

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

http://pages.stern.nyu.edu/~adamodar/New_Home_Page/littlebook/statisticsrelationships. htm (Retrieved 8th February, 2017)



http://study.com/academy/lesson/non-experimental-and-experimental-researchdifferences-advantages-disadvantages.html (Retrieved 9th February, 2017)



https://en.wikipedia.org/wiki/Closed-ended_question (Retrieved 9th February, 2017)

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