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
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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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