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1-consider the data obtained via either ArcGIS or SAS that you submitted as Excel Files and described as part of the Data/Methodology section. Submit a minimum of 20 observations (# them individually) about these data, providing commentary regarding them, e.g., are these results expected or unexpected, do particular aspects of the results stand out as odd, etc. (e.g. = for example…so, these are suggestions and not the limit of what you can comment on) Provide graphics (tables, charts, maps) that illustrate your commentary/observations.Speculate beyond the limits of your data as to, for example,whether younger or older driver results would be what you would expect for the general population.————————————————————————————————————————————————————-2- consider both the data obtained via either ArcGIS or SAS that you submitted as Excel Files and described as part of the Data/Methodology section and the commentary/analysis submitted as Excel Files and described as part of the Analysis/Results section of your paper. Submit a minimum of 5 summary or concluding thoughts regarding your efforts and provide some discussion as to the implications of the results – implications for traffic safety, for example.———————————————————————————————————————————————————–plz makes everyone different file
crashseverity_ror_2010_2014.xlsx

freq_crcomnnr_rur2laneror.xlsx

freq_csev_rur2laneror.xlsx

freq_csurfcond_rur2laneror_.xlsx

freq_day_rur2laneror.xlsx

sas.rar

20190405191937crashes_for_roadway_data_method_3__1_.doc

20190319210853crashes_for_roadway.doc

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Crash Severity
Fatal
Major Injury
Minor Injury
Possible/Unknown
Property Damage Only
Value
1
2
3
4
5
2010
33
110
253
287
678
1361
2011
36
92
228
225
591
1172
2012
31
103
193
211
459
997
2013
36
95
209
255
516
1111
2014
30
104
220
233
546
1133
sum
166
504
1103
1211
2790
5774
%age
2.87%
8.73%
19.10%
20.97%
48.32%
2010-2014 (5-year)
mean
mode
median
33
36
33
101
#N/A
103
221
#N/A
220
242
#N/A
233
558
#N/A
546
0th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
2010-2014 Quartiles (Exclusive)
1st
2nd
3rd
30.5
33
36
93.5
103
107
201
220
240.5
218
233
271
487.5
546
634.5
4th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
0th
30
92
193
211
459
2010-2014 Quartiles (Inclusive)
1st
2nd
3rd
31
33
36
95
103
104
209
220
228
225
233
255
516
546
591
Highest Number
Difference SAS & GIS
Iowa Rural, Primary, Two-Lane, Highway Run-off-road
Crashes 2010-2014 Crash Severity by year
800
Iowa Rural, Primary, Two- Lane, Highway Run-Off-Road Crashes
2010-2014 Crash Severity Percentages
2.87%
700
8.73%
600
500
400
19.10%
48.32%
300
200
100
0
Fatal
Major Injury
Minor Injury
2010
2011
2012
2013
Possible/Unknown
20.97%
Property Damage Only
2014
Fatal
Major Injury
Minor Injury
Possible/Unknown
Property Damage Only
4th
36
110
253
287
678
ELEMENTS
Non-collision
Head-on
Rear-end
Angle – oncoming left turn
Broadside
Sideswipe – same direction
Sideswipe – opposite direction
Unknown
Not Reported
Sum
CRCOMANNER
1
2
3
4
5
6
7
9
77
114
MANNER2010
1127
34
67
4
56
39
41
16
3
1387
MANNER2011
947
36
60
8
40
37
32
10
15
1185
MANNER2012
807
29
40
12
37
22
31
8
16
1002
MANNER2013
899
35
44
12
50
28
28
8
8
1112
MANNER2014
897
37
70
6
40
38
38
4
4
1134
Sum
4678
173
284
46
228
170
177
55
123
5934
%age
78.83%
2.92%
4.79%
0.78%
3.84%
2.86%
2.98%
0.93%
2.07%
2010-2014 (5-year)
Mean
Mode
Median
935
#N/A
899
34
#N/A
35
56
#N/A
60
8
12
8
45
40
40
33
#N/A
37
34
#N/A
32
9
8
8
9
#N/A
8
0th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
2010-2014 Quartiles (Exclusive)
1st
2nd
3rd
852
899
1037
31.5
35
36.5
42
60
68.5
5
8
12
38.5
40
53
25
37
38.5
29.5
32
39.5
6
8
13
3.5
8
15.5
4th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
250
200
150
100
50
0
Head-on
Rear-end
MANNER2010
Angle – oncoming
left turn
MANNER2011
Broadside
MANNER2012
Sideswipe – same
direction
MANNER2013
Sideswipe opposite
direction
MANNER2014
Unknown
2010-2014 Quartiles (Inclusive)
1st
2nd
3rd
897
899
947
34
35
36
44
60
67
6
8
12
40
40
50
28
37
38
31
32
38
8
8
10
4
8
15
Iowa Rural, Primary, Two-Lane, Highway Run-off-road Crashes 20102014 Manner of Crash/CollisionPercentages
Iowa Rural, Primary, Two-Lane, Highway Run-off-road Crashes
2010-2014 Crash Severity by year
Non-collision
0th
807
29
40
4
37
22
28
4
3
Not Reported
Non-collision
Head-on
Rear-end
Angle – oncoming left turn
Broadside
Sideswipe – same direction
Sideswipe – opposite direction
Unknown
Not Reported
4th
1127
37
70
12
56
39
41
16
16
Crash Severity
Fatal
Major Injury
Minor Injury
Possible/Unknown
Property Damage Only
Value
1
2
3
4
5
sum
2010
33
111
252
294
697
1387
2011
36
93
230
230
596
1185
2012
31
103
195
212
461
1002
2013
36
95
209
255
517
1112
2014
30
104
221
233
546
1134
sum
166
506
1107
1224
2817
5820
%age
2.85%
8.69%
19.02%
21.03%
48.40%
2010-2014 (5-year)
mean
mode
median
33
36
33
101
#N/A
103
221
#N/A
221
245
#N/A
233
563
#N/A
546
1164
#N/A
1134
0th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
2010-2014 Quartiles (Exclusive)
1st
2nd
3rd
30.5
33
36
94
103
107.5
202
221
241
221
233
274.5
489
546
646.5
4th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
0th
30
93
195
212
461
2010-2014 Quartiles (Inclusive)
1st
2nd
3rd
31
33
36
95
103
104
209
221
230
230
233
255
517
546
596
Highest Number
Difference SAS & GIS
Iowa Rural, Primary, Two-Lane, Highway Run-off-road
Crashes 2010-2014 Crash Severity by year
Iowa Rural, Primary, Two- Lane, Highway Run-Off-Road Crashes
2010-2014 Crash Severity Percentages
0.028749567
800
0.087287842
700
600
500
0.483200554
400
0.19102875
300
200
0.209733287
100
0
Fatal
Major Injury
Minor Injury
2010
2011
2012
2013
Possible/Unknown
2014
Property Damage Only
Fatal
Major Injury
Minor Injury
Possible/Unknown
Property Damage Only
4th
36
111
252
294
697
ELEMENTS
Dry
Wet
Ice
Snow
Slush
Sand/mud/dirt/oil/gravel
Water (standing/moving)
Other (explain in narrative)
Unknown
Not Reported
Sum
CSURFCOND
1
2
3
4
5
6
7
8
9
77
121
SRFCND2010
668
102
350
202
33
6
5
9
5
7
719
SRFCND2011
640
90
196
178
33
8
1
15
8
16
545
SRFCND2012
689
72
94
82
29
5
11
4
16
313
SRFCND2013
620
95
194
128
42
9
2
9
4
9
492
SRFCND2014
620
99
190
167
21
9
4
6
8
10
514
sum
3238
460
1027
761
163
43
19
58
38
135
2704
%age
1.197485
17.01%
37.98%
28.14%
6.03%
1.59%
0.70%
2.14%
1.41%
4.99%
2010-2014 (5-year)
Mean
Mode
Median
647
620
640
92
#N/A
95
205
#N/A
194
151
#N/A
167
32
33
33
7
9
8
3
#N/A
3
10
9
9
6
8
5
12
16
10
0th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
2010-2014 Quartiles (Exclusive)
1st
2nd
3rd
81
95
100.5
142
194
273
105
167
190
25
33
37.5
5.5
8
9
1.25
3
4.75
7.5
9
13
4
5
8
8
10
16
402.5
514
632
4th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
0th
72
94
82
21
5
1
6
4
7
313
2010-2014 Quartiles (Inclusive)
1st
2nd
3rd
90
95
99
190
194
196
128
167
178
29
33
33
6
8
9
1.75
3
4.25
9
9
11
4
5
8
9
10
16
492
514
545
Iowa Rural, Primary, Two-Lane, Highway Run-off-road Crashes 2010-2014
Manner of Crash/CollisionPercentages
Iowa Rural, Primary, Two-Lane, Highway Run-off-road Crashes 20102014 Crash Severity by year
0.70% 2.14% 1.41%
1.59%
800
700
4.99%
6.03%
600
500
28.14%
400
300
200
100
1.197485207
37.98%
0
17.01%
Series1
Series2
Series3
Series4
Series5
Series6
Dry
Wet
Ice
Snow
Slush
Sand/mud/dirt/oil/gravel
Water (standing/moving)
Other (explain in narrative)
Unknown
Not Reported
4th
102
350
202
42
9
5
15
8
16
719
ELEMENTS
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Value
1
2
3
4
5
6
7
sum
DAY2010
178
244
233
201
132
215
184
988
DAY2011 DAY2012 DAY2013 DAY2014
162
173
168
145
230
152
168
150
165
115
166
140
131
122
153
117
140
131
138
176
165
131
155
170
192
178
164
236
828
693
793
728
sum
826
944
819
724
717
836
954
4030
%age
20.50%
23.42%
20.32%
17.97%
17.79%
20.74%
23.67%
2010-2014 (5-year)
mean
mode
median
165
#N/A
168
189
#N/A
168
164
#N/A
165
145
#N/A
131
143
#N/A
138
191
#N/A
184
806
#N/A
793
Iowa Rural, Primary, Two-Lane, Highway Run-off-road Crashes
2010-2014 Crash Severity by year
0th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
2010-2014 Quartiles (Exclusive)
1st
2nd
3rd
153.5
168
175.5
151
168
237
127.5
165
199.5
119.5
131
177
131.5
138
158
143
165
192.5
171
184
214
4th
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
#NUM!
0th
145
150
115
117
131
131
164
2010-2014 Quartiles (Inclusive)
1st
2nd
3rd
162
168
173
152
168
230
140
165
166
122
131
153
132
138
140
155
165
170
178
184
192
Iowa Rural, Primary, Two- Lane, Highway Run-Off-Road Crashes
2010-2014 Crash Severity Percentages
300
250
20.50%
23.67%
200
23.42%
20.74%
150
17.79%
100
20.32%
17.97%
50
0
Sunday
Monday
Tuesday
Series1
Series2
Wednesday
Series3
Series4
Thursday
Series5
Series6
Friday
Saturday
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
4th
178
244
233
201
176
215
236
Running head: CRASHES FOR ROADWAY DATA/METHOD
Crashes for Roadway Data/Method
Student’s Name
Institution
Date
1
CRASHES FOR ROADWAY DATA/METHOD
2
Crashes for Roadway Data/Method
Introduction
Crashes for roadway require an insight. This calls for a better understanding of the
causes which can only be obtained through analysis and evaluation of the causal factors and
their impetus. Severity will be estimated through analysis of the impact that the crashes has
on the life of individuals using the transport medium. This paper will address crashes for
roadway majorly on Two-Lane Run of Road on the rural primary in Lowa state USA between
the year 2010 and 2014. High dependence on the means of transport predisposes the subjects
to the tragedy. National Transportation Safety Board has availed information indicating that
there is a rise in the number of accidents occurring in the United States. Mostly motor vehicle
crashes (Sivak & Schoettle, 2010). Various causal factors are leading to phenomena such as
substance abuse, use of devices such as phones mostly texting and calling negligence in
obeying traffic rules. Two platforms will be used to collect data regarding road crashes in the
mentioned areas that are GIS and SAS.
GIS &SAS
GIS is a structure meant to gather manages and analyze data. It is based on
geography. Spatial location is examined and organized into useful information with some
relationship, pattern and situations that can be used in making a sound decision. The system
was used to collect the information on the crashes and inference was built on the relation
between the different variables (Arendt & Stephens, 2018).
SAS was used for improved analytics and data management. Its capability on
multivariate analysis aided in the evaluation of the different variables prevalent in the case
study such as manner, year and severity. The software is used in mining, altering managing
and retrieving user data from various sources after which a statistical analysis is performed
(Valiant & Valiant, 2017)
Data overview
Data was presented in tabular form, bar graphs and pie charts. The utilization of the two
methods of collecting, evaluating and presenting data made exploration, querying and
summarizing of the information possible. The independent variables under scrutiny are crash
severity and manner in which the accidents occurred. Under seriousness, there were various
sub-categories such as fatal, major injury, minor injury possible/unknown and property
damage only. The dependent variable are the years that ranges from 2010 to 2014. Table 2
will be made up of. Under the manner variable, they were various sub-categories including
non-collision, head-on. Rear-end. Angle-oncoming Left turn, broadside. Sideswipe (same
direction and opposite direction, unknown and not reported cases. The mean, mode and
median of the variables will be obtained.
To get a better understanding, various causalities were investigated such as the defects
prevalent in the motor vehicle, climatic condition, and vacations and light. With these
concepts it was possible to identify the relationship between the causalities and the
independent variables that are the crashes. An association between time and the accidents was
also made possible. The time day of the accidents was recorded. The amount of light
prevalent of the particular time day aided in identifying the contributing factors.
Regarding crash severity in Lowa state, various categories were prevalent. These
were; fatal accidents which in the year had a percentage of 2.85% cumulatively for the five
CRASHES FOR ROADWAY DATA/METHOD
3
years. Major injuries had a record of 8.69%, and minor injuries had 19.02% possibly
unknown cases had 21.03%, and property damage only had a cumulative percentage of
48.40%. In total 5820 cases were recorded for the five years. The year 2010 had the highest
number of cases 1387 cases were reported followed by 1185, 1002, 112 and 1134 in the
consecutive years respectively. From this figure, it was possible to deduce there were more
cases in the year 2010 as the prevalence declined in the subsequent years. Another deduction
is that property damage was the main contributor of road crashes in the time length.
About the elements or how the accident occurred, on-collision recorded the highest
number of cases followed by rear end. The year 2010 had the highest number of crashes as
there was a total of 1127 non-collision cases and 67 rear end cases. This instance is the best
for comparison as they are contrasting. A total of 5934 cases were recorded in the period
whereby the year 2010 was the main contributor of the incidences. In that year sideswipe rear
end and non-collision had 41,67,1127 instances respectively. With the following record, a
relationship between the phenomena and the causalities was possible.
Among the contributing factors of the accidents are the manner of the crash and the
predisposing conditions such as snowed. The dry state had an impact on the subject as it was
the main contributor recording 3238 cases followed by ice with 37.98% and snow recording
28.14%. The year 2010 had the highest number of cases with 719 cases being recorded.
The condition discussed in the preceding discussion has a climatic backup stipulated
by seasons. To deduce this information record of cases in each month were evaluated
whereby January and February had the highest number of cases, i.e. 25.21 and 30.23
respectively. September had the lowest number of cases as the percentage was 12.39%. The
year 2010 had the highest number of cases as compared to other years standing at 731 issues.
Light factor affected the phenomena. Accidents that occurred during in broad daylight
recorded the highest percentage of 58.51% as roadways that were not lit had a percentage of
32.17%.the two aspects were the main contributors with 2010 having the highest rate.
Crashes may occur due to defects such as brakes, steering blowout etc. During this
period cases that had no defect scored the highest percentage of 97.70%. This is an indicator
that motor defects did not cause most accidents. Just like in other cases 2010 had the highest
number of such matters as there were 1513 reported instances. Vactions were contributing
factors vertical movement had the highest score of 89.42% with the year 2010 having the
highest number of cases i.e. 1579.Days of the week added to crashes in Lowa state too.
Saturday had the highest number of cases recording a total of 954 cases. Monday seconded
by 944 issues. None clear cases and the surface condition of the roads had an impact as they
had a score of 69.15% and 29.82% respectively. To get a better understanding of the
association time day factor was included emphasis being on Saturday and Monday as they
had the highest number of cases. It was observed that on Saturday’s accidents happen
between 3.59am to 7.59pm. On Mondays, more crashes occurred between 7.59 am to 9.59 am
and between 3.59pm to 5.59 pm.
Conclusion
In conclusion, the data availed could help in further analysis as some relationship
have been identified among the variables prevalent. For example, there is some link in the
high number of crashes on Monday during the morning and evening hours and the highest
number of accidents during the weekend mostly Saturday. The year 2010 has also recorded
the highest number of cases that later declined in the subsequent years, Vactions and other
CRASHES FOR ROADWAY DATA/METHOD
4
causalities such as defects indicate that the blame could be on the human factor. This calls for
further analysis.
CRASHES FOR ROADWAY DATA/METHOD
5
References
Arendt, A. M., & Stephens, M. (2018). Public Library Use of Geographic Information
Systems in the United States. Journal of Library Administration, 79-805.
Sivak, M., & Schoettle. (2010). Toward understanding the recent large reductions in US road
fatalities. 561-566.
Valiant, G., & Valiant, P. (2017). Estimating the Unseen: Improved Estimators for Entropy
and Other Properties. Journal of the ACM, 1-41.
Running head: CRASHES FOR ROADWAY
Crashes for Roadway
Student’s Name
Institution
Date
1
CRASHES FOR ROADWAY
2
Crashes for Roadway
Different modes of transport are prevalent in the United States. Safety is a concern in
the contemporary world as there are very many crashes emerging from different modes of
transport. Road transport is the most commonly used means of transport due to its availability
and convenience for short distances. Roadway has the highest number of accidents per year.
Due to this, institutions focusing on safety have been put in place to monitor and govern the
operations on roads. National Highway Traffic Safety Administration is among the agencies
whose mission is to protect the lives of individuals, inhibit injuries and reduction of crashes
that are related to automobiles. To achieve these goals, the agency has been mandated by the
federal government to come up and enforce automobile standards as per the set rules and
standards of the federal government. FARS system was established to suggest the way out of
the menace. Despite all these efforts from the federal government, the rate at which the
accidents occur is alarming. This paper will focus on crashes for roadway specifically for
Lowa Rural primary, Two-Lane Departures between 2010 and 2014(Sivak & Schoettle et al.
2010).
Road crashes encompass variety of accidents which occur. Various means prone to
accidents are used to navigate on roads. The high number of dependents of this mode of
transport makes it predisposed to to crashes. They include cyclists, motorbike riders, and
pedestrians. These categories of users create a large pool of accident likely individuals. NSC
have aided in reporting these cases.
National Transportation Safety Board is charged with the role of investigating and
reporting highway crashes and other types of fatalities in the United States. Information
concerning any accident will only be obtained from the board. It is a Federal organ. Recent
data indicate that fatality rates in the United States are higher compared to those of other
developed countries. Concerning the areas under study, the number of fatal accidents is
higher than those of other crashes in the region. This is marked by 48.3% standing in for
deadly accidents, 20.97% representing incapacitating crashes, 19.10% representing nonincapacitating. Property damages and other unknown crashes account for about 10%.
The National Safety Service has indicated that there was an increase in the number of
deaths caused by motor vehicle crashes in the United States. Among the contributing factors
includes an increase in population which has eventually led to the number of miles driven.
These phenomena have led to reduced driving and an increase in the number of destructors
on the roads (Sivak & Schoettle et al. 2010).
These incidences have been attributed by various factors whereby traffic intersection
is ranked as the most common cause of conflict. The crossing point nature brings about
driving destructors. Data from NHTSA in collaboration with Fatality Analysis Reporting
System have indicated that most fatal crashes happen in these areas. Two-way Departures
tend to cause conflict among drivers thus leading to the collision of automobiles (Sivak &
Schoettle et al. 2010).
Distracted drivers are also accounted for causing more crashes on the roads. The
many users of the means of transport pose challenges to drivers as they try to maneuver from
one area to another. Bicyclists and motorcyclist use the same driving paths with vehicles.
They are at times seen as distractors due to the unpredictability in patterns. Most of the users
are unlicensed and tend to break traffic rules. Non-observance of these rules will
automatically lead to crashes. Pedestrians have also been classified as distractors. They also
tend to ignore the traffic rules thus conflicting with automobiles (Sivak & Schoettle et al.
2010).
CRASHES FOR ROADWAY
3
Negligence of some drivers has led to severe crashes. Over speeding and ignoring
traffic rules such as the traffic lights meant to control traffic and making turns in areas that
they are not supposed to have led to conflicts. Generally, human factors have been attributed
as the most contributing factor of fatalities on roads. Drunkenness impairs reasoning. It is
among the human factors too as according to FARS there are various accidents that
investigation have proved that they were as a result of substance abuse (Berning et al. 2015).
Other factors that are leading to the glitches include environmental factors which have
played a minor role. Environmental factors include unfavorable weather conditions such as
storms and rain which interfere with actual dr …
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