University of South Florida College Environment for Young Adults Report i need help to write an introduction and a body for collected data that i did on my

University of South Florida College Environment for Young Adults Report i need help to write an introduction and a body for collected data that i did on my own, plus 2 other sources. all the information you need to know about the report are included in the attachment. please watch out the red font and clearly follow the instructions. this assignments has no word count limit. please make sure that you follow the right format (this is an example of how the report need to be like look at the attachment ‘example’) Recommendation Report
The format of this report will largely be determined by the nature of the problem you’re
addressing and the industries in which such a recommendation would be written. Part of
this project, then, is determining the appropriate final deliverable through a combination
of your own research and discussions with your instructor about what will best meet the
goals of the course. Overall, your recommendation must:


visualize the data you’ve collected. Your report must employ tables and graphs to
present the quantitative information your experiment generated. You’ll select the most
rhetorically appropriate graphic types and effectively design tables and/or graphs that
are most suited to conveying your data quickly and accurately despite its volume and
complexity.
interpret the data you collected. In other words, your recommendation must make an
argument about the data rather than present a tangle of numbers for readers to puzzle
over on their own. You’ll draw conclusions, explain the processes that lead to those
conclusions, acknowledge aberrant findings, and adapt your report to your managerial
audience (your ENC 3246 instructor) and your technical audience (your engineering
instructors/classmates/colleagues).
Data for the introduction and the body.
Two sources and a survey:
To get an idea of the two sources here is there Annotated Bibliography
Staglin, Garen. “Addressing Mental Health Challenges on College Campuses.” Forbes,
Forbes Magazine, 4 Oct. 2019,
https://www.forbes.com/sites/onemind/2019/10/04/addressing-mental-health-challenges-oncollege-campuses/#6e9f2fdb400d.
The Forbes article provides a great perspective from a reputable source into the current dire
mental health state of college students and what colleges around the United States are doing to
improve that mental health state. They use research done by prestigious colleges to show that
suicide rates of young people are rising. They also talk about the statistics that even though
college admittance is rising, long term mental health treatments provided for those students are
decreasing. They also shed some light on positive efforts that are being taken by colleges, such
as UCLA and their new STAND program, aimed at treating students with anxiety and
depression. Other universities, such as Rutgers, target treating specific mental health issues,
such as substance abuse. The article ends with a call to action, encouraging everyone to find
ways to improve this mental health crisis.
N.a. “Mental Health on College Campuses.” MentalHelp.net, Mental Help, n.d.
https://www.mentalhelp.net/aware/mental-health-on-campus/
The Mental Health article highlights the significance of stress from college and their impacts
on the mental health of students. It was noted that as of 2015, 58 percent of college students
reported overwhelming feelings of anxiety and depression during the course of the school year.
This stress was derived from not only the pressures of exams and workloads but from tuition
fees and future job prospects as well. These heightened feelings of stress and other mental
health issues in turn has led to sleep deprivation issues among students, which can significantly
impact their academic performances, along with increasing thoughts of dropping out and
suicide. With these statistics and effects in mind, the article stresses the importance of the
availability of mental health resources and support on college campuses to relieve the burdens
of students having to learn to manage their academic and personal lives and face the
debilitating-ness of anxiety and depression alone.
Note: we just need the introduction and the body for the report. Please make use of the
given sources.
1
2
Table of Contents
Table of Contents
2
Executive Summary
3
Introduction
4
Methods
5
Results
6
Part A: Route Waiting Times
6
Average Wait Time
9
Part B: Route Riding Times
10
Average Ride Time
13
Discussion
14
Conclusion
15
3
Executive Summary
Students and faculty across campus often depend on the University of South Florida’s
(USF) bus system to reach their desired destinations. In order to better assist students, USF
introduced the USF BullTracker application, a phone application that allows students to see how
long it will take a bus to reach a selected bus stop. Additionally, the rider may use the app to
calculate how long it will take him or her to reach their destination.
Currently, the USF bus system and the app that goes alongside it are both facing an
inconvenient challenge. Bus riders are complaining about delayed bus rides and the inaccuracy
of the app. Poor accuracy often causes students and faculty to arrive late to classes or
meetings. These delays can prove detrimental to the overall learning/teaching process. In this
study, data was gathered to evaluate the accuracy of the USF bus system and its
accompanying BullTracker application. As it turns out the bus system at USF is actually quite
accurate generally speaking, but it fails at case by case reliability. Thus, we recommend user
4
Introduction
On large university campuses across the country, there are transportation systems
created and integrated by the institutions in order to provide transportation to the students,
faculty, and other members of the community. At the University of South Florida (USF), we have
a multitude of bus routes that travel across and off-campus.
For this examination, the efficiency of the USF Bull Runner system was evaluated using
the USF mobile application. The USF application for mobile devices has a GPS feature that
tracks all of the buses that drive routes on campus. This GPS feature can often have
inaccuracies when it comes to showing the estimated arrival for a bus, which can affect how
members of the community must plan their day or adjust accordingly. In order to test the
efficiency, data was collected on every bus route for time discrepancies between what the USF
BullTracker reported as an estimated arrival time at a specific location and the actual arrival time
for that bus. The app-estimated ride times between stops was compared to the actual times it
took to ride to the destination. All of the data collected has allowed us to determine whether
routes needed to be changed, whether there should be more buses operating each day, or if
there are any other possible solutions that could fix issues with the efficiency of campus
transportation.
5
Methods
The process of this examination was for the group of surveyors to ride the bus routes
within a span of a couple of weeks and record data along the six bus routes. Each bus route
was assigned to a different member. The examination was split into two parts which were all
designed to test the efficiency of the BullTracker application and the Bull Runner.
Methods – Part 1
For part one of the analysis, the data were collected to compare the estimated arrival
time provided by the BullTracker app to the actual times it took to arrive. The following are the
steps we followed to collect the data.
1.
2.
3.
4.
Open the USF mobile application and determine a desired bus stop.
Head to the desired bus stop determined in step 1.
Upon arrival, open up the USF mobile application.
Once the application is open, click on the BullTracker option and select a route (the
application will now display the time it will take for a bus to get to your destination).
5. Record the estimated arrival time for the bus to get to your stop.
6. Right afterward enable your Stopwatch.
7. Wait until the bus arrives.
8. Once a bus has arrived, stop the stopwatch.
9. Record the value acquired through the stopwatch on the notepad.
10. Compare the results acquired.
Methods – Part 2
For part two, the application was used compare what the application estimated the
arrival at the desired stop with the time it actually took to ride. The steps we took to collect that
data are listed below.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Determine a bus route to ride and where you will board that bus.
Head to the desired bus stop and wait for the bus.
Once inside the bus, immediately after sitting down, open the USF mobile application.
Then click on the following tabs: BullTracker > Choose a route > Choose a stop for
arrival
Observe the time that the app estimates will take to drive that route to the immediate
next stop as soon as you get on the bus.
Record that estimated value on a notepad.
Start the stopwatch when bus begins to move.
Stop the stopwatch when the bus comes to a stop at the desired end location.
Record the difference between the estimated time the app provided before the route
began, and the time recorded on the stopwatch.
6
Results
Part A: Route Waiting Times
In part one of the experiment, we collected data to compare the estimated arrival time
provided by the BullTracker application to the actual times it took to arrive for every bus route
and every trip taken. The data that was collected is displayed below along with the conclusions
reached for each graph.
​GRAPH A.1
Shown in Graph A.1, Route A is usually delayed and takes longer to arrive then the
estimated times given by the dispatch.
7
​ RAPH A.2
G
Shown in Graph A.2, Route B, in most cases, is faster than the estimated arrival time.
​ RAPH A.3
G
Shown in Graph A.3, Route C is on par with the estimated times, only varying slightly
when the bus is faster than reported to be.
8
GRAPH A.4
Shown in Graph A.4, Route D is always faster than the estimated arrival times on the
application.
GRAPH A.5
Shown in Graph A.5, Route E has the tendency to be on time or running late compared to the
estimate
9
​GRAPH A.6
Shown in Graph A.6, Route F seemed to be accurate as it usually arrived on time.
Average Wait Time
Table A.1 displays the average wait time difference for each bus route which was
calculated using the average differences on whether or not the bus arrives before or after the
predicted time to your destination.
Route
Average Wait Time Difference
Route A
1:17 LATE
Route B
1:43 EARLY
Route C
1.2 SECONDS EARLY
Route D
24 SECONDS EARLY
Route E
1:23 LATE
Route F
18 SECONDS LATE
Table A.1
10
Part B: Route Riding Times
In part two of the experiment, we collected data to compare the application estimated
arrival time at the desired stop and compared it to the time it actually took to ride the bus to that
location. The data that was collected is displayed below along with the conclusions reached for
each graph.
GRAPH B.1
Shown in Graph B.1, Route A is as fast as or faster than the estimated time on the application.
11
​GRAPH B.2
Shown in Graph B.2, Route B, in most cases, is faster than the estimated arrival time.
GRAPH B.3
Shown in Graph B.3, Route C is on par with the estimated times, only varying slightly
when the bus is faster than reported to be.
12
GRAPH B.4
Shown in Graph B.4, Route D is faster or on time compared to the estimated times.
GRAPH B.5
Shown in Graph B.5, Route E is slower than the predicted time in order to get from the
start point to the destination.
13
GRAPH B.6
Shown in Graph B.6, Route F is slower than the predicted time in order to get from the start
point to the destination.
Average Ride Time
Table B.1 displays the average ride time difference for each bus route which was
calculated using the average differences on whether or not the bus arrives before or after the
predicted time to your destination.
Route
Average Ride Time Difference
Route A
9.6 SECONDS FASTER
Route B
47 SECONDS SLOWER
Route C
1:22 SLOWER
Route D
54.6 SECONDS FASTER
Route E
2:10 SLOWER
Route F
2:37 SLOWER
Table B.1
14
Discussion
Overall, after looking at the data and identifying the average deviation from the waiting
time and ride time showcased on the app we can determine several things.
On average, the USF bus system is actually quite accurate. As we can observe in table
A.2, the max bus arrival time discrepancy can be found in route B, where on average, the bus
arrives 1:43 minutes earlier than expected. Additionally, the max riding time discrepancy that
can be observed, once again on average, is that of route F on figure B.6, where the buses take
2:37 minutes longer than expected to reach their destinations.
It is important to make the distinction of why there has been such a large emphasis on
making sure everything above is in terms of “on average”. Throughout our study we observed
that when looking at a case-by-case basis, the USF buses would actually be accurate in most
scenarios, but not in all of them. This leads to an uncertainty factor. For example, if we take note
of table A.4 column 4, we see how the actual waiting time is closer to over 10 minutes, but the
app stated that the bus would take over 30 minutes to reach the destination. This scenario
repeats itself over the course of other tables.
There was a trend that we did pick up throughout the course of our study. Routes that go
off-campus tend to have the biggest time discrepancies in both arrival and riding time. Again, we
can observe this by looking at the results for routes C, D, and F on the average wait time and
average ride timetables.
15
Conclusion
Thus, we conclude that the USF BullTracker application system is quite accurate when
there is no real desire for absolute reliability, and the user can afford to be flexible with a few
minutes. However, we do note that if time is of the essence and every minute matters to the
user, then the BullTracker system will prove inaccurate in certain instances and cost the student
and/or faculty time since they might miss a bus.
We recommend that USF bus riders arrive at the bus stop where they will on-board the
bus at least 10 minutes before the estimated arrival time provided by the BullTracker app. This
will nearly eliminate the chance that a student/faculty member misses a bus and ensure that you
reach your desired destination in a timely manner. We concluded that there is not enough
evidence from the data we gathered to recommend that the department of transportation should
consider adding more buses or modifying bus routes.

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