an analysis of unt commuting patterns/67531/metadc28489/m2/1/high_res_d/thesis.pdf · emissions....
TRANSCRIPT
APPROVED:
Donald Lyons, Major Professor Murray Rice, Minor Professor Sean Tierney, Committee Member Paul Hudak, Chair of the Department of
Geography Michael Monticino, Dean of the Robert B.
Toulouse School of Graduate Studies
AN ANALYSIS OF UNT COMMUTING PATTERNS
Susan L. Waskey, B. S.
Thesis Prepared for the Degree of
MASTER OF SCIENCE
UNIVERSITY OF NORTH TEXAS
May 2010
Waskey, Susan L. An Analysis of UNT Commuting Patterns. Master of Science (Applied
Geography), May 2010, 109 pp., 15 tables, 24 illustrations, references, 45 titles.
Academic institutions have recently organized to address their campus’ greenhouse gas
emissions. Along those lines, the University of North Texas (UNT) pledged to minimize the
campus’ environmental impact, and conducted a transportation survey in May 2009. The
analyses confirm that commuting to campus was the second highest source (29%) of UNT’s
greenhouse gas emissions, following purchased electricity (48%). Students, faculty and staff
drive over 89 million miles per year, 84% of which comes from students. Forty‐two percent of
student driving trips originate in the primary and secondary core areas surrounding Denton,
which are partially served by buses. However, because these core areas are in close proximity
to the campus, they contribute only 8% of the total student driving distance. Beyond the
Denton core, the inner periphery of Denton County contributes another 22% of driving mileage.
Students living in the outer periphery (outside Denton County) contribute the remaining 70% of
total driving distance, and carpooling is currently their only alternative.
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Copyright 2010
by
Susan L. Waskey
iii
TABLE OF CONTENTS
Page
LIST OF TABLES ............................................................................................................................ v
LIST OF FIGURES ......................................................................................................................... vi
CHAPTER 1 INTRODUCTION ........................................................................................................1
CHAPTER 2 BACKGROUND AND LITERATURE REVIEW .................................................................4
2.1 General Background .......................................................................................................4
2.2 UNT Specific Background ..............................................................................................15
CHAPTER 3 RESEARCH QUESTIONS AND METHODOLOGY .........................................................19
3.1 Research Questions .....................................................................................................19
3.2 Methodology ................................................................................................................19
CHAPTER 4 FINDINGS ................................................................................................................36
4.1 Survey Response and Descriptive Data .........................................................................36
4.2 Vehicle Mix ...................................................................................................................41
4.3 Transportation Modal Split ...........................................................................................42
4.4 Driving Distance and Bus Travel Distance .....................................................................44
4.5 Commuting Geography Analysis ...................................................................................45
4.6 Demographic and Socioeconomic Analysis ...................................................................74
CHAPTER 5 CONCLUSION ..........................................................................................................79
5.1 Policy Discussion ..........................................................................................................79
5.2 Further Research Recommendations ............................................................................85
5.3 Summary ......................................................................................................................87
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APPENDIX A SURVEYS ...............................................................................................................89
APPENDIX B STUDENT DATA .....................................................................................................94
APPENDIX C FACULTY/STAFF DATA ...........................................................................................98
APPENDIX D SUPPLEMENTARY MAPS ..................................................................................... 102
REFERENCES ............................................................................................................................ 106
v
LIST OF TABLES
Page
1. Student Chi-Square Analysis .......................................................................................... 22
2. Student Class Standing .................................................................................................. 23
3. Student Age Groups ....................................................................................................... 24
4. Faculty/Staff Chi-Square Analysis ................................................................................... 25
5. Faculty/Staff Gender and Job Classification ................................................................... 26
6. Faculty/Staff Age Groups ............................................................................................... 26
7. Transportation Survey Response ................................................................................... 36
8. Vehicle Type .................................................................................................................. 41
9. Driving Distance and Trips/Week Summary ................................................................... 44
10. Bus Travel Distance and Trips/Week Summary .............................................................. 44
11. Student Driving Distance by Area................................................................................... 56
12. Apartment Dwellers in High Mileage Zip Codes ............................................................. 60
13. Commuting Emissions.................................................................................................... 66
14. Chi-Square Analysis of Student Factors .......................................................................... 75
15. Chi-Square Analysis of Faculty/Staff Factors .................................................................. 77
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LIST OF FIGURES
Page
1. Recent NASA CO2 Data ................................................................................................... 6
2. Student Gender and Age Group .................................................................................... 37
3. Student Class Standing .................................................................................................. 37
4. Student Employment Status .......................................................................................... 38
5. Student Parking Permits ................................................................................................ 39
6. Faculty/Staff Gender and Age Group ............................................................................. 39
7. Faculty/Staff Household Income .................................................................................... 40
8. Faculty/Staff Housing .................................................................................................... 40
9. Student Transportation Modal Split ............................................................................... 43
10. Faculty/Staff Transportation Modal Split ....................................................................... 43
11. North Texas Map ........................................................................................................... 45
12. Denton County Zip Code Areas ...................................................................................... 47
13. Student Home Zip Code Locations ................................................................................. 49
14. Student Weighted Driving Trips ..................................................................................... 51
15. Student Weighted Driving Trips—Denton County .......................................................... 52
16. Student Driving Trips per Person ................................................................................... 53
17. Student Driving Distance ............................................................................................... 55
18. Student Driving Distance—Denton County .................................................................... 57
19. Student Driving Trips Hot Spot Analysis ......................................................................... 59
20. Faculty/Staff Home Zip Code Locations .......................................................................... 64
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21. Faculty/Staff Weighted Driving Trips ............................................................................. 65
22. Faculty/Staff Driving Trips per Person ............................................................................ 67
23. Faculty/Staff Driving Distance ........................................................................................ 69
24. Faculty/Staff Driving Trips Hot Spot Analysis .................................................................. 71
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CHAPTER 1
INTRODUCTION
The reality of climate change has become widely accepted across the developed world.
It has also been demonstrated in scientific studies that human activities are generating
greenhouse gases at an every‐increasing rate since the beginning of the Industrial Age, thus
contributing to the warming of the planet, and resulting in a changing climate (UNIPCC 2007).
In the United States, scientists and academic institutions have been involved in gathering data
supporting observations of a warming planet for years, and the academic institutions
themselves have recently organized to address their own campus’ “carbon footprint” (ACUPCC
2009). Along those lines, in May 2008, the University of North Texas’ President Gretchen
Bataille made a pledge to minimize the UNT campus’ environmental impact by signing the
American College and University Presidents Climate Commitment (ACUPCC). The first action
required of this climate commitment was to conduct a greenhouse gas (GHG) emissions
inventory. That inventory was completed during the spring 2009 semester, resulting in
emissions data that was submitted to the ACUPCC in May 2009.
Because a significant part of the total greenhouse gases the UNT community generated
was thought to come from commuting to the Denton campus, a transportation survey was
conducted in early 2009 to estimate those emissions. Although the data were quickly analyzed
to support the ACUPCC emissions inventory‐reporting deadline of May 15, 2009, the data
generated by the survey needed to be more thoroughly examined because many unanswered
questions remain. The transportation survey generated two very large datasets: one for
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students, and another for faculty and staff. This research studied those datasets to better
establish an understanding of UNT’s commuting patterns.
Specifically, the research questions characterizing the geography of the transportation
patterns determined what locations in north Texas (defined by zip codes) students and
faculty/staff travelled from to get to campus, and how many driving trips/week were made
from each zip code. This analysis of location and distance was partially completed in May, but
needed further study. Questions remained such as is there spatial clustering of driving trips
from some locations? Did students or faculty and staff that live in areas with transit (buses)
utilize that transit mode? Or, were there clusters of individuals that drove alone from a transit
served area? Additionally, were there clusters of persons that drove from a specific Dallas Fort
Worth (DFW) location that was not served by transit? Another line of inquiry was to ask if
specific socioeconomic or demographic factors (age, gender, income, housing, etc.) influenced a
student or faculty/staff member’s decision to drive alone? For example, if an employee had
school‐aged children, did that lead them to drive alone? This may have occurred because the
employee must drop off and pick up a child as part of his/her commute to campus.
And finally, as a result of a better understanding of the commuting patterns, this thesis
recommends future actions to the UNT community to reduce emissions. For example, are
there transportation system improvements that can target clusters of students, faculty or staff
to reduce their transportation‐related impact on the environment? With the benefit of
background research done on corrective actions taken to minimize GHG emissions at other
colleges and universities, this thesis focused on several high‐emission commuting “targets” and
suggests policies that UNT could consider to reduce those emissions. Some of the more
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popular strategies used at other universities have included offering free bus or transit passes to
both students and faculty/staff, promoting bicycling over driving by clearly marking bike routes,
implementing park and ride vanpools, and shifting to a 4‐day instructional and workweek.
In summary, the thesis’ analyses confirmed that commuting to campus was the second
highest source (29%) of UNT’s greenhouse gas emissions, following purchased electricity (48%).
Students, faculty and staff drive over 89 million miles per year, 84% of which comes from
students. Forty‐two percent of student driving trips originate in the immediate “core” areas
surrounding the Denton campus, which are partially served by buses. However, because these
areas are in close proximity to the campus, they contribute only 8% of the total student driving
distance. Beyond the Denton core, the peripheral areas of Denton County contribute another
22% of driving mileage. Students living outside Denton County contribute the remaining 70% of
total driving distance, and carpooling is currently their only alternative for emission reduction.
This more complete analysis of the transportation survey data can now serve as a basis for
determining actions that may be pursued in the UNT Climate Action Plan.
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CHAPTER 2
BACKGROUND AND LITERATURE REVIEW
2.1 General Background
Climate change has become a widely accepted fact of our modern world. Although
numerous scientists such as the National Aeronautics and Space Administration’s (NASA) James
Hansen have been sounding the alarm for years (Hansen et al. 1981), popular awareness and
acceptance of global warming in the United States has been slow to develop. A recent
taskforce report from the American Psychological Association stated there is currently little
concern in the U.S. regarding the probable consequences of climate change, in spite of the
likely extreme weather effects that will result (Swim 2009). Nevertheless, the theory of climate
change, as a scientific topic of study and debate, has many supporters. The most broad‐based
organization supportive of the theory is the United Nations’ Intergovernmental Panel on
Climate Change (IPCC). The IPCC is an internationally recognized authority on the subject,
comprised of a group of more than 1,300 independent scientists from countries all over the
world. Their most recent report, the fourth in a series of reports on climate change, was issued
in 2008. The summary report, Climate Change 2007: Synthesis Report, states, “Warming of the
climate system is unequivocal, as is now evident from observations of increases in global
average air and ocean temperatures, widespread melting of snow and ice and rising global
average sea level” (UNIPCC 2007, 30). This warming trend is already having visible effects on
our planet’s natural systems. A World Resources Institute issue brief titled, Climate Science
2008 Major New Discoveries, states, “Changes in 28,800 plant and animal systems and 829
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physical climate systems have led scientists to conclude that human‐induced warming is already
having a significant impact on natural and physical systems” (Levin and Tirpak 2009, 16; see also
Rosenzweig et al. 2008). And finally, the vast extent of data supporting climate change is best
summarized by the IPCC in their statement from the 2007 report, “Of the more than 29,000
observational data series, from 75 studies, that show significant change in many physical and
biological systems, more than 89% are consistent with the direction of change expected as a
response to warming” (UNIPCC 2007, 33).
Earth’s climate has undergone many cycles of warming and cooling over hundreds of
thousands of years, mostly due to subtle changes in the planet’s orbit and the resulting
differences in solar radiation (NASA 2009). The level of greenhouse gases in the atmosphere
can also cause warming of the climate. Scientists John Tyndall and Svante Arrhenius recognized
the heat‐trapping nature of these gases, especially carbon dioxide, in the 1800s (NASA 2009).
NASA presents graphs of the planet’s atmospheric carbon dioxide (CO2) concentration (see
Figure 1). These graphs provide a historical comparison of CO2 levels determined from ice core
samples, and more recent direct atmospheric measurements, representing the levels of CO2 in
the atmosphere from 400,000 years ago until 2009. The graphs provide evidence that
atmospheric CO2 has increased since the Industrial Revolution, and that carbon dioxide levels
continue to increase annually (NASA 2009).
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Figure 1 Recent NASA CO2 Data. The chart on the left shows historical CO2 levels, and the chart on the right shows CO2 levels from recent years (NASA 2009).
The IPCC 2007 report also states that all types1 of greenhouse gas emissions have grown
since the beginning of the industrial age and that carbon dioxide is the most important
anthropogenic (human‐caused) GHG. In fact, CO2 emissions have grown approximately 80%
between 1970 and 2004, and represent 77% of all greenhouse gas emissions (UNIPCC 2007).
The IPCC further elaborates that worldwide, the transportation, electricity supply and industrial
sectors have seen the largest growth in GHG emissions from 1970 to 2004.
In the U.S., the Department of Energy (DOE) provides a more recent analysis of domestic
data regarding the source sector of carbon dioxide emissions. According to their report, U.S.
Carbon Dioxide Emissions from Energy Sources 2008 Flash Estimate, CO2 emissions from the
transportation sector surpassed the industrial sector in 1999, and have continued to grow every 1 Greenhouse gases listed by the IPCC: CO2, CH4, N20, HFC’s, PFC’s, and SF6.
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year since, until the onset of the economic slowdown in 2008. Furthermore, the DOE data
show that the transportation sector contributes a significant share (33%) of the total carbon
dioxide emissions generated in the U.S. (DOE Energy Information Agency 2008).
Many organizations around the world and within the U.S. have been involved in
addressing climate change. European governments led the world in actions to reduce their
GHG emissions with the adoption of the Kyoto Protocol in 1997 (UNFCC 2009). The Kyoto
Protocol is an international agreement designed to address climate change, which came into
force February 2005, and has since been ratified by 184 United Nations parties (UNFCC 2009).
The Kyoto agreement does have its weaknesses, namely that developing economies, such as
China and India, were given the latitude to delay their emission cuts beyond the initial deadline
imposed upon other countries (CBC News 2007). Despite the fact that the U.S. government did
not ratify the agreement, the city of Seattle’s Mayor Greg Nickels launched an initiative similar
to the Kyoto Protocol for U.S. cities in June 2005. This initiative, the U.S. Conference of Mayors
Climate Protection Agreement, took action to address climate change at the local level, despite
the lack of federal support. Currently, 971 mayors from all 50 states, the District of Columbia
and Puerto Rico, and representing a total population of over 84 million citizens, have ratified
the mayors’ agreement (U.S. Conference of Mayors 2009). In summary, actions to address
climate change have been ongoing at the international, national and the local level for many
years.
Interest in climate change at the university level has displayed a similar dichotomy.
Academic institutions have, for many years, have provided the scientists heralding the
seriousness of climate change. And now those same institutions are sources of scientists and
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engineers researching and solving the problems the planet faces as a result of climate change.
However, as the institutions or business units they are, the higher education community has
only recently taken specific actions towards more sustainable operations. Granted, some
campuses such as the University of New Hampshire (UNH) have been advancing the principles
of sustainability for many years and claim to have, with their University Office of Sustainability,
the oldest endowed sustainability program in higher education in the U.S. (UNH 2009). In the
past few years though, many colleges and universities have started competing to be the most
“green,” and are actively participating in many of the latest “green ranking” systems. For
example, the Princeton Review recently issued its second annual report ranking colleges and
universities on their policies and practices related to environmental issues, in addition to their
academic offerings. Institutions receiving the latest top Princeton Review ratings included
Arizona State University, Dickinson College, Georgia Institute of Technology, Harvard,
Middlebury College, Northeastern University, the University of California Berkeley, the
University of New Hampshire, and the University of Washington (Galbraith 2009, Princeton
Review 2009). Another example of a green ranking system is the Sustainable Endowment
Institute’s College Sustainability Report Card 2010. This ranking system evaluated 332 schools
by conducting surveys that were sent to students and administrators, and accessing publically
available information. The surveys gathered information about the sustainability of
endowment investment practices, campus operations, and student activities. The Institute’s
top rankings were given to 26 schools, including Arizona State University, Harvard University,
the University of Colorado Boulder, the University of New Hampshire, Dickinson College and
Middlebury College (Sustainable Endowment Institute 2009, Zernike 2008). And finally, the
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Association for the Advancement of Sustainability in Higher Education (AASHE) recently
launched its own tracking framework for sustainability, the STARS (Sustainability Tracking,
Assessment and Rating) system (AASHE 2009).
In addition to the more publicized actions of competing to be the most “green,” there is
a variety of other higher education initiatives underway designed to assess an individual
campus’ contribution to greenhouse gas emissions. These include Focus the Nation, Campus
Climate Challenge, World Resources Institute Greenhouse Gas Protocol, and the American
College and University Presidents’ Climate Commitment (ACUPCC). The ACUPCC initiative is the
most widely used (White 2008).
The ACUPCC is an organization created in 2006 to provide a framework for higher
educational institutions to make commitments that actively eliminate greenhouse gas
emissions from campus operations. The mission and history section of ACUPCC states that the
that institutions of higher education have a responsibility to serve as role models for their
communities and to train people who will develop the social, economic and technological
solutions to reverse global warming (ACUPCC 2009). In its 2008 annual report, it said that more
than 620 schools all over the country, representing about one third of the student population,
had signed the Climate Commitment. By signing the Commitment, the campus presidents are
pledging to complete an emissions inventory, take actions to reduce greenhouse gases and
develop an action plan to become climate neutral. The first step, completing the emissions
inventory, must be finished within one year of the college or university president’s signing of
the climate commitment document. The action plan for becoming climate neutral must be
submitted to ACUPCC within 2 years of the initial signing.
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As a result of conducting the GHG inventories, many institutions have found that the
majority of their GHG emissions are a result of activities broadly associated with energy use and
transportation activities. The ACUPCC reported on September 26, 2009 that, among
doctorate‐granting universities, two of the largest sources of emissions resulted from
purchased electricity/heating/cooling (47%), and from commuting activities (14%) (ACUPCC
2009). The goal of reducing the energy required to operate the campus has lead to initiatives
aimed at improving the energy efficiency of buildings; activities ranging from the simplest of
replacing light bulbs with compact fluorescents and installing motion sensors, to the most
capital‐intensive of only building new facilities certified by the U.S. Green Building Council’s
Leadership in Energy and Environmental Design. Other approaches to reducing the GHG impact
of energy use is to buy a portion of the campus’ needs from renewable sources or cogeneration
of energy from landfill gas or biomass wastes (Cleaves et al. 2008, Ernst 2009).
Addressing commuting emissions requires the study of transportation patterns used by
the university community. Transportation research is an established field within Geography,
although interest in minimizing transportation‐related emissions is a relatively new perspective.
Transportation patterns are inherently geographical, as they represent travel between two or
more nodes along some sort of network (e.g. roads, rail lines, waterways, etc.). Historically, the
academic and business worlds have been interested in optimizing the efficiency of those
networks. In 1909, Alfred Weber, a German economist, published the first general theory of
industrial location in which he developed a model to minimize distances between a warehouse
and its customers (Weber 1909). This field subsequently evolved through the work of Isard
(1956) and others, who were mostly concerned with the location of industrial facilities.
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More recently, the geographical discipline of location allocation analyses has evolved in many
directions. From a commercial prospective, location studies explore optimal sites for
production plants, distribution centers and retail outlets. Cooper (1963) describes methods and
equations to address industrial location‐allocation problems with respect to the establishment
of warehouses, distribution and communication centers, and production facilities. Ghosh and
Craig (1986, 354) describe a procedure for determining retail location strategies using a
“modified maximal covering location allocation model.” For a more specific case in the health
care industry, the goal is to balance locations of medical facilities for patient accessibility with
the cost of building those facilities. Dökmeci (1977) developed a planning model for locating
different types of facilities in a regional health system (e.g. the medical center, local hospitals
and health centers). This quantitative model was based on minimizing total costs, defined as
transportation costs plus facility costs. Generally, all these private sector models seek to
minimize the corporation’s total costs of transportation and facility construction.
For public sector, the locations of government centers, service facilities such as fire
stations, water and waste treatment facilities, and schools are studied. Fitzsimmons and Allen
(1983) discuss a good example of locating out‐of‐state offices for the Texas State Comptroller
Tax Audit division. The goal of this analysis was to optimize out‐of‐state office locations for
staff retention and the efficient conduct of tax audits of corporations that do business in Texas.
The accessibility of school locations is another commonly evaluated public service. For
example, Talen (2001) studied distances between students and 84 elementary schools located
in West Virginia, and the associated impact on travel costs to school versus socioeconomic
status.
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So, the academic study of facility locations and their associated transportation networks
have been occurring for at least a century. Generally speaking, the goals of these analyses have
been to maximize a business’s operating profit, provide optimal locations for customer service,
minimize response times in an emergency or minimize travel time and cost. As the impact of
the transportation system on climate change becomes a more critical issue, these same
location allocation methods and models can be applied to help reduce greenhouse gas
emissions generated by transportation activities. To clarify, the objective of this research was
not to pursue this level of location analysis, but rather to further characterize UNT‐specific
transportation data. However, future research of the UNT transportation network could
benefit from application of those methods to the analysis of specific transit problems.
At the specific campus level, addressing transportation related GHG emissions is a
complicated task but one that universities have tackled by involving all members of the
academic community. As a part of the ACUPCC‐required initial greenhouse gas inventory, many
universities have taken the preliminary step of conducting a transportation survey to gather the
data needed to calculate commuting emissions. These surveys have ranged from cursory
estimations of commuting distances travelled, to very detailed questionnaires of faculty, staff
and students.
An example of a very detailed approach to the transportation survey was found at UC
Berkeley. Berkeley has been analyzing their campus’ transportation data since 1960. A review
of the Transportation chapter of their 2005 Sustainability Assessment, and the associated 2005
Student Transportation Survey, reflects a thorough approach to data collection, containing 33
multipart questions. The extensive questions included student housing types for each of three
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semesters in an academic year; if the student moved during the year and why; the primary
mode of transportation used last year and currently; if the mode of transportation was
changed, then why; how the “primary” mode of transportation is defined by the student; which
of 15 defined transit modes the student typically uses to get to campus for each of the seven
days of the week; what time the student arrives and departs campus for each of the 7 days of
the week; how the student gets around on campus once he/she arrives; if the student uses
public transit and why; what kind of transportation problems the student experienced and how
frequently have they occurred this semester; and further questions about parking, bicycling,
walking, use of public transit, source of campus information, etc. (UC Berkeley 2005).
Another detailed example can be found with the University of New Hampshire. UNH’s
main campus is located in the suburban area of Durham, NH, about an hour away from Boston
and Portland, Maine. The UNH Transportation Policy Committee conducted a web‐based
Community Transportation Survey in 2007 covering students, faculty, staff, and visitors. That
survey consisted of 61 questions, with some questions targeting only specific groups from the
UNH community. The questions covered the use of and satisfaction with the 3 available public
transit systems; the zip code of residence; transit modes used to get to campus; other travel for
working off campus or other reasons; parking locations and permitting process/fees; and
awareness of the alternative transportation programs currently offered (e.g. guaranteed ride
home program, bike share, free transit, carpool parking). A subset of the web survey’s
respondents was then interviewed by phone with an even more detailed set of questions (UNH
2007).
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An example of a transportation survey at the less detailed end of the spectrum was
found in the University of Texas (UT) at Austin. The UT 2009 Greenhouse Gas Inventory report
used an existing transportation survey that was conducted in December 2000. That survey
collected data on the commute origin (by zip code) from the top ten areas ranked by reported
number of trips to campus. These top ten areas represented 66% of the survey respondents,
and the annual round trip commuting distances were subsequently calculated for each of the
ten areas, by the various reported transportation modes (e.g. walking, bicycling, driving, or
taking a bus to campus). These data were then scaled up from the 66% portion of trips, to total
campus trips and commuting distances for the year 2000. The 2000 estimate was subsequently
projected upwards to the 2006 student and faculty/staff campus populations, arriving at a final
commuting distance estimate used in the 2009 greenhouse gas inventory report (UT Austin
2009).
In summary, the transportation surveys conducted at other universities cover a broad
spectrum of detail and reflect the maturity of the study of transportation issues at the various
campuses. The complexity of the UC Berkeley transportation study reflects years of experience
in analyzing campus transit modes. As with Berkeley, the transportation survey at the
University of New Hampshire suggests a more mature transportation planning process, with
campus initiatives that have been implemented and are being evaluated to reduce commuting
emissions. Conversely, the transportation planning programs at UT Austin and UNT are just
beginning to address the greenhouse gas emissions created from commuting activities.
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2.2 UNT Specific Background
The UNT Sustainability Council appointed a five‐person subcommittee to conduct the
campus’ initial GHG emissions inventory. That subcommittee consisted of two faculty
members, the Director of the Office of Sustainability, and a graduate and undergraduate
student. Because UNT has traditionally been a campus with a significant commuter population,
commuting emissions were projected to be a significant source of total GHG emissions. In fact,
according to a recent Dallas Morning News article, only 17% of the undergraduate student body
is living on campus (Hacker 2009). With a community of more than 29,000 fulltime and part‐
time students in the spring semester of 2009, and 3100 fulltime faculty and staff, commuting to
campus was expected to generate a significant amount of the university’s greenhouse gases.
In preparing to collect the transportation data needed to support commuting emissions
calculations, various means of gathering the data were considered. One option discussed was
to get the transportation data from existing UNT sources, namely the Registrar and the campus
police department’s parking permit program. The Registrar data was discarded as a viable
alternative because the students’ home addresses were likely to be “permanent addresses” and
therefore not local. There was also no information in the Registrar’s database regarding the
various transportation modes (walking, biking, driving, etc.) used by the UNT community. The
other potential source, the parking permit database, was discarded because it did not capture
those students that do not have a parking permit, and those students that use an alternative
other than driving to get to campus.
Although unknown to the Sustainability Council task force before this survey was
conducted, a previous transportation survey had been conducted by the Office of Institutional
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Research and Accreditation in 2003, at the request of the UNT Chief of Police. That survey was
mostly designed to determine the parking and transportation needs of students, faculty and
staff, evaluate the need to construct a parking garage, and evaluate the bus transit system in
effect at the time (UNT 2003). Although it may have been helpful to review this survey prior to
creating the 2009 survey, the 2003 survey was mainly focused on the parking needs of the UNT
community, rather than gathering the types of data needed for estimating greenhouse gas
emissions.
A short‐term review of methods that other universities had used to estimate commuting
emissions was also conducted. Data elements in the review included methods for determining
commute origin location, distances travelled, transportation modes used to get to campus, trips
per week made by each mode, and types of vehicles used for those individuals driving to
campus. As previously discussed, the data elements and data‐gathering methods used at other
universities covered a broad spectrum, from the very detailed at UC Berkeley, to the more
general estimations at UT Austin.
In light of the limitations of existing data sources, the UNT task force decided to create
and implement a new transportation survey to collect UNT‐specific commuting pattern data.
Ideally, a survey with the level of detail seen in the University of New Hampshire or UC Berkeley
surveys would have been preferable. However, the time required to develop and conduct such
a survey was more than could be accommodated before the existing GHG inventory deadline of
May 15, 2009. Therefore, a practical approach was taken and a basic transportation survey was
developed for students and faculty/staff to extract the data needed for the GHG inventory.
This survey was designed with several goals in mind. The primary goal was to gather the
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commuting distance and transit mode data needed to complete the GHG emissions inventory.
A secondary goal was to collect data that would allow a more thorough follow‐on analysis of
the transportation patterns, including the originating locations of students, faculty and staff. A
final goal was to gather some general demographic data characterizing the commuting students
and faculty/staff.
With those goals in mind, the 5‐member Sustainability Council subcommittee, in
conjunction with the UNT Survey Research Center, developed the questions for the
transportation survey. The student survey was quickly piloted in several earth science lab
sections, querying approximately 200 students. The results of that review were then
incorporated into the final questionnaires. The surveys asked what location (zip code) students
and faculty/staff where commuting from, and how many trips/week were made using the
various transportation modes (walk, bike, motorcycle/scooter, bus, carpool, drive alone).
Another key piece of data gathered by the surveys was vehicle type (e.g. compact car, sedan,
small or large truck, small or large SUV, etc.). That data was necessary to compare the
assumptions about UNT’s vehicle mix and resulting fuel efficiency versus the Bureau of
Transportation Statistics fuel efficiency factors provided in the GHG calculator software.
After the survey was conducted, the essential data was extracted and calculations made
to estimate UNT’s commuting emissions. The UNT GHG inventory was then submitted to
ACUPCC on May 14, 2009. The next step in the ACUPCC Climate Commitment process is the
requirement for UNT to submit its Climate Action Plan by May 15, 2010. This action plan will
outline the university’s efforts to reduce the Denton campus’ greenhouse gases. Towards that
end, UNT needs to conduct a more comprehensive analysis of the dataset collected by the
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transportation survey. These datasets offer the most current and complete information
available about UNT commuting patterns. They present an opportunity to gain in‐depth
information about where in the north Texas area students and employees are travelling from,
what means they use to get to campus and how many times per week they make the trip. An
important fact to note from the completed emissions inventory is that UNT does (as projected)
generate a significant proportion of GHG emissions by commuting activities (29%), in
comparison to the ACUPCC‐reported average for doctorate‐granting universities (14%) (ACUPCC
2009). The UNT community and the Office of Sustainability should not be expected to
implement initiatives to reduce these significant commuting‐related GHG emissions, without
having a more thorough understanding of the campus’ transportation patterns. This thesis will
contribute to that understanding by further analyzing the data from the Transportation survey,
allowing UNT to focus future actions on reducing emissions from higher‐impact commuting
activities.
19
CHAPTER 3
RESEARCH QUESTIONS AND METHODOLOGY
3.1 Research Questions
This research characterized the commuting geography of students, faculty and staff
travelling to UNT’s Denton campus. Specifically, it defined where in the north Texas region
students were travelling from, how many trips per week were being made, and how many miles
were being driven. The thesis also explains the transit modes that were used for student
commuting, describing the share of trips per week made by single‐occupancy‐vehicle driving,
carpooling, walking, bicycling and bus riding. Any specific demographic or socioeconomic
factors that influenced the choice to drive alone to campus are also discussed. In a similar
manner, this research characterized the commuting geography of faculty and staff. It
determined employee‐commute trip origins, calculated travel distances, and examined whether
any specific socioeconomic or demographic factors are influencing their decision to drive alone
to campus.
3.2 Methodology
3.2.1 Survey Methods
The transportation survey collected data for two population groups of commuters:
students and faculty/staff. For each group, the sampled individuals provided information
related to his or her typical weekly commuting patterns. The student survey asked 18
questions, gathering information about the trips per week typically made to campus by 9
different modes of transportation; the zip code where the student lived at the time of the
survey; the make, model and size of vehicle used when driving; the type of parking used during
20
the week, whether the student had a permit, and what type; the number of credit hours
currently enrolled in and class standing; age group; gender; housing type; employment; and
personal income level category. The faculty/staff survey contained similar questions (16),
adding a question for whether children under the age of 18 lived in the employee’s household,
and did not include any student‐specific questions. A copy of these surveys can be found in
Appendix A.
In retrospect, and given more than a few weeks time, the UNT survey could have been
designed to better collect the commuting details needed for future emission reduction planning
efforts. For example, a limitation of the current surveys is the first question asking which of
nine different transportation modes were typically used in a week. Had that question been
asked for the “trips per day” for each of the seven days of the week, data might have been
available to analyze the feasibility of shifting to a 4‐day instructional week. The question was
initially structured to ask for that information by trips/day in the piloted survey, but was
simplified to trips/week in the final surveys. Another limitation of the current student survey
was the omission of the question asking whether the student had children living at home. That
question was included in the faculty/staff survey, but not the student survey. Having school‐
age children at home may force a nontraditional student to drive alone to campus to
accommodate the delivery of children to school or day care. A third limitation of the current
surveys was that no questions were structured to ask why someone drove alone to campus.
These questions were considered, but discarded as being not necessary to the immediate goal
of calculating greenhouse gas emissions from commuting.
21
After the survey’s form was finalized by the Sustainability subcommittee, UNT Survey
Research Center personnel then programmed and conducted the survey in March 2009 as a
pro‐bono contribution to the overall goal of determining the campus’ greenhouse gas
inventory. UNT President Bataille kicked off the survey with her personal email announcement
sent to all UNT community members, explaining the intent of the survey and encouraging
participation. Posters displayed around campus also advertised the survey, and participation
was further encouraged by $3000 worth of prizes. A web link to the survey was provided in the
President’s email message, and was also featured on the UNT Home Page, and the MyUNT and
Blackboard Vista login pages.
3.2.2 Survey Sample Representative of the Population?
As a preliminary analysis step, chi‐square tests were conducted to determine if data
from the survey respondents is representative of the full population of students and
faculty/staff. The null hypothesis for the chi‐square test is there is no difference between a
variable’s observed frequencies and the variable’s expected frequencies. If a large value of chi‐
square is calculated, the result indicates there is a large difference in the sample and population
(Ebdon 1977).
Data on the student population (spring 2009 semester) were obtained from the UNT
institutional research department and their Fact Book website for gender, age group and class
standing (freshman, sophomore, etc.) (UNT 2009). Data for faculty/staff gender, age group and
employment classification (faculty or staff) variables were also obtained from the Institutional
Research department.
22
3.2.3 Student Representativeness
The UNT transportation survey resulted in 6,619 student responses, representing 22.8%
of the population. Chi‐square tests were performed on three variables to determine if the self‐
selected sample of students responding to the survey was representative of the full population.
The results of the tests are detailed in Table 1.
Table 1 Student chi‐square analysis.
Variable Calculated Chi‐Square
Chi‐Square at p = .01 (df)
Accept/Reject H0
Gender 95.1 6.64 (1) Reject H0 Class Standing 32.9 13.28 (4) Reject H0 Age Group 24.7 15.09 (5) Reject H0
All three chi‐square tests rejected the null hypothesis, leading to the conclusion that the
students sampled were not statistically representative of the actual student population. For
the gender variable, 62% of the survey’s respondents were female compared to 56% in the
actual population. This higher female response rate in self‐selected samples is a trend
observed in previous UNT surveys. In fact, the UNT fall 2003 parking/transportation survey
reflected a 64% female sample, compared to an actual population that was 57% female (UNT
2003).
For the class standing variable, the chi‐square test conclusion is not robust, as it is
questionable whether students are able to correctly categorize their class standing compared to
the Registrar’s official records. For example, a student may consider himself a junior because
he has been attending classes for three years. However, he may not have accumulated enough
23
credit hours to be officially classified as a junior, thus leading to this statistical difference in the
observed vs. expected frequencies (see Table 2). Twenty‐four percent of students from the
survey classified themselves as juniors, when in fact the Registrar data for the spring 2009
semester lists 21% of students as juniors. This classification “error” or difference is the biggest
contributor to the large calculated chi‐square statistic that rejected the null hypothesis. Had
this “junior” classification error not occurred, the test would most likely have concluded the
sample of students responding to the survey was in fact representative of the entire
population.
Table 2 Student class standing.
Class Standing % Respondents (Survey)
% Population (Registrar)
Freshman 11.4 10.9 Sophomore 14.5 15.8 Junior 24.1 21.4 Senior 29.2 30.5 Grad Student 20.8 21.3
There were also some questions about the mismatch of age group data found at the
Institutional Research (IR) website compared to the age groups used in the survey. To get a
comparison of ages with identical groupings, IR subsequently provided a frequency listing by
age for the spring 2009 enrollment, and this data was used for the chi‐square comparison (see
Table 3). The resultant chi‐square test for the age group variable lead to the conclusion there
is some difference between the self‐selected sample and the population. However, looking at
the proportions of survey respondents compared to the population, it is a small, albeit
statistically significant difference. This discrepancy is likely due to the age group categories
24
picked for the survey, which are biased to collect more detail about younger students. During
the spring semester, UNT had students ranging from 15 to 74 years old, although more than
three‐fourths of the enrollment was aged 19 – 30. Future student transportation surveys
should widen the age groups, or collect age data numerically rather than categorically.
Table 3 Student age groups.
Age Group % Respondents (Survey)
% Population
<19 years 6.1 7.0 19 – 20 23.7 21.5 21 – 22 24.5 24.4 23 ‐ 25 17.5 18.5 26 ‐ 30 12.2 12.3 > 30 16.0 16.3
3.2.4 Faculty/Staff Representativeness
The faculty/staff transportation survey resulted in 1,453 responses, representing 45.3%
of the fulltime population, or 27.8% of the total population. To determine if the sampled
respondents were representative of the total population of employees, chi‐square tests were
also conducted. UNT’s Institutional Research department provided data on the employee
population for the spring 2009 semester for gender, job classification (faculty or staff) and age
group variables. Chi‐square tests were performed on all three variables and the results are
detailed in Table 4. For all three tests, the null hypothesis was rejected, leading to the
conclusion that the self‐selected sample of faculty and staff was not statistically representative
of the actual employee population.
25
Table 4 Faculty/staff chi‐square analysis.
Variable Calculated Chi‐Square
Chi‐Square at p = .01 (df)
Accept/Reject H0
Gender 32.6 6.64 (1) Reject H0 Classification 18.8 6.64 (1) Reject H0 Age Group 205.3 20.1 (8) Reject H0
Examining the gender variable, more females (61%) responded to the survey than are
reflected in the actual population data (54%). This is the same trend seen in previous UNT
surveys, and with students in this survey. In addition, a larger proportion of staff (71%) versus
faculty responded to the survey than are found in the actual population (66% staff). The bias
towards females answering a self‐selected survey likely skewed the chi‐square test results for
both the gender and job classification variables. Further comparison of the survey response
rates by both gender and job classification illustrates this point. Had the chi‐square test been
done on faculty only, the null hypothesis would have been accepted, i.e. there is no significant
difference between the sample and full population. However, looking at the test for only staff,
the statistical difference appears. The combination of the higher than expected response rates
from females and staff likely led to the rejection of the null hypothesis for both variables. A
possible reason for the higher female staff survey response rates is they may be more likely to
have desk jobs than male staff. And with easy access to a computer, female staff has more
opportunity to participate in surveys than their male counterparts (see Table 5).
26
Table 5 Faculty/staff gender and job classification.
Job Classification
Male Sample
%
Male Population
%
Female Sample
%
Female Population
%
Chi‐Square Accept/Reject H0 ?
Faculty 55 53 45 47 Accept Staff 32 43 68 57 Reject Total 39 46 61 54 Reject
The third chi‐square test, performed on the age group demographic factor, also resulted
in a rejection of the null hypothesis. In the survey, the younger age groups (< 30 years) did not
respond at the expected rate, and two mid‐range age groups responded at a higher than
expected rate (see Table 6). This data was also examined by separating faculty and staff age
groups, with no change in the test result. The null hypothesis for the chi‐square test was still
rejected. Future transportation surveys will need to be designed to collect data from a more
statistically representative sample of students and employees. Without the impending GHG
emissions inventory deadline imposed upon this survey, time should be available to plan and
conduct a future survey that samples a broader group of the UNT community.
Table 6 Faculty/staff age group.
Age Group Sample % Population % < 18 – 30 12.9 26.8 31 – 40 20.8 22.8 41 – 50 27.8 20.2 51 – 60 27.8 20.0 > 60 10.7 10.2
27
3.2.5 Vehicle Mix
The Sustainability subcommittee used software provided by the Clean Air‐Cool Planet
organization (CA‐CP) to calculate greenhouse gas (GHG) emissions from all campus operations
(e.g. purchased electricity, direct transportation, waste generation, employee and student
travel), as well as commuting activities. To determine the emissions specifically generated by
commuting, the model makes an assumption of an average mileage rate for the vehicle fleet.
So although vehicle mix data is not directly relevant to the primary commuting geography
questions addressed in this thesis, it was important to test the reasonableness of the mileage
assumption used in the CA‐CP software, which subsequently calculated greenhouse gas
emissions from travel distance inputs.
In the software, fuel consumption is derived by multiplying total annual distance driven
by an average fuel economy value of 22.10 miles per gallon (mpg) (CA‐CP 2009). The basis for
this mileage factor was taken from the Bureau of Transportation Statistics (BTS) report National
Transportation Statistics 2005 (BTS 2005). To determine if the types of vehicles driven by UNT
students and employees are comparable to the BTS assumptions, the survey collected “type of
vehicle” data for those respondents that reported commuting methods of “Drive Alone” or
“Drive/Ride in a Carpool.” That data was then compared to the BTS vehicle mix assumptions,
and the decision was made whether to proceed with the software’s mileage rate for UNT’s
emissions calculations.
3.2.6 Transportation Modal Split
The phrase “transportation modal split” refers to the various means that the UNT
community uses to arrive at and travel around the campus. Survey results suggest that both
28
students and faculty/staff are using multiple modes throughout a typical week to accomplish
their goals. For example, a faculty/staff member may walk or ride a bicycle to campus three
days per week, but drive alone the other days due to inclement weather. Also, a student may
drive alone to a remote parking lot on or near campus, and then take one of the shuttle buses
to get to his or her classroom. For this reason, the transportation modal split was evaluated by
looking at the proportion of trips per week generated by each mode, rather than the number of
people responding that they used a particular mode.
3.2.7 Driving Distance Calculations
To answer the research questions related to commuting geography (zip code location,
commuting trips/week, distance and travel modes), data from each of the surveys was sorted
by commute mode and home zip code, and then downloaded to spreadsheets for determining
distances travelled from all residential zip codes by the various commuting modes. This
analysis was done for both the faculty/staff and student databases.
Some assumptions were made for the driving distance calculations, including:
• That the transit mode data resulting from the survey representing a “typical week”
are in fact representative of the entire UNT population year around.
• Students (29,038) travel to campus at the frequencies, and by the reported transit
modes, 32 weeks per year for the fall and spring semesters of the academic year.
• A smaller population of students (15,000) travels to campus 4 times per week during
the 10 weeks of summer semesters.
• Fulltime faculty/staff (3184) travel to campus 48 weeks of the year.
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3.2.7.1 Trips/Week Calculations
In general, “trips per week” data were calculated for each residential zip code provided
by the respondents (for both Student and Faculty/Staff groups), and aggregated by one of four
driving modes (Drive Alone, Drive in a Carpool, Ride in a Carpool, or Ride a
Motorcycle/Scooter).
3.2.7.2 Weighted Trips/Week Calculations
Carpools were assumed to have 2 members each—a driver and a rider. Therefore, a
weighted number of trips per week were calculated to adjust the number of trips being made
from residential zip codes by the “Carpool” transit mode. The following formula was used for
this calculation:
WTi = DTi + MTi + 0.5(CTi)
where WTi = weighted trips per week from zip code area i
DTi = drive alone trips per week from zip code i
MTi = motorcycle/scooter trips per week from zip code i
CTi = all carpool trips per week from zip code i
3.2.7.3 Distance Values for each Residential Zip Code
The distance from the centroid of each residential zip code provided by respondents, to
the UNT campus (zip code 76201 used), was calculated using ESRI geographic information
system (GIS) software.2 In addition, after consulting with Ruth Boward, Senior Transportation
2 The UNT campus zip code is actually 76203. However, this is a post office box in Denton with no geographical shape associated with the zip code. Therefore, the downtown Denton zip code (76201), which does have a shapefile, was used as the campus location.
30
Planner at the North Central Texas Council of Governments (NCTCOG), a more reasonable
estimate of actual road miles travelled was calculated by multiplying the centroid‐to centroid
distance by a factor of 1.18 (email correspondence Boward to Waskey, January 23, 2009).
3.2.7.4 Total Distances Travelled
Using spreadsheet software, and for each residential zip code, a weighted trips/week
value was calculated, and then multiplied by the previously determined one‐way travel distance
for that zip code. The following formula was used for each zip code:
WDi = WTi x di x 2
Where WDi = weighted distance for zip code i
WTi = weighted trips per week from zip code area i
di = distance from zip code area i to zip code area 76201
Total weighted round‐trip distances travelled per week, for all zip codes, were then summed for
both student (Σ WDst) and faculty/staff (Σ WDfs) datasets.
And finally, a total distance travelled per year was calculated for each population, using
the following formulas:
Dfs = Σ WDfs x 48 x (3184/1453)3
Where Dfs = faculty/staff total distance travelled per year
Dst = [ΣWDst x 32 x (29,038/6619)] + [ Σ WDst x 10 x (15,000/6619)]4
3 The “(3184/1453)” term in the faculty/staff distance calculation extrapolates the distance data from the 1453 respondents to the fulltime faculty/staff population of 3184. 4 Likewise, the “(29,038/6619)” and “(15,000/6619)” terms extrapolate the distance data from the 6619 respondents, to the student population in the spring & fall semesters (29,038), and summer (15,000) semesters.
31
Where Dst = student total distance travelled per year 3.2.8 Bus Distance Calculations
UNT students are offered unlimited rides on any of three Denton County Transportation
Authority (DCTA) bus lines: the DCTA Connect, which provides bus routes in the city of Denton;
DCTA Commuter Express, which operates an express bus route from Dallas to Denton on
Interstate 35E; and the UNT Shuttle, which offers specialized service around the campus and to
several apartment communities in Denton that house students. The methodology used to
calculate bus distances travelled differs from driving distance calculations. For each of the
three types of bus routes, the number of trips per week was calculated for both student and
faculty/staff populations. Average round trip distances were then calculated for the UNT
Shuttle and DCTA Connect types of routes, using data provided by DCTA personnel. Average
trip length for the DCTA Commuter Express route was estimated to be 20 miles one‐way, since
most UNT commuters using that bus get on at the Carrollton or Lewisville stations, rather than
a Dallas station.5 Having calculated average round trip distance and trips/week data, the
following formulas were used to estimate total bus distances travelled:
5 Telephone conversation with DCTA personnel and UNT’s Associate Director of Transportation Services.
32
For faculty/ staff:
BDfsu = 2 x dun x BTfsu x 48 x (3184/1453)6
Where BDfsu = total UNT bus travel distance for faculty/staff
du = average one‐way trip length for the UNT shuttle bus
BTfsu = number of UNT shuttle bus trips per week made by faculty/staff
For students:
BDstu = [2 x du x BTstu x 32 x (29,038/6619)] + [2 x du x 4 x 10 x (15,000/6619)]7
Where BDstu = total UNT bus travel distance for students
BTstu = number of UNT shuttle bus trips per week made by students
3.2.9 Commuting Geography Analysis
One of the primary objectives of the geographical analysis was to determine if there are
clusters of high‐impact commuting activity that should be addressed (as defined by quantity of
driving trips/week)? To evaluate these questions, maps were generated using geographic
information system (GIS) software. Trips per week data made by the various transportation
modes were plotted against the zip code area of the commute’s origin, for both student and
faculty/staff. The GIS system was also used to overlay major transportation routes on the maps
to see if the clusters of high‐impact commuters were coming from areas that have existing bus
transit routes, and if there were clusters from areas along major roads that could benefit from a
park and ride‐type vanpool or carpool system. However, a limitation of the existing
transportation survey data was encountered with trying to analyze the location information for 6 Similar formula used for both DCTA Connect and DCTA Commuter Express distances/year. 7 Similar formula used for both DCTA Connect and DCTA Commuter Express distances/year.
33
commuters in the immediate city‐of‐Denton area. Because commute trip locations are not
defined more specifically than zip code, the data were not always precise enough to compare
local bus routes with clusters of driving commuters in the Denton city area.
Although mapping student transportation methods can provide a visual indication of the
overall pattern of commuting geography, statistical spatial analysis can serve to quantify the
observed patterns. In the case of the transportation survey data, we are interested in
identifying “hot spots” of high emission‐producing commuting activity, which is essentially
event‐based data (counts) that describe driving trips to campus. An excess of events coming
from particular area can then be defined as a cluster or hot spot that is significantly different
than the spatial pattern expected to occur as the result of random variation (Jacquez 2008).
Using spatial statistics can confirm the significance of visually observed patterns. Furthermore,
after identifying the significant hot spots of driving, UNT can consider actions to reduce the
resultant emissions.
Spatial statistics tools are available in the GIS software used to create the previously
discussed maps. The Getis‐Ord Gi* Hot Spot analysis tool was used to analyze the patterns of
student weighted driving trips per week on the zip code level. The null hypothesis for the test is
that the driving trips data display no spatial pattern and are randomly distributed across space.
The Gi* statistic is described by Ord and Getis (Ord 1995) and is essentially a z‐score (measure
of standard deviation) calculated for each feature (zip code area). For example, if a hot spot
analysis results in a low p‐value (significance) and either a very positive z‐score (hot spot) or
very negative z‐score (cold spot), the null hypothesis must be rejected, implying it is very
unlikely that the observed pattern is random.
34
3.2.10 Socioeconomic/Demographic Influence
This thesis also explored the transportation survey data to determine what demographic
or socioeconomic variables influence or predict the “drive alone” commuting mode choice.
Because the data is categorical, the determination was made by conducting chi‐square
statistical analyses. The intent of these analyses was to examine if there were any factors that
influenced a student or faculty/staff member to drive alone, or make more drive alone trips to
campus per week. Demographic factors that may have influenced the travel mode choice
include gender, age group and having children in the household. For example, female students
or employees may be more likely to drive alone due to their childcare responsibilities.
Economic factors that may have influenced the travel mode choice are income and housing
type. A faculty/staff member or part‐time student may not have been able to find suitable
housing in the neighborhoods of Denton that are currently served by the bus system. Another
economic factor for students might have been having an off‐campus job, requiring the student
to use a personal vehicle to get to work. The data were limited to a general look at the
influencing factors, as there were no questions in the survey that attempted to examine
motivations for the various commute mode decisions. The chi‐square analyses were therefore
intended to be a broad look at any factors that may be influencing the drive alone decision.
To simplify the drive alone commuting trip data, the variable was recoded from 20 different
categories into three, i.e. zero trips, 1 – 5 trips per week, or 6 or more trips per week. The null
hypothesis for the chi‐square tests was there is no relationship between the two variables. Or,
stated differently, the socioeconomic or demographic factor does not distinguish between the
student and employee’s choice to drive alone to campus or not drive alone.
35
The methodology chapter has provided detail regarding the transportation surveys and
the analyses conducted on the resulting datasets. Specific aspects of the dataset analyses that
have been described include vehicle mix, modal split, driving distances, bus travel distances,
and commuting geography. An examination of behavior‐influencing socioeconomic or
demographic factors was also described. The result of the statistical determination whether
sampled individuals were representative of the full populations of students, faculty and staff is
not robust. Although the strict interpretation of the testing resulted in rejection of the null
hypotheses, there are reasons to consider that the survey’s structure may have forced data
collection bias or errors. Examples of these are female staff’s ready access to computer‐based
surveys compared to male staff members’ access, potential student reporting errors in the class
standing variable, and the survey’s bias towards collecting more detailed student age group
data for younger students. So, because the transportation survey datasets represent the most
complete and thorough information regarding the UNT community’s commuting behavior, they
were used to estimate greenhouse gas emissions in May 2009. Further characterization of the
data and resulting transportation patterns is essential to developing emission‐reducing
corrective actions.
36
CHAPTER 4
FINDINGS
4.1 Survey Response and Descriptive Data
4.1.1 Survey Response
The survey resulted in 8,072 responses, representing approximately 24% of the UNT
population of students and faculty/staff at the Denton campus (see Table 7).
Table 7 Transportation survey response.
Faculty/Staff Students # Respondents 1,453 6,619 Population (part‐time & fulltime)
5,233 29,038
Response Rate 27.8% 22.8 %
4.1.2 Descriptive Data: Students
Students responding to the transportation survey represent a diverse collection of
individuals coming from all over the Dallas Fort Worth metroplex. More than 60% of the
respondents were female, and the average age was between 22 and 23 years (see Figure 2).
With respect to housing type, most students live in a house (38.4%) or apartment (40.5%),
while only 18.2% are living in a dormitory. This low level of students living in a dorm on‐
campus is consistent with expectations of UNT’s large commuter population. Students also
reported their class standing in the survey, and more than half (53.3%) consider themselves to
37
be juniors or seniors (see Figure 3). The median credit hour enrollment reported by the
sampled students was 12 hours.
Figure 2 Student gender and age.
Figure 3 Student class standing.
The majority of students (69%) responded that they work either fulltime or part‐time.
However, the youngest student age groups skew this 69% employment figure downward. In
fact, only 39% of the 19‐and‐under age group reported working, while student age groups older
38
than 20 years report being employed at rates of 71 to 79%. Of those employed, almost 70% of
them work off‐campus. Employment status also varies with class standing. Fifty‐nine percent
of freshman report they are not working, and not surprisingly, that unemployed status
diminishes as class standing rises to the senior (26%) and graduate levels (15%) (see Figure 4).
Figure 4 Student employment status.
Students that drive alone to campus reported they make an average of 3.5 trips (one‐
way), and 45% of them reported having a parking permit. The type of parking permit used was
usually a P (premium commuter) or G (general commuter) (see Figure 5). Detailed tables of
student descriptive data are included in Appendix B.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Freshman Sophomore Junior Senior Grad
Student Employment Status
None
Part‐time
Fulltime
39
Figure 5 Student parking permits.
4.1.3 Descriptive Data: Faculty/Staff
Faculty and staff responded to the transportation survey at a slightly higher
participation rate than students. As with the students, more than 60% of the employees
responding to the survey were female (see Figure 6). The average employee’s age was in the
41 – 50 age group, and 71% of the respondents classified themselves as staff and 29% as
faculty.
Figure 6 Faculty/staff gender and age group.
40
Household income for the respondents was fairly evenly spread among four of the five
groups, with the average falling in the $50,000 to $75,000 category (see Figure 7). The majority
of employees reported they live in a house (84%), rather than an apartment, duplex or mobile
home (see Figure 8), and less than 34% of them have children under the age of 18 living in their
home. And finally, 90% of faculty and staff have parking permits, 78% of which are D permits.
Additional detailed tables of faculty/staff descriptive data are provided in Appendix C.
Figure 7 Faculty/staff household income.
Figure 8 Faculty/staff housing.
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4.2 Vehicle Mix
Data for the number and types of vehicles registered in the U. S. in 2004 was taken from
the Bureau of Transportation Statistics (BTS). The BTS data was then compared to similar data
collected from those UNT survey respondents that reported “driving alone” or ”driving/riding in
a carpool” to campus (see Table 8). Vehicle type data was not collected for motorcycles in the
survey, so there is some discontinuity in the comparison of UNT data to BTS data. However, it
is known from other survey questions that motorcycle use accounted for 0.99% of faculty/staff
trips and 0.57% of student trips in a typical week. Because both groups used a motorcycle to
get to campus for less than 1% of their travel, the data was considered an insignificant
contributor to the vehicle mileage rate estimation.
Table 8 Vehicle type.
Vehicle Type BTS % of Total Faculty/Staff % Student % Passenger Car 58.3% 59.2% 72.9% Motorcycle 2.5% * * Other 2‐axle, 4‐tire Vehicles (Trucks, SUV’s)
39.2% 40.8% 27.1%
*Data not collected
Since UNT’s vehicle mix for Faculty/Staff was almost identical to that mix in the BTS
tables, we accepted the Clean Air Cool Planet organization’s mileage factor of 22.10 mpg as a
reasonable estimate. The model’s mileage estimate was also accepted for students, although
since their vehicle mix is skewed more towards passenger cars, a potentially higher overall
mileage rate could have been calculated, resulting in lower greenhouse gas emissions. This
42
variation was estimated to be negligible, though, as the EPA states, “that a small variation in
the fuel economy number will not change the rough estimate of greenhouse gases
derived…”(EPA 2005).
4.3 Transportation Modal Split
Transportation modal split refers to the various means that the UNT community uses to
arrive at and travel around the campus. Students made a total of 50,951 trips per week to their
destination on campus by walking, biking, riding a bus, driving or carpooling. Faculty/staff
made a total of 7,651 trips per week. Specific details of the various trips by mode are included
in Appendices B and C. Figures 9 and 10 summarize the modal split data for each population
group.
As expected, faculty/staff trips to campus are accomplished almost exclusively by
driving alone (77%). In contrast, student trips are split fairly evenly among walking (30%),
driving alone (29%) and riding a bus (24%). It is also interesting to note that carpooling is used
at similar rates for faculty/staff (10%) and students (11%), and that riding a motorcycle or
scooter is used by 1% or less for both groups. Further discussion of transportation modes is
included in the section titled Commuting Geography.
43
Figure 9 Student transportation modal split.
Figure 10 Faculty/staff transportation modal split.
Drive Alone 29%
Carpool 11%
Bus 24%
Motorcycle 1%
Bike 5%
Walk 30%
Students
Drive Alone 77%
Carpool 10%
Bus 3%
Motorcycle 1%
Bike 2%
Walk 7%
Faculty/Staff
44
4.4 Driving Distance and Bus Travel Distance
Annual estimations of driving distances and bus travel distances were calculated for the
full UNT population per the methods outlined in Chapter 3. The UNT community drives more
than 89 million miles per year commuting to campus, and rides a bus 8.9 million miles per year
(see Tables 9 and 10). Students generate almost 84% of those driving miles, making, on
average, a 26.1‐mile round‐trip to campus. The average round‐trip distance for faculty and
staff is 21.7 miles. Students also dominate use of the buses, accounting for 97.5% of the trips
taken each week.
Table 9 Driving distances and trips/week summary.
Students Faculty/Staff All Weighted Driving Trips/Week
73.7% 26.3% 24,000 trips/wk
Total Weighted Round Trip Driving Distance/Year
83.9%
16.1%
89.6 million miles
Average Round Trip Driving Distance/Trip
26.1 miles
21.7 miles
Table 10 Bus travel distance and trips/week summary.
Students Faculty/Staff All UNT Shuttle Trips/Wk 98.5% 1.5% 9,590 trips/wk DCTA Connect Trips/Wk 96.8% 3.2% 1,853 trips/wk DCTA Commuter Trips/Wk 94.8% 5.2% 690 trips/wk Total Bus Travel Distance/Year 97.5% 2.5%
8.9 million miles
45
4.5 Commuting Geography Analysis
To determine where clusters of high‐impact commuting activity are occurring, maps
were generated using Geographic Information System (GIS) software. The first two maps will
serve as a geographical reference for much of the subsequent commuting discussion. The first
map, north Texas, depicts the Dallas Fort Worth metropolitan region surrounding the UNT
campus and the city of Denton (see Figure 11).
Figure 11 North Texas map (Rand McNally 2010).
46
The second map, Denton County zip codes, diagrams the specific zip code areas in the
county and areas immediately adjacent (see Figure 12). Denton city zip codes 76201 through
76206 are defined as the “primary core” areas surrounding the UNT campus.8 Beyond the
primary core, residences located in the rest of the city of Denton are defined as the “secondary
core”. These include zip codes 76207, 76208, 76209 and 76210. Moving further outward from
the campus, a third ring of surrounding locations is defined as the “inner periphery,” which
includes all of the remaining zip code areas in Denton County, as well as the Keller area (zip
76248) in northern Tarrant County which has a significant commuter population. The final area
is defined as the “outer periphery” and includes all residences outside of Denton County.
8 76201 and 76205 are the only zip codes with physical areas. Zip codes 76202, 76203, 76204, and 76206 are post office boxes located in downtown Denton.
47
Figure 12 Denton County zip code locations.
48
4.5.1 Students
Students responding to the survey are commuting from all over the north Texas area.
They reported local residences in 269 different zip codes, although 49% of them live in the
Denton primary core. An additional 13% of students live in the Denton secondary core. The
student home zip code locations map illustrates these reported home residences (see Figure
13). Although not visible on this map, UNT students live as far away as Texarkana and Temple,
TX. Most students, however, live in the counties surrounding Denton and the Metroplex.
Furthermore, the map emphasizes the fact that 62% of students live in the primary and
secondary core areas of Denton.
49
Figure 13 Student home zip code locations.
50
The next two maps illustrate student weighted driving trips/week (drive alone trips plus
½ carpool trips) activity.9 The student weighted driving trips map (Figure 14) indicates, as
expected, driving trips come from all over north Texas, although most trips come from Denton
County. Figure 15 provides a closer look at Denton County driving trips, and indicates there is a
substantial volume of trips being made from the Denton primary and secondary core areas. In
fact, zip codes in the primary core generate 42% of the total driving trips made per week. This
large proportion of driving trips is a concern because many of the areas are within walking or
bicycling distance of campus, or are served by bus routes.
Further examination of the driving trip data was accomplished by controlling for the
number of students living in each of the zip code areas. This step was taken to determine if the
dense concentration of driving trips seen in the primary and secondary core areas was a
function of many students living in those areas, or rather an indication of excessive driving. The
next map, student driving trips per person, illustrates the results of this calculation and displays
the data for Denton and surrounding counties (see Figure 16). In the Denton primary core, the
map specifically shows the Denton central city zip code (76201) is contributing 2 or 3 driving
trips per person per week, and Denton city zip 76205 is contributing 3 to 4 trips per person per
week. So the large proportion of driving trips per week appears to be somewhat a function of
the concentration of students living in the primary core. However, this volume of commuting
by car still presents a potentially significant target for reduction of commuting miles. Actions
could be taken to transform these driving trips into bus, walking or bicycling trips.
9 Data for students reporting more than 20 drive alone trips/week was reclassified to “missing”, as the validity of that student‐reported data was suspect.
51
Figure 14 Student weighted driving trips.
52
Figure 15 Student weighted driving trips‐‐Denton County.
53
Figure 16 Student driving trips per person.
54
In addition, the survey data reveal 631 students live on campus (zip code 76203), and
those individuals reported making 1561 driving trips per week (9% of total trips). This
represents an average of 2.5 trips per week per person. Some of this driving may be due to off
campus employment. Forty‐six percent (289) of the students living on campus reported that
they work, but only 17% (106) report working off campus. So working off campus cannot be
the only reason they are making driving trips to campus. Much of the driving is probably due to
going to a friend or family member’s home or a shopping destination.
The goal of reducing UNT’s commuting mileage requires critical examination of the
travel distances that are most contributing to the estimated student total of 75 million miles
per year. The next two maps plot student weekly driving distance (one‐way) by zip code area.
The student driving distance map (Figure 17) shows most commuting miles are generated from
zip codes in the Denton primary and secondary core areas, and in the county inner periphery.
Western Collin County and northwest Dallas County areas in the outer periphery also
contribute significant driving mileage. These large travel distances per week are a function of
many students making numerous trips to campus. Students living in the outer periphery areas
of Cooke, Ellis and Parker Counties also generate some substantial travel mileage. However,
these larger driving distances are a function of the longer travel distance from the UNT campus,
rather than the number of commuters living there.
55
Figure 17 Student driving distance.
56
Figure 18 provides a closer examination of student driving distances for the Denton core
and inner periphery areas. The zip codes generating the most commuting miles per week are
76210 (southern part of the city of Denton), 75067 (Lewisville), and 76248 (Keller). The mileage
from just these three areas is more than 10% of students’ weekly driving distance. The
remaining zip code areas in the Denton core and inner periphery contribute another 20% of
student weekly driving distance, for a total of 30% of total student driving mileage. Table 11
displays the breakdown of driving mileage by the primary and secondary core areas, and the
inner and outer periphery. As seen in the table, 70% of student driving mileage is generated
from the outer periphery. This high impact on (GHG) emissions results from a few students
commuting a long distance to campus.
Table 11 Student driving distance by area.
Originating Location Driving Miles/Week (One‐Way)
% Total Weekly Distance
Primary Core10 4943 2.1%
Secondary Core 14, 203 6.2%
Inner Periphery 50,156 21.7%
Sum 3 Above 69,302 30.0%
Outer Periphery 161,409 70.0%
10 This distance number is slightly underestimated in the calculations due to the defined campus destination of zip 76201 vs. 76203. Recall that zip 76203 has no physical location, and therefore could not be used as the destination zip code area.
57
Figure 18 Student driving distance‐‐Denton County.
58
Students driving to campus from the Denton primary core (zips 76201 through 76206)
and part of the secondary core (zip 76209) do have bus service, although the service may not
be convenient in the timing or the transfers required to get to campus. In addition, the DCTA
Commuter bus park‐and‐ride option is available to students travelling from at least eight of the
higher mileage periphery zip code areas along the I‐35E corridor (Lewisville 75057, 75067,
75077; Carrollton 75006, 75007, 75010; The Colony 75056; and Flower Mound 75028), which
account for 13.5% of the student driving mileage. For the remaining zip code areas in the inner
and outer periphery, no nearby public transportation option is available to get to campus.
Carpooling is currently the only alternative to driving alone for these students.
The student maps depicting driving trips and driving distance in Denton County provide
visual clues to clusters of high‐impact student commuting activities, although the distance maps
discount the contributions of Denton’s central city area (zip 76201) (see Figures 15 and 18).
Whether these visually apparent clusters are statistically significant hot spots of commuting
activity was determined by conducting a Getis‐Ord Gi* hot spot analysis of the weighted driving
trips feature. The student driving trips hot spot analysis map illustrates the results of this test
and indicates statistically significant clusters are present (see Figure 19). All of the Denton
primary and secondary core areas (zip codes 76201 through 76210) exhibited Z‐scores greater
than 2.58 standard deviations (p < .01), as did one area in Lewisville (75067). This result means
we are 99% confident that these patterns are statistically significant, and it is very unlikely that
the observed cluster pattern is random. Aubrey (76227) and Keller (76248) also exhibit Z‐scores
that indicate some clustering has occurred, but at a less significant level (.05 < p < 0.1).
59
Figure 19 Student driving trips hot spot analysis.
60
These statistical results confirm the same general conclusions as derived from visually
examining the student driving trips and driving distance maps. The hot spot analysis presented
a distinct spatial pattern of significant student driving activity and strengthens the argument to
focus driving trip reduction efforts on the Denton primary and secondary core areas, as well as
the Lewisville area in the inner periphery.
Towards that goal, an opportunity may exist to implement further UNT Shuttle Bus
services to apartment complexes in four of the previously discussed high mileage areas in the
Denton core (zip codes 76205, 76208, 76209 and 76210). These four areas have significant
numbers of apartment dwellers (46 – 80%‐‐see Table 12). Shuttle bus service to the apartment
communities in these areas could give students a low cost alternative to driving and be a
feasible next step for the university. Transforming the portion of driving trips coming from
these apartment residents into bus trips could possibly reduce total student driving trips by
14%, which represents 4% of student total driving distance. Transforming all driving trips from
these four Denton primary and secondary core areas into bus trips could reduce student driving
distance by 7%.
Table 12 Apartment dwellers in high mileage zip codes.
Zip Code
% Students Living in
Apartments
# Driving Trips/Week
Potential # Trips
Reduced
Potential Apt. 1‐Way Driving Distance
Reduction
Potential Total 1‐Way Driving
Distance Reduction
76205 80 1422 1138 2506 3132 76208 59 498 294 1796 3044 76209 46 1016 467 1137 2472 76210 48 1090 523 3637 7577 Total 2422 (14%) 9076 (4%) 16,225 (7%)
61
Data for student carpool trips were also mapped by trip origin. However, because the
patterns shown on the carpool map generally mimic those shown on the student weighted
driving trips, the carpool map is not included in this report. Some of this replication is to be
expected since carpooling trips are a factor in the weighted driving trips calculation. The
important point is that some carpooling is occurring, but not at a significant proportion of total
driving trips. In fact, commuting by carpool usually accounts for less than 10% of the trips
shown on the driving trips map. Encouraging a larger proportion of students to carpool to
campus would be an opportunity to reduce UNT’s greenhouse gas emissions.
A supplementary map, student driving trips per person—north Texas, is a depiction of
driving trips for the entire north Texas region controlled by the number of students reported
living in those areas (see Appendix D). On this wider view of north Texas, some outliers of
commuting activity are coming from Alvord (Wise County west of Denton) and Royce City (east
of Dallas County). These zip code areas represent individuals who reported driving to campus
more than 12 times per week. This type of behavior seems unlikely and may represent a
misunderstanding of the survey question. Although not shown on this map, some “extreme
commuting” is also occurring from two zip codes in Temple, approximately 174 miles south of
Denton. Extreme commuters choose to spend more than 2 and one‐half hours travelling to and
from work (or school) rather than relocate, usually for family reasons (Levinson 2008). One of
three students travelling from Temple reported he/she is driving alone to campus 10 times per
week, a highly unlikely scenario considering the distance from campus. This data may again
reflect a situation in which the student answered the survey question incorrectly. These outlier
62
examples have been included in the discussion to highlight points of confusion that may need
clarification in future surveys.
In addition to the maps depicting the geography of student driving behavior, maps were
also generated for the reported number of student trips per week made by bicycling and
walking. However, those maps were not particularly useful as they reflect walkers and bikers
with home zip code locations all over the north Texas metropolitan area. It is likely that these
maps reflect the mixed transportation modes being used to arrive at the student’s campus
destination (i.e. drive to a remote parking lot, then walk or bike further into campus), rather
than commuting that is solely walking or biking from one’s home.
And finally, zip code maps were generated to depict the three bus transit modes (UNT
Shuttle, DCTA Connect and DCTA Commuter buses). These maps reflect the same mixed mode
usage as found on the walk and bike maps, but also indicate that students are often confused
as to which bus they are actually boarding. An example of this confusion is that students living
on campus reported taking more than 70 trips per week to campus on the DCTA Commuter
bus, which runs from downtown Dallas to the campus via Interstate 35E. Future transportation
surveys will need to be structured to distinguish the primary mode of commuting to campus
from any secondary means of getting around campus, and to better distinguish between the
three bus services.
The analysis of student commuting geography has shown the Denton primary and
secondary core areas, as well as two zip code areas in the inner periphery (Keller and
Lewisville), are generating the most impact on student emissions. UNT should strongly consider
efforts to shift these student‐driving trips in the primary and secondary core to walking, biking
63
or bus transit trips. An opportunity also exists to shift more driving trips from the I‐35E corridor
(13.5% of driving mileage from eight zip code areas) to the DCTA Commuter Express bus, and
develop a park and ride bus to service the I‐35W corridor (9% of mileage from 5 zip code areas).
The potential driving mileage reductions from these 2 corridors (22%), combined with shifting
all driving from the primary and secondary core areas (8%), could result in a maximum
achievable mileage reduction of 30%. The remaining 70% of driving mileage, and the
associated greenhouse gases emitted, is an unfortunate result of a larger commuter student
population.
4.5.2 Faculty/Staff
Faculty and staff responding to the survey are commuting to campus from a tighter
geographical area than was seen with the students. In fact, they travel from 120 different zip
codes in the north Texas area, even though a slightly smaller proportion (57%) of them live in
the Denton primary and secondary core areas (76201 through 76210) compared to the
surveyed students (62%). The faculty/staff home zip code locations map shows that UNT
employees live predominantly in the Denton core and inner periphery areas (see Figure 20).
Although not shown on this map, three of the sampled faculty/staff respondents reported they
live as far away as Bryan, Round Rock and Liberty Hill Texas, all located considerably south of
the metroplex.
The faculty/staff weighted driving trips map illustrates driving activity by zip code for the
counties surrounding Denton (see Figure 21). This map shows employee‐driving trips come
predominantly from the Denton primary and secondary core areas. Four specific Denton zip
codes (76201, 76205, 76209 and 76210) in the primary and secondary core areas exhibit the
64
Figure 20 Faculty/staff home zip code locations.
65
Figure 21 Faculty/staff weighted driving trips.
66
largest concentration of driving trips per week, accounting for 47% of all faculty/staff trips.
Within the inner periphery, a substantial volume of faculty/staff driving also comes from those
areas north of Highway 380.
Figure 22, the faculty/staff driving trips per person map, depicts the driving trip data
controlled by the number of employees living in each zip code. This map shows that employees
living in most areas in the Denton secondary core and periphery make, as expected, 4 to 6 trips
to campus per week (one‐way). However, the primary core zip code 76201 indicates slightly
less driving activity is occurring per person, with 3.5 trips per person. Also on this map, outliers
of excessive commuting trips are seen from southwestern Collin County (Plano) and two areas
west of Fort Worth. These individuals reported making seven to ten trips per week to campus.
Although faculty and staff commuting only contribute 4% of the total greenhouse gas
emissions generated by the university, one may think it insignificant compared to the 25% of
total emissions generated by student commuting. However, on a per person basis, employee‐
commuting emissions were determined to be 60% higher than student emissions in UNT’s
greenhouse gas inventory (see Table 13).
Table 13 Commuting emissions.
% Of Total Campus GHG Emissions
Emissions/Person MT eCO2
Faculty/Staff Commuting
4% 1.83
Student Commuting 25% 1.12 TOTAL 29%
67
Figure 22 Faculty/staff driving trips per person,
68
For this reason, further examination of the travel distances most contributing to the
employee emissions is warranted. The next map, faculty/staff driving distance, depicts one‐
way driving distances to campus (see Figure 23). This map indicates the bulk of the employee
driving mileage comes from the Denton core and inner periphery areas. Although not shown
on this map, an outlier of extreme commuting is coming from Bryan, TX. This individual
reported driving to campus 5 times per week for a total of 1010 miles per week (one way). The
map also depicts the highest concentration of driving distance originates from zip code 76210
in the Denton secondary core, comprising 8.7% (5898 miles) of the total weekly employee
commuting distance. The next most intense activity originates from six zip code areas mostly in
the county inner periphery, contributing an additional 21.6% (14,669 miles) of the weekly
mileage. One of these six areas (76208) is in a Denton secondary core area not currently served
by public transit, as are the three areas in Krum (76249), Aubrey (76227) and Sanger (76266) in
northern Denton County. Carpooling is the only alternative to driving alone for these
employees. However, Lewisville (zip 75067) and southern Denton (zip 76210) areas are
partially served by public transit in that the DCTA Commuter bus line provides park and ride
stops along Interstate 35E. DCTA Connect buses also serve portions of Denton zip code area
76209.
69
Figure 23 Faculty/staff driving distance.
70
The faculty/staff weighted driving trips and distance maps have provided visual clues to
clusters of high‐impact employee commuting activities, although the distance map again
discounts the contributions of Denton’s central city zip 76201. To determine whether these
visually apparent clusters are statistically significant hot spots of commuting, a Getis‐Ord Gi*
hot spot analysis was performed on the weighted driving trips feature. The faculty/staff driving
trip hot spot analysis map depicts the results of this test and indicates statistically significant
clusters are present (see Figure 24). All of the Denton primary and secondary core areas (zips
76201 through 76210) exhibited Z‐scores greater than 2.58 standard deviations (p < .01). The
communities lying along major highways leading into Denton also exhibited highly significant
hot spots of activity (Aubrey 76227, Sanger 76266, Krum 76249, Argyle 76226, and Highland
Village/Lewisville 75077). The results indicate we are 99% confident that the patterns seen are
statistically significant clusters, and it is very unlikely that the observed pattern is random.
Ponder (76259), Little Elm (75068) and Lewisville (75067) exhibit Z‐scores that indicate some
clustering has occurred, but at a less significant level (.01 < p < .05). And, the Keller (76248) hot
spot is somewhat less significant (.05 < p < 0.1). This statistical analysis confirms the general
conclusions drawn from visual examination of faculty/staff driving trip and distance maps. The
Getis‐Ord Gi* hot spot analysis presented a distinct spatial pattern of significant driving activity
and strengthens the argument to focus faculty/staff driving trip reduction efforts first on the
Denton primary and secondary core areas, and then on the inner peripheral communities. In
comparison to the student hot spot analysis, the faculty/staff hot spots display a wider
geographical spread (see Figures 19 and 24), extending into the inner periphery. However,
both populations generate significant driving activity from the Denton
71
Figure 24 Faculty/staff hot spot analysis.
72
primary and secondary cores, and from the Lewisville and Keller areas in the periphery. So,
efforts to shift driving trips to bus transit trips in the primary and secondary core areas could
positively impact both student‐ and faculty/staff‐generated emissions. Likewise, promoting bus
transit, park and ride or vanpool options along the I‐35E and I‐35W corridor for students could
also help faculty/staff shift some driving trips to bus trips.
Because bus transit is not available in many of the Denton inner periphery communities,
carpooling is often the most viable option for reducing faculty/staff commuting mileage.
Employees currently use carpooling, but it only represents about 11% of total driving trips. A
map depicting total carpool trips per week was plotted, but it exhibited activity patterns similar
to previous maps. Zip code areas representing the city of Denton show the most carpooling
activity. Two other areas north of Denton, Aubrey and Sanger, also contribute a share of
carpool trips. So, as with the student commuting geography, carpooling is occurring from high
commuting activity areas, but not at a significant rate. Encouraging a larger proportion of
faculty and staff to carpool to campus would be an opportunity to reduce their driving
distances and resulting per capita greenhouse gas emissions.
As with students, maps were generated to depict faculty/staff reported trips per week
made by bicycling and walking. However, the walking map was not particularly useful as it
reflected walkers with home zip codes from geographically impossible points of origins. This
was the same phenomena observed with students, and most likely reflects the mixed
transportation modes being used to arrive at a campus destination (driving and walking). The
Faculty/Staff Bike Trips map depicts a more reasonable set of data (see Appendix D).
Faculty/staff are making a limited number of weekly trips to campus by bicycle from the Denton
73
primary and secondary core areas. Two outliers of biking activity shown on the map could be
errors in reporting, or reflect mixed mode activities of driving to a remote parking area on
campus, and then bicycling to the employee’s campus destination.
Finally, maps for faculty/staff were generated to depict the three bus transit modes.
The UNT Shuttle bus trips map reflects the same mixed mode usage as found on the walk map,
and on the analogous student map. Unlike the students, there is little indication that faculty
and staff personnel are confused as to which of the three buses they are boarding. The DCTA
Commuter bus route, which runs along I‐35E, appears to be used by a limited number of
commuters from as far away as downtown Dallas (see Appendix D). Most of these commuters
appear to board the bus in Dallas or at the Carrollton and Lewisville park‐and‐ride lots. The
Dallas boarders may be UNT System Center employees who have since had their offices
relocated. The park‐and‐ride lots offered by the Commuter Bus offer an unrealized opportunity
for UNT employees living near the Interstate 35E corridor to reduce their commuting emissions
from driving.
So, although faculty and staff commuting only comprise a minor portion of UNT’s total
emissions, many of the efforts to shift student driving trips to transit trips could also be directed
at faculty and staff. Individuals living in the Denton primary and secondary cores should be
targeted first, then commuters coming from the Lewisville and Keller areas in the inner
periphery. Actions should also be taken to encourage more carpooling among faculty and staff
commuting from the inner periphery areas north of Highway 380.
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4.6 Demographic and Socioeconomic Analysis
4.6.1 Students
Given that the drive alone travel mode generates the bulk of student commuting
emissions, it is important to examine any factors that could be influencing this behavior. To
that end, Pearson’s Chi‐square analyses were conducted to test for a relationship between
demographic, socioeconomic and academic factors and the drive alone commuting mode
choice (see Table 14). These tests were conducted to determine what the most important
contributing factors were to a student’s decision to drive alone. As shown in the table’s data,
the Chi‐square tests suggest a statistically significant relationship (p < .01) between the
students’ drive alone commuting choice and eight of the nine factors. The null hypothesis that
no relationship exists was rejected for these eight factors. There was no relationship between
the gender factor and driving alone. Because the calculated Chi‐square values were quite large
for the eight significant factors, we are certain a relationship exists. To further examine the
strength of the association between the factors, the Cramer’s V statistic was calculated. This
measure can range from 0 to 1, and is similar to a correlation coefficient (Field 2005). Only one
factor, having a parking permit, had even a medium association with the decision to drive
alone. Therefore, although there was a statistically significant relationship between driving
alone and 8 of the 9 factors, the results of the chi‐square analyses are not robust. The Cramer’s
V test confirms this conclusion, indicating no factor except having a parking permit had even a
moderate influence on drive alone behavior. These seemingly conflicting results are not
uncommon with large sample sizes. Iverson (1979, 116) explains this in Statistics for Sociology,
“with large samples chi‐squares tend to be large because there is so much evidence in the data
75
to suggest that relationships really exist in the population.” Therefore, we are more interested
in the strength of the relationship indicated by the Cramer’s V than the size of the calculated
chi‐square. Another measure of the size of the parking permit effect is the odds ratio, which
indicates students with a parking permit were 5.18 times more likely to drive alone to campus
than those without a parking permit (Field 2005).
So to summarize, the only demographic, socioeconomic or academic factor that has a
substantive influence on student driving alone behavior is having a parking permit. The current
student parking permit system that allows unlimited use of commuter parking for a relatively
small annual fee actually encourages driving trips to campus. Previous speculation that gender
may influence a student’s decision to drive alone was shown to be false. The remaining factors
did not exhibit a strong influence on drive alone behavior.
Table 14 Chi‐square analysis of student factors.
Factor Calculated Chi‐square
Significance Significant
Relationship? Cramer’s
V Strong
Relationship? Gender .624 .732 No ‐‐ ‐‐ Age Group 210 .000 Yes .130 No Employed FT/PT/Not
124 .000 Yes .100 No
Employed On/Off Campus
97.8 .000 Yes .151 No
Income 169 .000 Yes .138 No Housing 428 .000 Yes .185 No Class Standing 200 .000 Yes .127 No Credit Hour Enrollment
65.9 .000 Yes .074 No
Have Parking Permit
725 .000 Yes .343 Medium
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4.6.2 Faculty/Staff
To address a similar question for the faculty and staff, Pearson’s chi‐square analyses
were conducted to test for relationships between the demographic and socioeconomic
variables and the drive alone commuting mode choice (see Table 15). As shown in the table’s
data, there is a statistically significant relationship (p < .01) between the faculty/staff’s drive
alone commuting choice and all factors except “having children under 18 living in the
household”. To examine the strength of the association between the factor and the drive alone
variable, the Cramer’s V statistic was calculated. As with the student data, only one factor,
having a parking permit, had even a medium association with the decision to drive alone.
Therefore, although there was a statistically significant relationship between driving alone and
6 of the 7 variables, the results of the chi‐square analyses are not robust. As confirmed by the
Cramer’s V test, no variable except having a parking permit had even a moderate influence on
that decision. Again, the large sample size of the dataset is most likely influencing the chi‐
square statistic rather than demonstrating a true cause and effect situation. Calculating an
odds ratio to further characterize the size of this effect for faculty and staff indicates employees
with a parking permit were 7.3 times more likely to drive alone to campus than those without a
parking permit.
77
Table 15 Chi‐square analysis of faculty/staff factors.
Factor Calculated Chi‐Square
Significance Significant Relationship?
Cramer’s V Strong Relationship?
Gender 16.8 .000 Yes .110 No Age Group 39.4 .001 Yes .118 No Have Children < 18 in Household
8.54 .014 No ‐‐‐ ‐‐‐
Income 35.5 .003 Yes .114 No Housing 35.9 .000 Yes .113 No Classification (Faculty or Staff)
20.3 .000 Yes .085 No
Have Parking Permit
134 .000 Yes .310 Medium
One final observation about these faculty/staff analyses is the only variable that showed
no statistically significant effect (at p < .01) on the drive alone decision was having children
under 18 in the household. This is contrary to what was expected. It was thought that having
children might motivate an employee to drive alone rather than carpool or take a bus to
accommodate the delivery of children to school or day care. But this factor was not as
important as speculated, or its influence was overwhelmed by the fact that relatively few
employees commute to campus by any means other than driving alone (77% of trips).
In summary, the UNT community travels over 89 million miles per year, 84% of which
comes from student commuting. Although many bus routes have been added to the local
transit system in recent years, 59% of student driving trips are still originating in the Denton
primary and secondary core areas, which are largely served by transit. And, although
faculty/staff commuting does not have a significant of an impact on overall emissions, 77% of
employees are driving alone to campus, leading to a higher emissions per capita contribution to
UNT’s environmental impact. Finally, none of the demographic or socioeconomic factors were
78
particularly good predictors of the drive alone behavior. Rather, the only variable having any
influence on commuting behavior for both students and faculty/staff was having a parking
permit.
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CHAPTER 5
CONCLUSION
5.1 Policy Discussion
Commuting to campus was found to be a significant source of UNT’s greenhouse gas
emissions. Student commuting contributes 25% of the total emissions, so actions to reduce
those driving miles should be one of the UNT community’s priorities. The transportation survey
collected a wealth of data regarding the travel patterns of students, faculty and staff, and the
geography of commuting activities have been characterized. In the past few years, the
university has taken steps to promote the use of bus service among students from apartment
communities in the city of Denton. Twenty‐four percent of all weekly student trips were
reportedly made by bus, roughly comparable to the fraction of driving alone trips (29%). In
addition, walking and bicycling comprise 35% of student trips, which are zero‐emission‐
producing transit modes. The university is on the right track with its implementation of the
UNT Shuttle buses, but efforts should be increased to further shift existing driving trips to
walking, bicycling and bus trips.
A significant number of weekly student driving trips (42%) are still occurring from the
Denton primary core area. Evidently, the convenience of driving to campus has not compelled
all students living in these central areas to walk, bike or take a bus. Other universities have
reduced commuting emissions by encouraging walking and bicycling. North Carolina State
University (NCSU) published a map diagramming the time needed to walk to different parts of
campus. UNT could publish a similar map illustrating walking times from key locations in the
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Denton community to the campus, or to bus stops that service the campus. NCSU also offers
“occasional commuter” parking benefits to individuals who usually travel by walking, bicycling
or using transit. UNT could service this segment of the student population by replacing some
permitted parking spaces at locations spread around campus with electronically metered
parking systems. An example of this system has been implemented at Rensselaer Polytechnic
Institute in Troy, New York. Their system utilizes an “eCard” that allows students to set up a
debit account with the university to pay for parking services as used, rather than paying for an
annual unlimited‐use pass.
UNT could further encourage low‐carbon commuting choices from the Denton city areas
by partnering with the city to provide bike lanes, allowing a well‐marked and safe means to ride
a bicycle to campus. These efforts should start with marking dedicated bike lanes around
campus with eye‐catching paint and signage. Excellent examples of this can be found in the city
of Portland, Oregon, where the signage is easily understood to both cyclists and automobile
drivers. After implementing the bike lanes on campus, the university could work with the city
of Denton to extend the concepts and signage to surrounding city streets. This effort could
start with some key streets around campus (Oak, Hickory, Eagle, Welch, and Bonnie Brae
Streets, for example), and then be extended to other key routes to campus from areas farther
away.
Beyond locations within walking or biking distance of campus, the university should
consider expanding UNT Shuttle bus services to more areas within the Denton primary and
secondary core. A good first step may be to target more apartment communities within these
areas (e.g. zip codes 76205, 76208, 76209, 76210). As discussed in Chapter 4, student driving
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trips could be reduced 14% by shifting reported driving trips to bus trips from the apartment
dwellers in these zip codes, although the resulting total driving mileage reductions are less
significant (4%), due to the apartments’ close proximity to campus. Adding a park and ride stop
in a large retail lot in those areas might also encourage the shift away from driving alone. A
potential example of a spacious lot is the Kroger shopping center located on Teasley Lane in
south Denton. This park and ride lot may be a convenient stop for employees or students who
want to stop for groceries, pizza or at the dry cleaner on the way home.
Beyond the city of Denton, more effort should be directed to encouraging driving
commuters from the I‐35E corridor to take the DCTA Commuter bus. These efforts could
target students now driving from Lewisville, Highland Village, Flower Mound, Carrollton, and
The Colony. The current DCTA Commuter bus line has a park and ride lot in Lewisville/Highland
Village at FM407, in Lewisville near the Vista Ridge Mall, and in northern Carrollton at DART ’s
transit center. Marketing the advantages of the Commuter bus line as a low cost (transit fees
are part of the fees all students pay), trouble free means to get to campus, without the cost or
difficulty of parking should be pursued with a wider audience of students. These efforts can
then be transitioned to the A‐Train, the DCTA transit system’s commuter rail, planned to be in
operation to Denton in late 2011.
The other hot spot of student commuting activity is in Keller, in northern Tarrant
County. A significant amount of driving mileage is also generated from the neighboring
community of Roanoke. The university should consider implementing a park and ride service or
shuttle bus from a retail lot along Interstate 35W near the Denton County line. This service
could mimic that provided by the DCTA Commuter Express along Interstate 35E, providing bus
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transit services to students in the high driving mileage areas of Roanoke, Keller, Fort Worth, and
Bedford, as well as the whole Tarrant County area.
In the outer periphery areas, which generate 70% of the student driving mileage but
have low resident densities, carpooling is currently the only alternative to driving alone. UNT
does offer a service to facilitate carpooling through the UNT Transit website and a link to the
AlterNetRides service. However, student survey respondents indicated that only 11% of the
weekly trips to campus were made by carpooling. UNT should consider marketing this service
more to students as a means to reduce their commuting and parking expenses.
Although it is a sensitive topic, the university’s current student parking permit system
probably encourages driving to campus rather than walking, riding a bike or taking a bus. As
discussed in Chapter 4, having a parking permit was the only socioeconomic or demographic
factor that had any significant influence on students’ decision to drive alone to campus. The
annual cost of a student permit ($115‐180) is apparently not severe enough to discourage
driving, although parking availability is becoming an issue. Additional non‐permitted parking
options currently include metered campus spaces, and limited, but free parking on city streets
surrounding the campus. Inexpensive driving and parking expenses encourage students to
drive alone. Students are much more likely to drive alone to campus, even driving multiple
times per day, if they have a parking permit. UNT has no control over vehicle operating
expenses such as the price of gasoline, nor the availability of off‐campus parking, but it can
control the cost of parking on campus. The university may want to consider slowly transitioning
some commuter student parking from the annual permit to the previously discussed
electronically metered system. Although it may seem a draconian approach, raising the cost of
83
an annual parking permit, or transitioning to a “pay‐as‐you‐park” metered system would likely
discourage excess driving trips. However, any effort to make campus parking more expensive
should only come after earlier efforts to improve the availability of better walking and bicycling
routes, offer more opportunities to use park and ride and bus transit systems, and promote
more carpooling among students.
Addressing the smaller group of students that live on campus who reported making an
average of 2.5 weekly trips to campus will also be a sensitive issue. Again, resident parking is
inexpensive and conveniently located near on‐campus housing. Those factors may encourage
students to rely on a personal vehicle to take more trips off‐campus for shopping or
entertainment in the north Texas area, rather than taking a bus to a local venue. Relocating the
majority of resident parking to a more remote location (e.g. the Research Park), serviced by the
UNT Shuttle buses, may persuade students to use the bus system for local trips. Some resident
parking could be left near the dormitories but switched to electronically metered spaces rather
than unlimited use permitted spaces. The relocation of resident parking would then free
valuable campus space for additional academic facilities.
Greenhouse gas emissions from faculty and staff commuting, although higher on a per
capita basis than students, contribute only 4% to the campus total. From that overall
perspective, one would logically spend little effort trying to reduce this driving. However, the
actions of the UNT administration, as well as staff and faculty, set the tone for how serious the
university is about reducing greenhouse gas emissions. If no initiatives are taken to reduce
employee emissions, students will be less inclined to take the problem seriously. Therefore, it
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is important that UNT employees also take action to reduce their commuting emissions. Survey
respondents indicated that more than three‐fourths of the population drives alone to campus.
Initially, more UNT employees could be encouraged to use the bus as an alternative means of
getting to work. Because many of the weekly driving trips to campus do originate in the Denton
primary and secondary core, bus service is available to many employees. UNT’s Sustainability
Council could survey the faculty and staff population to see how many individuals currently
have access to bus service within a short walk of their homes, and ask questions regarding what
would motivate them to use the bus rather than drive. Perhaps offering employees reduced
cost bus passes would be a helpful incentive to transform driving trips into bus trips. An effort
to inform employees about the currently available UNT Shuttle, and DCTA Connect and
Commuter bus routes may also be warranted. Employees may not be fully aware of the
existing bus services, or the park and ride opportunities offered by the DCTA Commuter route.
As discussed in Chapter 4, the hot spot of faculty/staff driving mileage comes from zip
code area 76210 in southern Denton. This area is served by a park and ride station along
Interstate 35E at Wind River Lane. As an alternative, if a UNT shuttle bus route is implemented
for students from a retail lot in 76210, faculty and staff could be encouraged to use that service
too. And similarly, if UNT expands shuttle bus services to more areas of the city of Denton to
accommodate students (by establishing park and ride stops in select retail lots), employees can
be encouraged to use the service rather than drive.
Carpooling to work should also be promoted among faculty and staff beyond the
current 11% rate. Other universities have encouraged this practice by providing preferential
parking or discounted permits for carpoolers. Another option to reduce emissions could
85
include incentivizing UNT employees to commute to campus in a high mileage or hybrid electric
vehicle by issuing lower cost permits to individuals registering those types of vehicle. Or, an
employee who drives a hybrid or low emission vehicle could be provided with a special green
permit to park in a preferred location as has been done at Ohio State University.
A final option to reduce the greenhouse gas emissions generated by UNT employee
commuting would be to give faculty and staff the flexibility to work a four‐day workweek or
work from home one day per week. This may not be feasible for all employees, but many staff
and faculty jobs could be restructured to a four‐day week. Alternatively, the work could be
accomplished by telecommuting one day per week. The potential commuting emissions
reduction from this initiative alone is significant, allowing a 20% reduction in driving trips to
campus.
5.2 Further Research Recommendations
As UNT develops its climate action plan and implements changes to reduce greenhouse
gas emissions from commuting, consideration should be given to continuing the practice of
conducting transportation surveys on a regular basis. Rather than surveying both employees
and students simultaneously though, it may be more effective to administer the surveys on
alternate years. Data regarding the commuting habits of each population can then be assessed
biennially, and more time will have elapsed to judge the results of corrective actions taken.
Surveys can be more detailed and better adapted to determining the needs of each population,
and questions structured to minimize misunderstanding on the part of the target group.
Questions can also be designed to explore the feasibility of potential actions to reduce the 70%
of student driving distance generated from the outer periphery. This will be a difficult problem
86
to address, as the emissions resulting from this driving are generated by students living in
locations scattered around the metroplex. However, further development of online classes and
satellite class locations (e.g. Collin College) could be pursued. Closer to campus, actions taken
to shift student‐driving trips to walking, bicycling or transit trips should also be considered with
faculty and staff needs in mind. For example, employees can be queried to determine how
corrective actions taken to expand student bus transit options could be applied to reduce
faculty and staff driving trips.
UNT needs to work closely with DCTA for the future transition from the Commuter bus
line to the A‐Train light rail service. Unfortunately, the planned A‐Train route will terminate in
downtown Denton, not on the UNT campus as the Commuter bus route does now. This
inconvenience could be a major deterrent to getting I‐35E driving commuters to switch to rail
service. A bus rapid transit or other alternative needs to be developed to provide timely and
convenient transport from the downtown Denton rail station to the UNT campus.
Another recommendation for additional research involves further analysis of the
existing datasets from the transportation survey. These datasets are immense, and
supplemental information detailing the vehicle types, fuel choices, and models of cars driven,
as well as the specifics of types of parking used each day of the week are available. The data
may be useful to further characterize the commuting population’s vehicle types and parking
needs.
A final recommendation for further study is to use the UNT Center for Spatial Analysis
personnel and their GIS expertise to review existing bus routes and plan new routes. Detailed
street address data (without identifying names) could be pulled from existing UNT databases
87
for those student and faculty/staff members that list home locations in the primary and
secondary Denton core areas. Those homes could then be plotted on GIS maps with an overlay
of existing bus routes. The maps could be used to determine more effective routes or to
implement new bus routes, in cooperation with DCTA personnel. Graduate student resources
in the UNT Geography department could also conduct location allocation analysis of the UNT
population’s travel patterns to better define the geography of UNT’s transit needs.
5.3 Summary
The May 2009 greenhouse gas inventory demonstrated that commuting to campus was
the second highest source (29%) of UNT’s greenhouse gas emissions, following purchased
electricity (48%). This fact was expected, as the university has traditionally attracted a large
commuter population. Students, faculty and staff drive over 89 million miles per year travelling
back and forth to the Denton campus, 84% of which comes from students. Forty‐two percent
of student driving trips originate in the primary and secondary core areas surrounding Denton,
which are partially served by buses. However, because these core areas are in close proximity
to the campus, they only contribute 8% of the total student driving distance. Bus services
should be expanded to serve more students in these areas, but the resulting greenhouse gas
reductions may not be that significant. Beyond the Denton core areas, the inner periphery of
Denton County contributes another 22% of driving mileage. The DCTA Commuter Bus is a
current alternative to students in eight high mileage zip code areas along the I‐35E corridor.
Shifting these student driving trips to bus trips could reduce driving mileage by 13%. In
addition, if a similar commuter bus service could be provided from the Keller area in northern
Tarrant County, student driving mileage could be reduced another 9%. Other than the DCTA
88
Commuter Bus, no public transit is currently available to students travelling from other areas of
the inner periphery. Students living in the outer periphery (essentially outside Denton County)
contribute the remaining 70% of total driving distance, and carpooling is currently their only
alternative.
In conclusion, as the UNT community grows with increases in student and employee
populations, so will the university’s total annual emissions. This is a downside to growth, and
makes the need to reduce commuting emissions more important. However, because UNT has
such a large population of commuters travelling from all over the Dallas Fort Worth area,
reducing these emissions will be a formidable task. The large share of emissions generated by
student driving from outer periphery locations (i.e. outside Denton County) will be particularly
challenging, but a topic that nonetheless must be addressed. UNT’s forthcoming climate action
plan will undoubtedly address steps to reduce these commuting emissions, as the university
strives to reduce its total greenhouse gas emissions and fulfill its climate commitment.
89
APPENDIX A
SURVEYS
Reproduced with permission.
90
UNT FACULTY/STAFF TRANSPORTATION SURVEY
UNT is measuring the total carbon emissions we produce as a university community. This “Carbon Emissions Inventory” is comprehensive and will include emissions from transportation services, police, maintenance, the physical plant and other UNT operations. Faculty, staff and students are another source of emissions and UNT needs your help to calculate that measure.
As a token of appreciation for your help, all completed questionnaires will be entered in a random drawing for prizes . There will be drawings for prizes of…………….
The survey is being conducted by UNT’s Survey Research Center and will be maintained on a secure server. Your answers will be used in combination with all responses and will not be used to identify you individually. The survey will take 5 to 10 minutes to complete. If you have any questions you can call (565‐ )
Thanks for your help!
1. Consider the different ways that you travel to campus. For the travel methods listed below, indicate the total number of times you used that method in a typical week.
Transportation Method Number of times in a typical week
Walking Bicycling By motorcycle or scooter Riding the UNT shuttle bus Riding the DCTA “Connect” service Riding the DCTA Commuter bus (I‐35E route) Riding in a carpool (2 or more persons) Drove a carpool (2 or more persons) Drove Alone
2. When driving alone or in a carpool, what make of vehicle do you typically use? (drop menu with 43 makes) (select one)
3. Year: (drop menu 2009 back to 1991) (select one)
4. Type of vehicle: drop menu with 11 types (select one)
5. Size of vehicle if they selected pickup truck or SUV in q4d: small or large (select one)
6. Model: (open end for those who know)
7. What type of fuel does this vehicle use? GASOLINE DIESEL HYBRID/ELECTRIC
8. Do you have a UNT parking permit? YES NO
If YES, what type? A D G DP
9. If you drove to UNT last week, indicate the number of times you used the following types of parking each day:
91
Mon Tue Wed Thu Fri Sat Sun Permitted Lot/Space Parking Meter/Parking Garage On City Streets
10. Are you faculty or staff?
1. Faculty 2. Staff 3. Both
11. What is the zip code where you currently live? _______________
12. Do you live in a house, duplex, mobile home or an apartment? 1. House 2. Duplex 3. Mobile home 4. Apartment
13. Into which of the following age groups do you fall?
1. 18‐25 2. 26‐30 3. 31‐35 4. 36‐40 5. 41‐50 6. 51‐55 7. 56‐60 8. 61‐65 9. Over 65
14. Do you have children under 18 living in your household? 1. Yes 2. No 15. Which category did your total household income for the past year fall?
1. Less than $20,000 2. $20,000 ‐ $30,000 3. $30,001 ‐ 40,000 4. $40,001 ‐ 50,000 5. $50,001 ‐ 75,000 6. $75,001 ‐ 100,000 7. $100,001 ‐ $150,000 8. Over $150,000 9. NR/DK
16. Are you: 1. Male 2. Female
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UNT STUDENT TRANSPORTATION SURVEY
UNT is measuring the total carbon emissions we produce as a university community. This “Carbon Emissions Inventory” is comprehensive and will include emissions from transportation services, police, maintenance, the physical plant and other UNT operations. Faculty, staff and students are another source of emissions and UNT needs your help to calculate that measure.
As a token of appreciation for your help, all completed questionnaires will be entered in a random drawing for prizes . There will be drawings for prizes of…………….
The survey is being conducted by UNT’s Survey Research Center and will be maintained on a secure server. Your answers will be used in combination with all responses and will not be used to identify you individually. The survey will take 5 to 10 minutes to complete. If you have any questions you can call (565‐ ) Thanks for your help!
1. Consider the different ways that you travel to campus. For the travel methods listed below, indicate the total number of times you used that method in a typical week.
Transportation Method Number of times in a typical week
Walking Bicycling By motorcycle or scooter Riding the UNT shuttle bus Riding the DCTA “Connect” service Riding the DCTA Commuter bus (I‐35E route) Riding in a carpool (2 or more persons) Drove a carpool (2 or more persons) Drove Alone
2. When driving alone or in a carpool, what make of vehicle do you typically use? (drop menu with 43 makes) (select one)
3. Year: (drop menu 2009 back to 1991) (select one)
4. Type of vehicle: drop menu with 11 types (select one)
5. Size of vehicle if they selected pickup truck or SUV in q4d: small or large (select one)
6. Model: (open end for those who know)
7. What type of fuel does this vehicle use? GASOLINE DIESEL HYBRID/ELECTRIC
8. Do you have a UNT parking permit? YES NO
If YES, what type? P G R RP
9. If you drove to UNT last week, indicate the number of times you used the following types of parking each day:
Mon Tue Wed Thu Fri Sat Sun Permitted Lot/Space Parking Meter/Parking Garage On City Streets
93
10. What category best describes you?
a. Freshman b. Sophomore c. Junior d. Senior e. Grad student
11. How many credit hours are you currently enrolled in this semester? _________
12. What is the zip code where you currently live while attending classes? _______________
13. Do you live in a house, duplex, mobile home or an apartment? 5. House 6. Duplex 7. Mobile home 8. Apartment 9. Dorm
14. Into which of the following age groups do you fall?
1. Under 19 2. 19‐20 3. 21‐22 4. 23‐25 5. 26‐30 6. Over 30
15. In addition to school, are you employed full‐time or part‐time?
1. FULL‐TIME 2. PART‐TIME 3. NOT EMPLOYED (SKIP TO Q17)
16. Do you work 1. On campus 2. Off campus Select city: [openend text box]
17. Which category did your personal income for the past year fall?
1. LESS THAN $5,000 2. $5,001 ‐ 10,000 3. $10,001 ‐ 20,000 4. $20,001 ‐ 25,000 5. $25,001 ‐ 30,000 6. $30,001 ‐ 50,000 7. OVER $50,000 9. NR/DK
18. Are you: 1. Male 2. Female
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APPENDIX B
STUDENT DATA
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Student socioeconomic/demographic data.
Factor Category % Survey Respondents
% Survey Cumululative
Sum
% Population Data
Gender Female Male
62.1 37.9
56.0 44.0
Age Groups < 20 years 21 – 22 23 – 25 26 – 30 > 30
29.8 24.5 17.6 12.1 16.0
< 25 = 71.8
< 25 = 71.6 12.4 16.0
Housing Dorm Apartment House Other
18.2 40.5 38.4 2.9
Personal Income < $5000 $5001 – 10,000 $10,001 – 20,000 $20,001 – 25,000 $25,001 – 30,000 $30,000 – 50,000
27.0 21.0 19.1 6.2 5.1 10.6
Credit Hours Enrolled
0 – 6 hours 7 – 12 13 ‐ 18
17.6 38.9 43.5
Classification Freshman Sophomore Junior Senior Grad Student
11.4 14.5 24.1 29.2 20.8
10.9 15.8 21.4 30.5 21.3
Have a Parking Permit?
Yes No
45 55
Type of Permit G P R RP
21.0 45.7 28.0 5.3
96
Student employment.
Factor Category % Survey Respondents
Employment Fulltime Part‐time Not Employed
24.5 45.2 30.4
Employed On/Off Campus
On Campus Off Campus
30.3 69.7
Student commuting trips.
Student Factor Category % Respondents Comments
Drive Alone Trips 1 ‐ 2 3 ‐ 4 5 6‐10 >10
42.0 28.7 18.3 9.9 1.1
Average = 3.5 trips/person; 4273 Responses
Carpool Trips 1 ‐ 2 3 ‐ 4 5 6 – 10 11 ‐ 20
62.7 20.5 9.7 5.9 1.2
Average = 2.75 trips/person; 2168 Responses
Walking Trips 1 ‐ 2 3 ‐ 4 5 6 – 10 11 – 15 16 – 20
19.3 17.7 22.5 26.1 7.2 7.2
Average = 6.5 trips/person; 2434 Responses
Bicycling Trips 1 ‐ 2 3 ‐ 4 5 6 – 10 11 ‐ 20
41.9 21.1 15.3 16.1 5.5
Average = 4.2 trips/person; 620 Responses
97
Student bus trips.
Student Factor Category % Respondents Comments
UNT Shuttle Bus Trips 1 ‐ 2 3 ‐ 4 5 6 – 10 >10
30.7 23.3 17.2 21.6 2.3
Average = 5.0 trips/person; 1919 Responses
DCTA Connect Bus Trips
1 ‐ 2 3 ‐ 4 5 6 – 10 >10
43.4 24.3 13.1 12.9 6.3
Average = 4.2 trips/person; 444 Responses
DCTA Commuter Bus Trips
1 ‐ 2 3 ‐ 4 5 6 – 10 >10
61.9 20.9 7.5 8.3 1.2
Average = 2.8 trips/person; 239 Responses
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APPENDIX C
FACULTY/STAFF DATA
99
Faculty/staff socioeconomic/demographic data.
Factor Category % Survey Respondents
% Population Data
Gender Female Male
61.3 38.7
53.7 46.3
Age Groups 18‐25 26‐30 31‐35 36‐40 41‐50 51‐55 56‐60 61‐65 >65
3.9 9.0 9.8 11.0 27.8 15.2 12.7 7.6 3.1
10.7 16.1 11.3 11.5 20.2 10.4 9.6 5.8 4.4
Housing House Apartment Other
83.7 11.3 5.0
Household Income
< $20,000 $20,001 – 30,000 $30,001 – 40,000 $40,001 – 50,000 $50,001 – 75,000 $75,001 –100,000 $100,001‐150,000 > $150,000 Do Not Know
2.3 7.2 10.6 8.9 22.3 19.6 16.7 8.5 3.9
Classification Faculty Staff Both
28.6 71.4 1.7
34.1 65.9
Have Children < 18
Yes No
34.6 65.4
100
Faculty/staff commuting trips.
Faculty/Staff Factor Category % Respondents Comments
Have a Parking Permit?
Yes No
89.6 10.4
Type of Permit A D G
21.3 77.9 0.8
Drive Alone Trips 1 2 3 4 5 6‐10 >10
4.2 4.3 6.2 8.1 66.4 9.6 1.2
Average = 4.9 trips/person; 1219 Responses
Carpool Trips 1 2 3 4 5 >5
16.8 20.8 9.4 8.9 36.1 7.9
Average = 3.8 trips/person; 202 Responses
Walking Trips 1 ‐ 2 3 ‐ 4 5 6 – 10 11 – 15 16 – 20
31.2 18.5 32.1 12.9 1.8 4.6
Average = 4.9 trips/person; 109 Responses
Bicycling Trips 1 2 3 4 5 6 – 10 11 ‐ 20
27.1 30.5 8.5 8.5 18.6 3.4 3.4
Average = 3.4 trips/person; 59 Responses
Motorcycle/Scooter Trips
1 2 3 4 5 >5
21.7 17.4 17.4 21.7 13.0 8.6
Average =3.3 trips/person; 23 Responses
101
Faculty/staff bus trips.
Faculty/Staff Factor Category % Respondents Comments
UNT Shuttle Bus Trips 1 ‐ 2 3 ‐ 4 5 6 – 10
55.0 17.6 19.6 7.9
Average = 2.9 trips/person; 51 Responses
DCTA Connect Bus Trips
1 ‐ 2 3 ‐ 4 5 6 – 10
47.3 21.0 21.1 10.6
Average = 3.1 trips/person; 19 Responses
DCTA Commuter Bus Trips
1 2 4 5
23.1 38.5 15.4 23.1
Average = 2.8 trips/person; 13 Responses
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APPENDIX D
SUPPLEMENTARY MAPS
103
Student driving trips per person‐‐north Texas.
104
Faculty/staff bike trips.
105
Faculty/staff DCTA commuter bus trips.
106
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