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Alternative and adaptive transportation : what household and neighborhood factors support recovery from a drastic increase in gas price?

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Title:
Alternative and adaptive transportation : what household and neighborhood factors support recovery from a drastic increase in gas price?
Creator:
Bronson, Rachael K.
Place of Publication:
Denver, Colo.
Publisher:
University of Colorado Denver
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Language:
English

Thesis/Dissertation Information

Degree:
Master's ( Master of Engineering)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Civil Engineering, CU Denver
Degree Disciplines:
Civil Engineering
Committee Chair:
Marshall, Wesley E.
Committee Members:
Janson, Bruce N.
McAndrews, Carolyn

Notes

Thesis:
Civil engineering
General Note:
Department of Civil Engineering

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|University of Colorado Denver
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Auraria Library
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All applicable rights reserved by the source institution and holding location.
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809605601 ( OCLC )

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Full Text
ALTERNATIVE AND ADAPTIVE TRANSPORTATION:
WHAT HOUSEHOLD AND NEIGHBORHOOD FACTORS SUPPORT
RECOVERY FROM A DRASTIC INCREASE IN GAS PRICE?
by
RACHAEL K. BRONSON
B.S., University of South Carolina, 2007
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Master of Engineering
Civil Engineering
2014


This thesis for the Master of Engineering degree by
Rachael K. Bronson
has been approved for the
Civil Engineering Program
by
Wesley E. Marshall, Chair
Bruce N. Janson
Carolyn McAndrews
December 3, 2013


Bronson, Rachael K. (M.E., Civil Engineering]
Alternative and adaptive transportation: What household and neighborhood factors
support recovery from a drastic increase in gas price?
Thesis directed by Assistant Professor Wesley E. Marshall
ABSTRACT
Transportation resiliency is the ability for a transportation system to maintain or
return to a previous level of service after a disruptive event. Among many methods to
measure resiliency, quality and quantity of mode choice are shown to be promising. The
goal of this research is to understand how the availability of bicycling, walking, and transit -
three mode options in Denver, CO contributes to transportation resiliency under
economic shocks to the system caused by an abrupt doubling of gas price. In order to
realistically assess the alternative mode options, accounting for the fact that not eveiyone
will bike, walk or use transit (even in situations where those modes offer the lowest
,,costs,3, we adapted and refined the bicycle level of traffic stress approach developed by
Mekuria et al.This methodology classifies streets based upon the level of traffic stress (LTS]
that they impose on the bicycle user, and we apply this theory to the pedestrian and transit
modes as well. By understanding how well each bike/pedestrian/transit LTS option serves
the population, we are then able to assess the ability of areas across Denver to shift to these
modes utilizing two tools: one, via outputs derived from the Denver Regional Council of
Governments (DRCOG] Regional Travel Demand Model; and two, using a multinomial
logistic regression mode choice model. Results of this study suggest that certain areas -
which are nearer to downtown, have lower stress transportation choices, or possess
households with higher income are better able to adapt and recover after gas price
drastically increases. There is a cumulative effect in these results as well: low-income,
iii


suburban areas spend more of their household budget on their work commute than urban,
higher income areas, thus increasing their vulnerability. Through this analysis, we are able
to understand how geographically and demographically diverse areas in Denver are
affected by a disruptive event such as a gas price increase. We are also able measure the
financial benefit and resiliency value of various multi-modal transportation infrastructures
-even if few people are using those facilities today and how these investments may
support vulnerable communities.
The form and content of this abstract are approved. I recommend its publication.
Approved: Wesley E. Marshall
iv


DEDICATION
I dedicate this work to those whose well-being and quality of life may be
improved by better transportation services and choices.


ACKNOWLEDGEMENTS
Thanks goes to Dr. Wesley Marshall for his guidance, support and vision on this project.
Several other individuals at University of Colorado Denver supported this work, including
Alejandro Henao, Dr. Bruce Janson, Dr. Carey McAndrews, and the members of the ACT
Research Group. This study was made possible thanks to data from the City and County of
Denver and the Denver Regional Council of Governments. Funding for this work was
provided by the Mountain-Plains Consortium, a program sponsored by the U.S. Department
of Transportation. Last but not least, thanks to my husband, Michael, and sister, Leah, for
their support, love and encouragement.
vi


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION..........................................................1
II. BACKGROUND............................................................4
III. OVERVIEW OF STUDY.....................................................8
Traffic Stress Methodology...........................................10
Bicycle Level of Traffic Stress...................................10
Pedestrian Level of Traffic Stress................................12
Transit Level of Traffic Stress...................................13
Statistical Methodology..............................................14
IV. RESULTS..............................................................20
Citywide Analysis....................................................20
Census Tract Analysis................................................21
Census Tract Study Areas.............................................24
Study Area Characteristics........................................24
Proximity to Downtown.............................................27
Income............................................................28
Alternative Transportation Infrastructure.........................32
V. DISCUSSION...........................................................36
VI. CONCLUSION...........................................................40
REFERENCES.................................................................44
vii


TABLE
LIST OF TABLES
I. Criteria for bicycle level of traffic stress (LTS], based on posted speed limit and
number of travel lanes, adapted from Mekuria et al(LTS 1 is unlisted as it applies
only to off-street paths and trails] (2012]............................................11
II. Criteria for pedestrian level of traffic stress (LTS] based upon sidewalk width and
bicycle LTS (where a LTS does not apply to sidewalk widths of less than 5 feet that
are within the Bike LTS 1 category since such facilities are never narrower than this
width].............................................................................13
III. Descriptive statistics of the data used in the multinomial logistic regression mode
choice model.......................................................................18
IV. Results of the mode choice model...................................................18
V. Car, transit, walk and bicycle mode share in the baseline and resiliency scenario for
the entire City and County of Denver...............................................20
VI. Household and housing characteristics of each origin census tract in Denver (Social
Explorer 2013].....................................................................25
VII. Car mode share normalized for six census tract study areas under a normal and an
adjusted income of $59,230 (based upon 2011 median household income as
reported by the Denver Metro Chamber of Commerce]..........................................31
VIII. Level of Traffic Stress for biking, walking and transit, for all census tract origins to
the CBD and Aurora.................................................................34
IX. Trip characteristics for the Stapleton to Aurora work trip......................36
X. Walking and transit mode share after traffic stress for the Stapleton to Aurora trip is
reduced from LTS 4 to LTS 3.....................................................37
XI. Trip characteristics for the City Park West to CBD work trip....................38
XII. Walking and transit mode share after traffic stress for the City Park West CBD trip
is increased for both modes.............................................................39
viii


FIGURE
LIST OF FIGURES
I. Change in car mode share by census tract in City and County of Denver after the
resiliency scenario, in relation to the major streets and highways, bicycle paths, and
light rail transit network.......................................................23
II. Change in car mode share by census tract in City and County of Denver after the
resiliency scenario where income is held constant at $59,230 (based upon 2011
median household income as reported by the Denver Metro Chamber of
Commerce]........................................................................23
III. Denver census tracts displayed based upon median household income; the six
census tract study areas are outlined and location of two top work destinations
(Aurora and CBD] are indicated...................................................26
IV. Car mode share to the CBD in the baseline and resiliency scenario for the six census
tract study areas................................................................27
V. Car mode share to Aurora in the baseline and resiliency scenarios for the six census
tract study areas................................................................28
VI. Percent of income spent on gas for households traveling to Aurora at both the
baseline and resiliency scenarios................................................30
IX


CHAPTER I
INTRODUCTION
Transportation is critical to sustaining the economic and social vitality of
communities. As these systems become more complex and integrated regionally, nationally,
and internationally, their sustained safety and operation becomes increasingly essential to
the social and economic activities of a community. Given this intrinsic relationship, the
continued operation of these transportation systems is critical to societal well-being
(Freckleton etal 2012].
One aspect of transportation that is vulnerable to both abrupt variability as well as
long-term change thus causing significant disruption to individuals, households, and the
overall community is its cost. According to research conducted by the Center for Housing
Policy, households of all income levels spend on average about 20% of their budget on
transportation, while working families with incomes in the range of $20,000 $50,000
spend nearly 30% of their budget on transportation. This disproportionate impact to lower
income households is due in part to the tradeoff in housing and transportation costs -
families that spend less of their total budget on housing spend more of their budget on
transportation, up to three times more than those in more expensive housing (Lipman
2006].
Exacerbating the situation are several trends; most relevant to this research is the
fact that gas prices have been increasing over the last decade and are projected to continue
to increase (Lipman 2006]. According to data from the US Energy Information
Administration, from 2002 2012 gas prices have increased more than 10% annually,
compoundedWeekly U.S. All Grades All Formulations Retail Gasoline PricesDollars Per
Gallon 2013. At this rate, gas prices would be more than $8.00/gallon in 2020 Moreover,
1


gas prices are also subject to extreme volatility and have the potential to increase
dramatically in a short time period. Such abrupt fluctuations are difficult to guard against,
as the events that might cause them are unpredictable and often half a world away.
The goal of this research is to understand the potential impact of extreme price
shocks to the transportation system for work trips. More specifically, we seek to
understand how a sudden doubling of gas prices affects the resiliency of neighborhoods in
Denver, with respect to mode choice and the availability of varying types of bicycle,
pedestrian, and transit options. In the analysis, we focused on transportation resiliency in
the form of work trips because such trips are less discretionary in nature. In other words,
most people will still have to find a way to travel to work regardless of the prevailing gas
price or travel conditions. Such a situation occurred on December 1,2013 in New York City
with the derailment of the commuter train. Approximately 26,000 people use that transit
option on a daily basis, and after the derailment, many commuters had to seek other forms
of transportation. An interview on National Public Radio revealed one commuters struggles:
It's going to add at least another hour to my commute because I work on Wall Street. I
don't have a choice. I meant's the only way I get to work. In such a way, work trips
represent travel that people would likely still need to make after a catastrophic
circumstance, which is why we are investigating work commute behavior under a gas price
eventInvestigation of New Work Train Derailment Continues 2013.
Using a level of stress analysis, we distinguished between various forms of non-
automobile transportation based upon their accessibility to people of different skills and
ability. For instance, we assess a route with a bike path differently than a route with a bike
lane because research suggests that a larger segment of the overall population would ride a
bicycle on the former but not the latter (Dill and McNeil 2012].
2


After this preliminary analysis, we built a multinomial logistic regression mode
choice model to investigate whether certain communities with varying quantity and quality
of mode choice options are better equipped to cope with such gas price disruptions. Two
primary sources contribute to the mode choice model:i] data from the Denver regional
travel demand model, which provided the origin and destination for all work trips taken in
the Denver region; and ii] a level of traffic stress analysis, which assessed the quality of the
walking, biking, and transit mode options for these work trips. The resulting mode choice
model was then used to determine how people might shift modes given a sudden change in
the cost of driving. These findings offer a novel approach to valuing multi-modal
transportation investments and new insight into community and neighborhood resiliency.
3


CHAPTER II
BACKGROUND
In an effort to find more affordable housing, many people in todays society move to
the suburbs; unfortunately, the trade-off in housing savings often comes with increased
transportation costs. According to research conducted by the Center for Housing Policy, the
increase in transportation costs outweighs the savings on housing for individuals and
families that relocate to the suburbs (Lipman 2006]. This leads to an increased share of
household income required to meet these combined expenditures. Moreover, these
households on the suburban fringe typically have limited transportation options that are
safe, convenient, and accessible to jobs, schools, and other opportunities. Such households
become isolated in these car-dependent areas with diminishing resources to support such
lifestyles (Newman, Beatley, and Boyer 2009].
When a crisis arises, the households that are already vulnerable because of their
poor access to transportation and other vital resources will be most deprived (Fitzgerald
2012]. Such vulnerable households are often those who have significant housing and
transportation cost burdens. Because of the constraints and budgetary limitations to these
households, they have the least access to coping resources if a crisis arises. Collectively,
these households represent the weakest point in a citys capacity to mitigate such an event;
in such a way, a catastrophic event not only threatens the usefulness of physical
infrastructure and the built environment, but it also impacts social systems (Lipman 2006].
Policies to overcome these risks have often focused on lowering gas prices (Haas et
al 2008]; however, gasoline and motor oil average only 21% of total transportation
expenditures (Bureau of Labor Statistics 2013]. To truly overcome these hazards, the goal
must be to build resilient cities that offer a network of sustainable systems and
4


communities [Newman, Beatley, and Boyer 2009. Broadly, resiliency is a systems capacity
to manage unexpected events without catastrophic failure (Heaslip, Louisell, and Collura
2009]. A city without these resiliency measures is vulnerable to a threat that arises
(Godschalk 2003].
The study of resiliency has been explored in terms of many different disciplines,
including water, power, and communication; however, transportation resiliency has been
studied to a lesser degree. Heaslip et al offer that transportation resiliency is "the ability for
the system to maintain its demonstrated level of service or to restore itself to that level of
service in a specified timeframe" (2010]. Resiliency, if properly applied to a transportation
system, has the ability to not only reduce the likelihood of system failure but to also lessen
the consequences of any failure that does occur, thereby improving recovery (Freckleton et
al 2012.
In the book Resilient Cities: Responding to Peak Oil and Climate Change, Peter
Newman et al state that "[t]he agenda for future resilient cities is to have sustainable options
available so that a city can indeed reduce its driving or VMT" (vehicle miles travelled].
Newman et al propose seven elements to achieve more resilient transportation systems that
have reductions in VMT; bicycling, walking and transit as alternatives to driving are
central to each of these elements (2009]. VMT is linked to negative effects of traffic safety,
environmental health, public health, energy consumption, and other social costs of
automobile user (Ewing and Cervero 2010]; reducing VMT is thereby fundamental to
building resiliency. However, the capacity to reduce VMT even if a community is not doing
so today is equally important. Understanding this capacity is where our study hopes to
make its contribution.
Decision makers need metrics and tools to assess transportation system resiliency;
5


however, predicting and measuring transportation under disruptive events is extremely
complex. The use of multiple metrics is one approach that Godschalk and Murray-Tuite offer
to address this complexity; collectively, these authors identify ten critical components of
transportation resiliency: redundancy, diversity, efficiency, autonomous components,
strength, collaboration, adaptability, mobility, safety, and the ability to recover quickly
(2003; 2006]. Many of these elements are qualitative in nature; however, the work of
Heaslip et al attempts to quantify many of these resiliency measures by using a fuzzy
inference approach (2009].
The resiliency model structure developed by Heaslip et al uses a pairwise
comparison and a dependency structure, which "is based on a dependency relationship
between variables in a hierarchical structure" (2010]. In this model, movement towards the
top of the hierarchy offers greater potential for economic recoveiy and mobility. Central to
this methodology are four metric groups of interest: the individual, the community, the
economy, and the recovery. The measure of overall network resiliency is based upon the
fulfillment of these various levels and attributes within the resiliency hierarchy. In the
Heaslip et al study, there are various attributes that support these metric groups; the one
that is central to this study is personal mode choice (2009].
Transportation mode choice, for the individual and community, is the opportunity
to use multiple means of transportation. Providing such options better facilitates resiliency
to potentially threatening events. If only one mode choice were viable after an event, the
network would be overloaded and weakened as users would scramble for that one option;
however, if more than one option were available, the network would be less compromised
(Freckleton et al 2012]. Thus, creating a built environment with transportation alternatives
6


and land uses that support them is an important and effective strategy for lowering total
transportation costs (Haas et al 2008] and building resiliency into a system.
The goal of this work is to measure resiliency under a drastic gas price increase in
terms of mode shift from driving to walking, bicycling, and transit use. Unlike the existing
literature on resiliency, we are less interested in what mode people are choosing today and
more interested in what mode people have the ability to choose in extreme event
circumstances. Although this approach is similar to the Center for Housing Policy
affordability work that measures combined housing and transportation costs (Lipman
2006], we make one important distinction: we do not assume that people pursue the same
transportation mode as they did before the event. In this way, this research offers an
approach to measuring the transportation resiliency in Denver after a dramatic gas price
event, and in the process, reveals how certain communities and neighborhoods
demonstrate different mode shift capabilities based upon varying environmental and
demographic circumstances. This work also presents us with a unique understanding of the
option value of multi-modal infrastructures.
7


CHAPTER III
OVERVIEW OF STUDY
The analysis in this paper focuses on the mode share for work trips in the City and
County of Denver following a drastic gas price increase. To assess this hypothetical mode
shift, actual trips made in the region are analyzed under a series of gas price scenarios using
a multinomial logistic regression mode choice model. These trips were extracted from the
Denver Regional Council of Governments (DRCOG] Focus travel model,a regional activity
model. This model was based on an in-depth travel behavior survey of 12,000 households in
the Denver region, called Front Range Travel Counts (Denver Regional Council of
Governments 2013].
The database output from the DRCOG Focus travel model was provided to us in the
information platform Microsoft SQL Server. Several queries with specific characteristics
were executed, such as Tour Type: Home based and Tour Purpose: Work to determine the
number of total work trips from each origin traffic analysis zone (TAZ] to all work
destination TAZs. The total trips were broken down by the following four modes:
automobile, pedestrian, bicycle, and transit. The trips databases were then aggregated from
the TAZ to the census tract level for the home origin. For the work destination, they were
aggregated from the TAZ to the neighborhood level (within the city of Denver] or city (in
the case that a trips work destination was located outside the City and County of Denver.
There were a total of 143 home origins, which comprises the total number of census tracts
in the City and County of Denver as configured in the 2010 US Census; of these origins, the
top four work commute destinations were extracted. Four destinations were selected as
this number offers a sizable portion of the total trips taken in the census tract with respect
to the overall distribution while still offering a viable number of total regional trips to
analyze. Trips to the top four destinations were investigated for each of the 143 census
8


tracts, for 572 in total. Data for each of the four transportation modes was collected for
each of these 572 trips; this equals 2,288 different combinations.
In order to understand the bike, pedestrian, auto, and transit mode choices for each
combination, the Google Maps Engine Lite tool was consulted to determine the suggested
route for each mode between these origins and destinations. The geographic coordinates of
each census tract centroid was determined and entered into Google Maps as the starting
location. For the neighborhood destinations, the geographic coordinates of the centroid
were also used. However, if the destination was outside of Denver neighborhoods and the
destination was a city, Google Maps was consulted to provide the best location for the citys
geographic coordinates (as the centroid of the city limits does not often represent a city
center]. From these results, the top trip route option was selected for each mode and
several variables were recorded as follows:
For the auto mode,
Travel time (minutes];
Trip length (miles]; and
Whether the trip required limited access highway travel.
For the bicycle and pedestrian modes,
Travel time (minutes];
Trip length; and
Level of traffic stress for the trip.
For the transit mode,
Travel time (minutes] and
o Level of traffic stress for the trip.
9


In the following section, the traffic stress methodology is described in greater detail
including the relevant variables of interest.
Traffic Stress Methodology
In order to more realistically assess the alternative mode options for each trip,
accounting for the fact that not everyone will bike, walk, or use transit even in situations
where those modes offer the lowest dollar cost options we adapted and refined the bicycle
level of traffic stress approach developed by Mekuria et al(2012]. This methodology
classifies streets based upon their bicycle level of traffic stress (LTS] that they exhibit to the
user, and we applied our own adaptation of this methodology to the pedestrian and transit
modes as well.A Geographic Information System (GIS] was used to assign traffic stress
levels to Denver streets by the bike and walk mode, while Google Maps was used to
determine transit traffic stress. By estimating the bike/pedestrian/transit LTS options, we
were able to more realistically assess the ability of different population groups across
Denver to shift to these modes from the driving mode.
Bicycle Level of Traffic Stress
The bicycle LTS work of Mekuria et al assigns four traffic stress levels to street
segments and intersections based on characteristics such as operating space, speed, and
intersection treatment (2012]. In this methodology, we attempted to reasonably measure
the stress that different types of bicyclists might experience while relying on variables that
were readily available or easily measurable. The methodology we used for the analysis of
Denver streets, while based on the work of Mekuria et al, focused on three traffic and street
characteristics: speed, number of travel lanes, and the presence of bicycle facilities. The two
data sources used in this analysis are:
10


A street database for the City and County of Denver available in a GIS format with
attribute data for each street segment including the number of lanes, speed limit,
and functional classification (local, collector, arterial]; and
A street database for the City and County of Denver available in a GIS file with all
of the on- and off-road bicycle facilities in Denver, including varying bicycle
treatments (bike lanes, cycle tracks, etc.].
Similar to the work by Mekuria et al,four levels of stress were identified in the
Denver methodology and assigned to every street in the city. LTS 1 is acceptable for all
users and includes paved off street paths and trails only. Many adults tolerate LTS 2, while
LTS 3 is unacceptable to most. Finally, LTS 4 is the highest stress and is tolerated by few
individuals (Mekuria, Furth, and Nixon 2012]. The specific characteristics designating each
stress level are summarized in table I (with LTS 1 unlisted as it applies only to off street
paths and trails].
Table I: Criteria for bicycle level of traffic stress (LTS], based on posted speed limit and
number of travel lanes, adapted from Mekuria et al(LTS 1 is unlisted as it applies only to
off-street paths and trails] (2012]
<25 mph =30 mph >35 mph
2-3 lanes LTS 2 LTS 3 LTS 4
4-5 lanes LTS 3 LTS 4 LTS 4
6+ lanes LTS 4 LTS 4 LTS 4
As an addendum to the above criteria, we assessed specific bicycle infrastructure on
streets and adjusted traffic stress accordingly. For instance, if a street characterized by LTS
4 had a bike lane, this street was reassigned to LTS 3. Also, if a lower traffic stress street
intersected a higher traffic stress street, the approaching lower stress street segment was
reassigned the higher stress level. The rationale behind this is that a user will likely
experience the stress of the higher LTS street when crossing that street even if the street
that they were travelling on was defined by a lower stress level.
11


After each street in Denver was assigned a traffic stress level, the top four work
commute trips for each of the 143 Denver census tract origins were assigned a traffic stress
level based upon the stress of the streets along the trip route (route determined by Google
Maps as described in the introduction to this section]. The LTS of the route was predicated
by the highest traffic stress value assigned to any street segment along the way; thus, if a
route contained largely LTS 2 streets but crossed one LTS 4 arterial, then that route was
assigned the highest stress experienced by the user, or LTS 4. These values were assessed
and recorded for all 572 trips for the bike mode.
Pedestrian Level of Traffic Stress
As with the bicycle LTS methodology, the pedestrian approach we developed
intends to measure the stress that pedestrians experience on a roadway by using data that
is measurable and readily available. The pedestrian LTS was based upon three primary
characteristics: speed, number of travel lanes, and sidewalk width. Since the bicycle LTS
methodology measured these first two variables, as well as the presence of bicycle facilities
(which are often installed as a countermeasure to improve pedestrian safety (Harkey and
Zegeer 2004]], the pedestrian LTS methodology was built upon the bicycle LTS
designations.
Given this approach and using the GIS data built for the bicycle LTS levels, the
pedestrian analysis assigned traffic stress based upon the sidewalk width of these bicycle
LTS graded streets. The data available on the sidewalk width was acquired from a citywide
database in a GIS file that included all of the sidewalks in the Denver street network. Table II
describes each pedestrian LTS designation; in the criteria, the larger the sidewalk widths
contribute to lower pedestrian level of traffic stress. Since this is based upon bike LTS,
sidewalk widths of less than 5 feet are not applicable to the bike LTS value of 1 since all
12


bicycle paths in Denver (which exclusively represents bike LTS 1]are greater than this
width.
Table II: Criteria for pedestrian level of traffic stress (LTS] based upon sidewalk width and
bicycle LTS (where a LTS does not apply to sidewalk widths of less than 5 feet that are
within the Bike LTS 1 category since such facilities are never narrower than this width]
Bike LTS 1 Bike LTS 2 Bike LTS 3 Bike LTS 4
Sidewalk >5ft LTS 1 LTS 1 LTS 1 LTS 3
Sidewalk 4ft n/a LTS 1 LTS 2 LTS 3
Sidewalk 3ft n/a LTS 2 LTS 3 LTS 4
Sidewalk <2ft n/a LTS 3 LTS 4 LTS 4
The process of assigning traffic stress to the pedestrian mode option for the top
four-commute areas was similar to the bicycle mode. Given the trip route suggested by
Google Maps, pedestrian LTS was based upon the highest stress street experienced, and this
was again repeated for all 572 walking trips.
Transit Level of Traffic Stress
Instead of focusing on street and traffic characteristics for the transit LTS methodology,
as was done with the bicycle and pedestrian LTS methodology, this approach analyzed the
transit options available for each of the four trips using Google Maps. Transit traffic stress
was based upon two criteria: the number of transfers required to make the trip and
whether these transit connections were available by light rail transit or commuter bus.
Cognitive research conducted in the US and Europe has shown an individual preference for
light rail over bus (Scherer 2010]; thus, in this methodology, light rail transit favors a lower
traffic stress experience, as do fewer transit transfers. Accordingly, transit traffic stress was
assigned based upon the following assignments:
LTS 1:Light rail only;
LTS 2: Light rail with one transfer, or bus only (no transfers];
LTS 3: Light rail with two transfers, or any other transit combination (bus-bus or
light rail-bus] with one transfer; and
13


LTS 4: Light rail with three or more transfers, or any other transit combination (bus-
bus or light rail-bus] with two transfers.
For each trip origin and destination, the number of transfers and transit options
were assessed for the first route suggested in the Google Maps results. For the transit
function, the Google Maps tool defaults to the current date and time that the user is
investigating. Thus, a consistent day and time was utilized: the trip was entered to arrive by
8:00 AM on the nearest Wednesday. In the analysis, if walking was determined to be more
efficient than taking transit, Google Maps often recommends walking as the first option. In
this case, the first instance that transit is recommended was utilized for that particular trip.
Finally, if there were no transit options available for a certain trip, no LTS level was
assigned. This procedure was repeated for all 572 transit trips.
Statistical Methodology
The statistical relationship between mode choice and a drastic shift in gas price,
with respect to the level of traffic stress of the various modes, was investigated by using a
multinomial logistic regression model. The intent was to provide us with a realistic
understanding of who might be able to access certain facilities. Many mode choice
investigations fail to differentiate between different types of infrastructures. For instance,
the bicycle pavement marking known as the sharrow (or shared-use arrow] that is present
on a busy street might not be modeled any differently from a bike lane or a cycle track. In
reality, there is a percentage of the population that would ride everyday on a cycle track but
not in a bike lane; and there is another percentage of the population that would ride in a
bike lane but not on a route marked with a sharrow. These distinctions are what we were
looking to better model.
14


Accordingly, the LTS proxy variables took into account the following: the presence
of different types of bicycle, pedestrian, and transit infrastructure; characteristics of the
street such as number of lanes and speed of traffic; and functional classification of the
street. Also considered were population density and socioeconomic status (SES] variables
such as household income and the percentage of minorities. Interactions among the
selected variables were also tested and analyzed; in particular, interactions between the
LTS and SES variables were tested. The variables used in the final models were selected in
an effort to maximize model significance using the Akaike Information Criterion (AIC] value.
With respect to multi-collinearity, none of the variables used in the final models were highly
correlated with one another.
The basic structure of a multinomial logistic regression mode choice model is
derived from a basic logit model. The following generalized logit equation determines the
probability of choosing a specific mode (Martin and McGuckin 1998].
e
pi = ~7T
Sf=1e
where:
Pt = probability of somebody choosing mode i =1,2,..., k;
ut = utility function describing the relative attractiveness of mode i; and
YJi=i ^Ui- sum of the functions for all available mode alternatives
The probability of choosing a particular mode depends on the above utility function
relative to the utility functions for all the other mode options. In conventional four-step
model transportation planning, the utility function of the logit equation typically contains
variables such as in-vehicle travel time, out-of-vehicle travel time, and the cost associated
with each mode for a particular type of trip between two specific zones. Our utility
functions included travel time and costs but also took into consideration the level of traffic
15


stress for bicycling, walking, and transit, and with respect to driving, whether or not the trip
includes a limited access highway. Four mode types were modeled transit, walking,
biking, and driving and to account for four separate categorical outcomes, a multinomial
logistic regression model was used (Ben-Akiva and Morikawa 2002]. A multinomial logistic
regression simultaneously considers a binary logit model for every possible combination of
outcomes; in this study, the four different outcomes are equivalent to six binary logit
models (Long 1997]. One assumption of this model is that the probabilities related to the
mode choices sum to 1:
P^transit) + P(walking) + P^biking) + P^driving)=1
For such a probability-based model, the multinomial logistic regression equation is
as follows:
=l\xt) = i+^/e(x.P])form = l
P(yi = m\Xi)=1+zx)for m > 1
where:
y = dependent variable,
j = number of categorical outcomes for four mode choices,
P(y = m\x) = probability of choosing mode m given x,
xt = independent predictor variable, and
P = estimated coefficient representing the effects of the independent
variable.
The probability of the four modes (transit, walking, biking, and driving] was
calculated for the top four work trip destinations for each Denver census tract origin using
the multinomial logistic regression model for a baseline gasoline price of $2.70 and a
16


doubling of that price to $5.40 per gallon. The base gas price was chosen because it was the
prevailing gas price estimate for Denver region when the Front Range Travel Survey was
being administered (this survey is an in-depth household travel survey for the region from
which our data was gathered] (Denver Regional Council of Governments 2013]. This gas
price was used to determine the average annual cost of gas for each 572 commute trips
using an average vehicle efficiency of 20.2 miles per gallon (Environmental Protection
Agency Office of Transportation and Air Quality 2007]. We then calculated the average
annual percent of the median household income spent on gas for commute trips in each
census tract, a value that could be doubled in the model to reflect the resiliency scenario.
This informed the cost of driving for these work commute trips and was an important area
of analysis that will be reviewed in the results section of this report.
Table III provides the descriptive statistics of all of the data that was put into the
model. This includes the following for each variable: the minimum and maximum values, the
mean, standard deviation (SD], and number of observations. Table IV shows the results of
the mode choice model.
17


Table III: Descriptive statistics of the data used in the multinomial logistic regression mode
choice model
Variable Obs Mean SD Min Max
Population of origin census tract 2,288 4,129.42 1,567.84 314.00 9,462.00
(J (A s Population density of origin census tract 2,288 7,032.95 3,938.61 28.51 24,770.81
Percent minority in origin census tract 2,288 25.20 17.12 0 79.91
Median HH income of origin census tract 2,288 52,354.48 24,550.32 9,571.00 153,571.00
# of driving miles to work (avg.} 2,288 6.47 4.74 0 27.40
3 # of minutes driving to work (avg.} 2,288 13.80 6.61 0 40
O Whether car trip to work includes hwy driving (avg. of 0,1 variable] 2,288 0.46 0.50 0 1
3 < Proportion of income spent on annual driving to work favg.] 2,288 0.01 0.01 0.00 0.11
# of minutes for transit trip to work (avg.} 2,224 44.60 23.73 0 123
# of transfers for transit trip to work (avg.} 2,224 0.55 0.64 0 3
2 Whether transit trip to work includes light rail (avg. of 0,1 variable] 2,224 0.14 0.35 0 1
Transit LTS score for trip to work (avg.} 2,224 2.48 0.72 0 4
# of walking miles to work (avg.} 2,288 5.86 13.01 0 304.00
> # of minutes walking to work (avg.} 2,288 106.00 71.62 0 469
Walking LTS score for trip to work (avg.} 2,288 3.60 0.66 0 4
cS # of biking miles to work (avg.} 2,288 6.22 4.28 0 26.80
# of minutes biking to work (avg.} 2,288 35.35 23.30 0 138
Biking LTS score for trip to work (avg.} 2,288 3.93 0.44 0 4
Table IV: Results of the mode choice model
Variable Transit Walkine Biking
Intercept 0.6197 1.9941 1.1106
Miscellaneous
Population of origin Census Tract 0.00008 0.00021 0.00014
Population Density of origin Census Tract 0.000071 0.000095 0.000055
Percent Minority in origin Census Tract 0.00799 0.0155 0.0032
Median HH Income of origin Census 0.0000029 0.00000363 0.00000759
Tract 4 9 2
Driving
# of driving miles to work (avg.} 0.1477 0.7477 0.1907
Proportion of income spent on annual driving to work favg.) 45.4014 66.357 50.6937
Transit
Transit LTS score for trip to work (avg 0.4131 0.0249 0.3663
Whether transit trip to work includes light rail 0.7098 0.4461 0.2331 **
Walking
Walking LTS score for trip to work (avg 0.313 0.3473 0.2145
Model Fit
Observations 2,224
18


Results of the mode shares for a given home census tract were weighted based upon
the relative number of trips. For example, if the top 4 destinations for a home zone have
100 people total and 60 of them were going to destination A with 80% auto mode share, 20
to B with 60% auto mode share,15 to C with 90% auto mode share, and 5 to D with 40%
auto mode share, the home census tract automobile mode share would be 75.5%, as
follows:
0.755 =
0.8(60) + 0.6(20) + 0.9(15) + 0.4(5)
100
To assist in evaluating the results, we analyzed mode shift at the census tract level
and at the citywide level. To determine the mode shifts at the census tract level after a two-
fold increase in gas price, each trip taken in Denver was averaged and normalized at the
census tract level based upon the actual number of trips taken for each origin and
destination. Data used for this analysis was from the 2010 American Community Survey
(ACS], administered by the US Census (Social Explorer 2013]. The census tract mode share
values were then averaged and normalized based upon census tract population, resulting in
the citywide mode shares.
19


CHAPTER IV
RESULTS
Citywide Analysis
In reporting the results, we first explore expected trends at the citywide level, and
then we investigate expected changes at the census tract level. We compare mode shifts at
the city and census tract level in Denver in a scenario where the gas price doubles, from a
base price of $2.70 per gallon (the baseline scenario] to a two-fold increase of $5.40 (the
resiliency scenario]. Table V displays the mode shift after a doubling in gas price at the
citywide level. In the resiliency scenario, car mode share decreased while biking, walking,
and transit use increased. Of the three non-driving modes of travel, transit mode share
increased the most in the resiliency scenario. This table indicates that with a two-fold gas
price increase, some individuals in Denver respond by changing their work transportation
mode away from driving to transit, walking and biking.
Table V: Car, transit, walk and bicycle mode share in the baseline and resiliency scenario
for the entire City and County of Denver
Scenario Car mode Transit mode Walk mode Bicycle mode
share share share share
Baseline 83% 11% 4% 2%
Resiliency 78% 13% 5% 4%
While this citywide analysis offers a broad understanding of mode shift and would
be interesting to compare with similar analyses of other cities it is also important to
understand the differing impact of this resiliency situation at smaller levels of geography.
In other words, what neighborhoods are better or worse off? What factors play a role in the
resiliency disparities? The remainder of this section will look to answer these questions by
first exploring the mode shift during the baseline and resiliency scenarios of the 143 Denver
census tracts, and then by digging deeper and exploring these changes at six unique census
tract study areas.
20


Census Tract Analysis
As discussed in the results, we first explore expected trends at the census tract level
for the entire city and county of Denver, and then we explore trends and contributing
factors further by investigating expected changes at six specific census tracts. We compare
mode shifts at the census tract level in Denver in a scenario where the gas price doubles,
from a base price of $2.70 per gallon (the baseline scenario] to a two-fold increase of $5.40
(the resiliency scenario].
For each 143 Denver census tracts, after the resiliency scenario, car mode share
decreased by varying amounts while bicycle, walking, and transit mode shares increased.
This data is best depicted spatially in figure I, where the changes in car mode shares in each
census tract are shown. Figure I is compared side-by-side to figure II, where the change in
driving mode share is displayed with income held constant. Figures I and II also illustrate
the interstate highway network, bicycle paths, and light rail facilities in Denver. While the
interstate highways are distributed throughout the city, the bicycle path and light rail
network are not. The bicycle paths and light rail lines service the same general areas of
Denver: the CBD and southern portions of the city. Denver bicycle paths and light rail
network are permanent, higher ease-of-use routes, as opposed to the less fixed and lower
ease-of-use options, such as bicycle lanes and bus.
Those census tracts that have the highest change in driving mode share, displayed in
figure I as the darker shaded color, have a greater shift away from driving to transit, biking,
and walking. Many of these census tracts appear to be located away from the Central
Business District, particularly scattered throughout the southwestern areas of Denver. On
the other hand, those census tracts with the lowest shift in driving mode share appear to be
clustered around the CBD and in the northeast areas of Denver. These more urban census
tracts already have a lower driving mode share, thus the driving mode shift after the
21


resiliency scenario is less acute. Other socio-economic or demographic factors may also
affect the shift as it occurs in different geographic census tracts.
In order to understand what other factors may be impacting these trends, we held
income constant for all Denver census tracts. The Metro Denver Economic Development
Corporation reports thatfor 2011,the median household income in Denver was $59,230
(Metro Denver Economic Development Corporation 2013]. Holding income constant at this
value, the resulting change in mode share between the baseline and resiliency scenario with
income at $59,230 are spatially displayed in figure II.
In figure II, the darker colors again indicate the higher shift away from driving mode
share. There is a significant group of these census tracts with a higher shift away from the
driving mode share located south of the CBD. These census tracts are not adjacent to the
CBD, but they are surrounded by high ease-of-use transit and bicycling facilities: the
southeast and southwest light rail transit lines service the area, as do multiple bicycle paths
including the Platte River trail and the Cherry Creek bike path. Thus, it appears that there
are factors in addition to income that may be impacting mode share, and further analysis is
merited to better understand what these elements may be.
22


Figure I: Change in car mode share by census tract in City and
County of Denver after the resiliency scenario, in relation to the
major streets and highways, bicycle paths, and light rail transit
network
Figure II: Change in car mode share by census tract in City and
County of Denver after the resiliency scenario where income is
held constant at $59,z30 (based upon 2011 median household
income as reported by the Denver Metro Chamber of Commerce]
ts3
00


Census Tract Study Areas
Study Area Characteristics
Since it appears that geographic and demographic factors affect transportation
choices after the change from the baseline to the resiliency scenario, we take a closer look at
mode shift trends in six Denver census tracts. In selecting these census tracts, our variables
of interest are proximity to downtown and income. We selected three census tracts that are
situated closer to the City center and three that are in more suburban locations. We also
chose census tracts that have low, middle, and high median household incomes, selecting
two in each income range (based on 2010 ACS household income values] (Social Explorer
2013]. To facilitate the comparison, we also selected census tracts that share the following
two top work destinations: Denver Central Business District (CBD] and the City of Aurora
(located outside of Denver city limits].
The six census tract origins that were selected for this analysis will be referred to by
the neighborhood contained within the census tract, not by the census tract number.
Household and housing characteristics of each census tract origin are illustrated in Table VI,
again retrieved from the 2010 ACS (Social Explorer 2013]. Globeville, an urban census tract
with lower household income, is located just north of the CBD. College View/S Platte is a
lower income, suburban census tract that is located in the southern part of the Denver
city/county limits. City Park West is a middle-income census tract that is located just east of
the CBD. Sunnyside is also a middle-income census tract but it is located in the
northwestern portion of the Denver limits. Finally, Country Club is a higher income census
tract that is located directly southeast of the CBD, while Stapleton is high income census
tract that is located near the northeast corner of Denver city/county limits.
24


Table VI: Household and housing characteristics of each origin census tract in Denver
(Social Explorer 2013]
High income Middle income Low income
Country Club Stapleton City Park West Sunnyside Globeville College/ S Platte
Driving distance to CBD (miles) 2.9 12 2.9 3.7 3.4 17.8
Median HH income $130,321 $133,393 $51,371 $51,163 $24,190 $30,076
No. persons per HH 2.5 2.8 2.2 2.5 3.1 3
Home values $723,100 $458,600 $325,100 $218,500 $164,200 $170,300
Monthly rent $964 $1682 $667 $714 $833 $710
Pop density 4761 1686 7302 5705 1544 4258
% Non-white 16% 17% 30% 20% 30% 37%
% Hispanic or Latino 4% 16% 10% 62% 80% 64%
Table VI lists several variables that are relevant to the data analysis. The two lower
income census tracts have larger households, and their home values are lowest (home
values are reported for median house value for all owner-occupied housing units]. Monthly
rent is listed in terms of the average gross rent for renter-occupied housing units, and the
higher income study areas have the highest monthly rent costs. The study areas vaiy in
their population density, and the areas with the lowest percent non-white populations are
the highest income areas. With the exception of Globeville, the census tracts with highest
portion of Hispanic or Latino ethnicity are suburban; also, the highest percentage of this
ethnicity within the six study areas are in the lowest income census tracts.
As mentioned earlier, work trips from these six census tracts to two destinations
(Aurora and the CBD] were also analyzed. Aurora and CBD are two of the top four of work
commute destinations for each origin census tract. Additionally, CBD and Aurora represent
an urban and suburban destination respectively, which is relevant to our analysis of urban
and suburban trip origins. Figure III illustrates the location of these two destinations in
relation to the six work trip origins, as well as the median household income of each census
25


tract in Denver. Geographically, these six census tract study areas are scattered throughout
the City of Denver.
Figure III: Denver census tracts displayed based upon median household income; the six
census tract study areas are outlined and location of two top work destinations (Aurora and
CBD] are indicated
In terms of the entire city, figure III reveals how census tracts of varying household
income are situated throughout the city. Lower income census tracts are located around the
City, particularly in the northern/central limits and western/central limits of the City.
Higher income census tracts are located around the northeast of the City and just southeast
of the CBD.
Now that the characteristics of the six Denver census tract study areas have been
reviewed, mode shift trends will be explored. In the analysis of these census tracts, three
elements have a significant impact on mode shift in our six census tracts and are further
explored: proximity to downtown, income, and availability of multi-modal transportation
infrastructure.
26


Proximity to Downtown
Results from the mode choice model reveal that for the six Denver census tract
study areas, the driving mode share is consistently higher for suburban census tracts
origins as compared to their urban counterparts. Trips from Stapleton, Sunnyside, and
College View/S Platte have higher driving mode share than trips from Country Club, City
Park West, and Globeville. Figure IV illustrates these trends for work trips from the six
census tract study areas to the CBD. This trend is particularly acute for the higher income
census tracts: Stapleton and Country Club. A factor influencing this is that Stapleton is
significantly further from the CBD than Country Club (12 miles versus 2.9]. Globeville and
College View/S Platte -low-income census tracts also are quite different in their distance
to the CBD (3.4 and 17.8 miles respectively], but do not display the driving mode share
difference that the high-income study areas do.
Car Mode Share to the CBD
120%
I

u
S
QJ
O
S
u
n
u
100%
80%
60%
40%
20%
0%
Baseline Scenario
Country Club Stapleton City Park West Sunnyside Globeville College View /
S Platte
Figure IV: Car mode share to the CBD in the baseline and resiliency scenario for the six
census tract study areas
Another interesting trend related to the driving mode share is that all trips to CBD,
regardless of the origin, have lower driving mode share than those same trips to Aurora. So
27


in addition to driving mode share being impacted by proximity to downtown, it is also
impacted by the proximity to downtown of a households destination. Figure V displays this
trend.
Car Mode Share to Aurora
Baseline Scenario
120%
100%
80%
| 60%
o
S 40%
A
u
20%
0%
Country Club Stapleton City Park Sunnyside Globeville College
Platte
Figure V: Car mode share to Aurora in the baseline and resiliency scenarios for the six
census tract study areas
When comparing the trends in figures IV and V, we see that the car mode share from
Stapleton to the CBD (95%] and to Aurora (97%] for the resiliency scenario remains fairly
unchanged. In these cases, already nearly all of the households are opting to drive for both
their trip to Aurora and CBD. However, most of the other study areas show a significant
increase in car mode share for the trips to Aurora when compared to the CBD for the
resiliency scenario. Because of this overall higher mode share across all study areas, the
difference in car mode share between urban and suburban census tracts and Aurora is less
dramatic than it was in figure IV.
Income
One important impact of the trip distance differences for those people living in the
urban and suburban origins, particularly as it relates to resiliency, is the fact that it directly
impacts their household budgets in terms of gas expenditures. Those households in the
28


suburban origins that have a farther distance to travel for work trips are spending more
money on gas than their urban counterparts. This discrepancy impacts low-income
households more than high-income areas in terms of the percent of income spent on gas.
With high household income, these census tracts have more capacity to withstand increases
in gas price than those areas with more constrained financial resources. For this reason, we
would expect to see less of a change in driving mode shift for high-income areas when
compared to lower income areas after the resiliency scenario.
This trend is indeed apparent in figure V for trips to Aurora. For the middle and
higher income households, after the resiliency scenario, car mode share generally remains
high. With more income available to these high-income households, they can better cope
with higher costs of driving and do not necessarily have to change their travel behavior to
mitigate the impact to their budget. On the other hand, for the low-income households
(Globeville and College View/S Platte], car mode share falls more substantially after the
resiliency scenario. The same trend occurs for trips to CBD as displayed in figure IV,
although it is less acute. In figure IV, the shift away from driving after the resiliency scenario
is greater for the lower income census tracts than for the middle and higher income
households.
To further understand this trend, we determined the percent income spent on gas
for each trip. As discussed in the results, we were able to calculate this value based upon the
length of the trip in miles, and assuming an average vehicle efficiency of 20.2 miles per
gallon (Environmental Protection Agency Office of Transportation and Air Quality 2007].
Results revealed that higher income households (Stapleton and Country Club] spent the
least percentage of their household budget on gas for trips to Aurora than any other census
tracts being reviewed even after the resiliency scenario (see figure VI]. For trips to the
CBD after the resiliency scenario, lower income households spent more of their income on
29


gas than other areas. When compared to percent of income spent on gas for trips to Aurora,
trips to the CBD have less impact on household income which again is due to the distance
of these trips and amount of gas used. Finally, suburban trips have a higher percent income
spent on gas than their urban counterparts, except for the Stapleton to Aurora trip (which
relates to the fact that this trip is shorter in distance than Stapleton to CBD].
Percent Income Spent on Gas to Aurora
9.0%
^ 8.0%
7.0% 1
I
6.0% 1
c I
2 5.0% 1
4.0%
g 3.0%
s
2 2.0%
^ 1.0%
0.0%
Figure VI: Percent of income spent on gas for households traveling to Aurora at both the
baseline and resiliency scenarios
In order to understand how other factors may be influencing mode share, we again
hold income constant, this time specifically for the six census tract study areas. In doing so,
we expect to better understand the extent to which proximity to downtown and other
variables may impact mode share. With a median household income again adjusted to
$59,230 for the six census tract study areas, car mode share in certain areas experience
some interesting changes, as depicted in table VII (Metro Denver Economic Development
Corporation 2013]. Table VII lists the changes in car mode share for the baseline and
resiliency scenarios under their normal household income as well as the values before and
after the resiliency scenario under the adjusted income.
Baseline Scenario
Country Club Stapleton City Park Sunnyside Globeville
West
College
View / S
Platte
30


Table VII: Car mode share normalized for six census tract study areas under a normal and
an adjusted income of $59,230 (based upon 2011 median household income as reported by
the Denver Metro Chamber of Commerce]
actua Car mode share under 2012 median household income
Baseline scenario Resiliency scenario Change in car mode share
Country Club 73.5% 71.2% -2.3%
Stapleton 94.6% 93.8% -0.8%
City Park West 58.0% 53.3% -4.7%
Sunnyside 81.9% 75.8% -6.1%
Globeville 76.7% 64.6% -12.1%
College View/S Platte 87.6% 76.8% -10.8%
Car mode share with income held constant at $59,230
Car mode share, adjusted income Resiliency car mode share, adjusted income Change in car mode share
75.8% 70.6% -5.2%
95.1% 93.1% -2.0%
57.9% 54.0% -3.9%
82.1% 77.2% -4.9%
79.5% 76.0% -3.5%
89.6% 86.2% -3.4%
31


In table VII, for both the actual and adjusted incomes, the baseline car mode share
values are consistently higher than the resiliency scenario car mode share. Country Club
and Stapleton (which normally exhibit median household income above $130,000]
experience a greater driving mode share under this adjusted income level with the
resiliency scenario. With less income at their disposal, the formerly higher income areas
have a greater shift away from driving when their income is adjusted to $59,230. On the
other hand, the lower income households in Globeville and College View/S Platte have less
of a shift away from the driving mode share when their income is adjusted. In other words,
these formerly lower income areas maintain a higher driving mode share during the
resiliency scenario when their income is increased to the adjusted value of $59,230; with
more income, these areas are less reliant on alternative modes of transportation in coping
with the resiliency scenario.
Another trend under the adjusted incomes is that two of the more urban census
tracts, Country Club and Globeville, have a higher shift away from the driving mode share
after the resiliency scenario than their suburban counterparts. In these areas, more people
are opting to take alternative forms of transportation with the resiliency scenario. However,
City Park West, the third urban census tract, does not display this trend, which suggests that
another demographic or environmental factor may be involved in favoring Sunnyside
(suburban, middle income census tract] to have a greater shift driving mode shift. Finally,
the areas with the highest driving mode share under the adjusted income are Stapleton and
College View/S Platte, which are farther from downtown.
Alternative Transportation Infrastructure
An important influence to mode share for certain neighborhoods and households is
the availability of low stress transportation options. With more transportation modes
available to urban origins, individuals and households particularly those with budget
32


constraints may choose transportation options other than driving for their work travel
needs. Thus in addition to income and proximity to downtown, another variable that
impacts mode shift is availability of active transportation options and the level of traffic
stress of those options.
In the mode choice model, bicycle LTS was removed since it was highly correlated to
walk LTS. Thus, we analyze walk LTS to understand how bike LTS may also affect mode
choice. Table VIII provides the walk and transit LTS values for all trips in the census tract
study areas. For the walk mode, only three trips to the CBD from census tract origins are of
the lowest traffic stress, LTS 3: Sunnyside, City Park West, and Country Club. Consequently,
these trips have some of the highest walk mode share, respectively: 4%, 22%, and 10%
(reported for the baseline scenario]. It is interesting to note that Countiy Club and City Park
West are urban areas (while Sunnyside is not]; yet, they all have the highest walk mode
shares of all six-study areas. This suggests that the low traffic stress walking experience for
those traveling from Sunnyside to CBD contributes in improving the walk mode share for
this suburban area. During the resiliency scenario, these walk mode shares for Sunnyside,
City Park West, and Country Club increase to 6%, 24%, and 11% (respectively]. Because
these trips are less stressful, traveling along streets with lower speed, wider sidewalks, and
fewer lanes, individuals are more likely to shift to the walk mode for their work
transportation needs.
33


Table VIII: Level of Traffic Stress for biking, walking and transit, for all census tract origins
to the CBD and Aurora
Trips to CBD Trips to Aurora
Transit LTS Walk LTS Transit LTS Walk LTS
Country Club 3 3 2 4
Stapleton 2 4 4 4
City Park West 2 3 3 4
Sunnyside 2 3 2 4
Globeville 2 4 3 4
College View/S Platte 3 4 3 4
Another area of analysis that indicated that factors related to the transportation
environment impacted the trips from Country Club, City Park West, and Sunnyside were the
results in table VII. We will recall that in table VII, when income is held constant, the areas
with the largest shifts away from driving mode share include these census tracts. Given that
only two of the census tracts (Country Club and City Park West] are proximate to downtown
suggest that other factors allow for the higher shift away from driving mode share for
Sunnyside, which is not as near to downtown. After this analysis of alternate modes of
transportation, it is clear that level of traffic stress is important in impacting that mode shift.
When compared to trips to CBD, walking trips to Aurora are more stressful. No trips
to Aurora by foot are less than LTS 4. This suggests the extent to which this suburban
destination does not support the pedestrian mode of transportation and how the driving
mode share to Aurora from households throughout Denver remains as high as it is (refer to
figure IV].
Transit LTS is measured by number of transfers and whether the trip includes light
rail transit or commuter bus. Of all of the trips in the study areas, the only trip of LTS 4 is
from Stapleton to Aurora. At the same time, this trip has the lowest transit mode share of
3.2% at the baseline scenario. Trips that are of transit LTS 2 range in mode share from 3.7%
to 21.0%, with the lower range value being impacted by a higher trip length and duration.
34


Sunnyside, the more suburban of the middle-income census tracts, has a higher
transit mode share to Aurora than that same trip from City Park West, its urban
counterpart. This is likely because the transit level of traffic stress from Aurora to
Sunnyside is only a value of two, while to City Park West it is LTS 3. This indicates that the
trip from City Park West is more stressful than the trip from Sunnyside with respect to the
number of transfers (since light rail transit does not serve this trip]. This further
demonstrates how the transit experience, measured in traffic stress, can influence mode
share even when there may be disparities in trip length and distance.
35


CHAPTER V
DISCUSSION
In the analysis of mode share after the resiliency scenario in Denver, several factors
impact mode shift. To fully understand these elements, we will explore two trips in further
detail, allowing us to understand how proximity to downtown, income and LTS impact
mode share. The trips we will review are Stapleton to Aurora and City Park West to CBD.
Tables IX and XI describe the characteristics of each trip.
Table IX: Trip characteristics for the Stapleton to Aurora work trip
Mode Length of trip LTS Baseline scenario mode share Resiliency scenario mode share
Walk 5.6 miles,lh 53m 4 0.2% 0.3%
Bike 6 miles, 34 min 4 0.6% 0.7%
Transit lh 6m 4 2.1% 2.5%
Car 7 miles,12 min n/a 97.1% 96.5%
For the trip from Stapleton to Aurora, although the trip length and duration are
fairly moderate, the baseline mode shares are largely car dominated. Returning to table VI,
we recall that Stapleton is 12 miles from downtown Denver, which suggests that alternative
transportation mode options are not as plentiful as they may be nearer to downtown.
Further exacerbating this situation is the fact that the trip destination is Aurora, also not
downtown and lacking in multi-modal transportation options. Aurora is also very likely to
have more availability of free parking, further encouraging car use. We also recall that
Stapleton is our highest income census tract, which suggests that household budgets in
Stapleton are likely better able to withstand significant and immediate increases in
transportation costs. After the resiliency scenario, the cost of gas to drive from Stapleton to
Aurora doubles to $3.21/trip. With high household income, this increase remains a small
portion of household budgets in Stapleton, thus individuals have less incentive to shift
modes where it may otherwise offer some cost savings to those with constrained budgets.
36


Table IX reveals a level of traffic stress by bike, walk, and transit for the Stapleton to
Aurora as a value of four. This means that the walking route intersects higher speeds and
multi-lane roads without wide sidewalks; the biking route is along and intersects similar
roads that also do not have a bicycle facility; and the transit trip involves two transfers and
no light rail transit option. Additionally for the driving mode, Google Maps suggests that the
traveller follow the interstate for the trip. These characteristics do not create an inviting
environment for bicycling, walking, or transit use, which results in very little mode shift
after the resiliency scenario. As a result, the work trip from Stapleton to Aurora does not
offer safe and accessible mode options, nor do a majority of the residents require these
options due to financial reasons.
To understand how mode share might be affected if there were better bicycle,
walking, and transit facilities serving the Stapleton to Aurora trip, we use the model to
decrease traffic stress for the walk and transit mode (refer to table X]. When walk traffic
stress is reduced from LTS 4 to LTS 3, walk mode share for both the baseline and resiliency
scenario increases one tenth of a percent. We would expect a similar trend for the bicycle
mode share with a reduction in bike LTS. And when the same LTS reduction is made for the
transit mode share, transit use increases by more than two percentage points for both the
baseline and resiliency scenario. So, although these trips display low walking, biking, and
transit mode share in both the baseline and resiliency scenarios under current traffic stress
conditions, when LTS is reduced a mode shifting away from driving is more notable -
particularly for the transit option.
Table X: Walking and transit mode share after traffic stress for the Stapleton to Aurora trip
is reduced from LTS 4 to LTS 3
Mode LTS value after adjustment Mode share, adjusted LTS Resiliency mode share, adjusted LTS
Walk 3 0.3% 0.4%
Transit 3 4.2% 4.8%
37


When compared to Stapleton to Aurora, the trip from City Park West to the CBD
offers a different depiction. The trip by bike, foot, transit, and car are all very moderate in
length and duration. We will recall from table VI that City Park West is a middle-income
census tract and is very close to downtown (2.9 miles]. This means that there are more
transportation options available to this area, and the baseline mode shares in table XI
suggest fairly high alternative mode option use. And with a middle household income, City
Park West is less able than the high-income study areas to withstand significant disruptions
to their household budget.
Table XI: Trip characteristics for the City Park West to CBD work trip
Mode Length of trip LTS Baseline scenario mode share Resiliency scenario mode share
Walk 1.7 miles, 33 min 3 32.1% 35.4%
Bike 2 miles,13 min 4 5.7% 6.1%
Transit 18 min 2 19.8% 21.0%
Car 2.9 miles, 9 min n/a 42.4% 37.5%
After the resiliency scenario, there is substantial mode shift from driving towards
walking, transit use, and lastly, to biking. With their moderate income, households in City
Park West are less able to withstand the increase in the cost of gas for the driving mode
($0.78/trip], so they embrace the low stress transportation options that are more available
to them -walking and transit. Such behavior is resilient and adaptive to the disruption of a
gas price increase.
Table XI reveals that the traffic stress of the walking and transit trips from City Park
West to CBD are moderate. The walk option is traffic stress level three, and the transit is
two. The walking trip traverses some more stressful roads that lack wide sidewalks, and the
transit trip does not offer a light rail option but it does not involve transfers. The biking trip
does cross high stress streets that may act as a barrier to certain less tolerant individuals,
which results in a traffic stress level of four for that mode.
38


To understand how a more stressful multi-modal transportation environment might
affect the trip from City Park West to CBD, we use the model to increase walk and transit
LTS. Table XII displays these results, and in both cases, walking and transit mode share
decrease. For the walk mode share, the baseline and resiliency mode share decreases by
over 40%, and we might expect a similar situation for bike mode share. The transit mode
share decreases by over 30% for both the baseline and resiliency scenario. Thus, walk mode
share is more impacted by a decrease in traffic stress than transit is, while transit mode
share is more impacted by an increase in traffic stress than walking.
Table XII: Walking and transit mode share after traffic stress for the City Park West CBD
trip is increased for both modes
Mode LTS value after adjustment Mode share, adjusted LTS Resiliency mode share, adjusted LTS
Walk 4 18.0% 21.1%
Transit 3 13.0% 14.6%
39


CHAPTER VI
CONCLUSION
In measuring mode shift before and after a drastic increase in gas price, this study
sought to understand how certain areas in Denver, CO, with various environmental and
demographic characteristics, are better equipped to return to a normal level of service than
other areas. In terms of this mode shift, we focused not on how individuals are behaving
today, but on what they have the ability to do in a disruptive gas price event based upon
environmental and demographic characteristics.
To measure this latent mode share behavior, we employed a multinomial logistic
regression mode choice model, which allowed us to consider the shift for four primary
modes of transportation: transit, walking, biking, and driving. Results of the model revealed
that certain neighborhoods and individuals are better suited to withstand a disruptive gas
price event. Three attributes appeared to be most relevant in these trends: household
proximity to downtown, median household income, and the availability of multi-modal
transportation options. All told, the closer to downtown, the higher the household income,
and the better the accessibility to multi-modal forms of transportation, the better able
certain areas in Denver are to react to the disruptive event.
In terms of proximity to downtown, results of an investigation of six diverse census
tracts (selected based upon income and geography] indicated that the driving mode share is
consistently higher for suburban census tracts origins; for a trip from a suburban census
tract to a suburban destination, car mode share was at its highest. Thus, the farther an
origin and destination are from downtown, the more likely car mode share would dominate
as the transportation mode option. Another factor that impacts mode share was income. For
the six census tracts analyzed, the low income areas displayed a notable shift away from
driving after the resiliency scenario, while car mode share remained generally high for the
40


middle and higher income study areas. This suggests that the lower income households are
shifting their transportation mode away from driving to walking, biking, and transit because
the latter options better support their needs when the price of gas drastically increases, and
not necessarily because those transportation options are more convenient, efficient, safe, or
accessible.
To better understand how the multi-modal transportation environment supports
those alternative modes, we explored accessibility to low stress multi-modal transportation
options. Findings revealed that access to low stress alternative modes particularly for
suburban, lower income areas proved to be an important element in supporting more
biking, walking and transit use after the resiliency scenario. This suggests that as a
community, investments in better multi-modal transportation options should be targeted
towards neighborhoods and households that have diminishing options and increased
vulnerability after a disruptive event. Such investments would be particularly well served in
suburban, low-income areas, where household income spent on gas for driving trips is
greater than urban and higher income areas. By targeting biking, walking, and transit
investments to those communities that are most constrained in terms of mobility and
financial resources, and who would likely respond most favorably to improvements to the
multi-modal transportation system, we are better supporting the resiliency and strength of
the community as a whole.
Several limitations should be considered in this research. For the traffic stress
analysis, lack of data about average annual daily traffic or actual speeds along roads limited
the analysis of stress along certain roads. Additionally, the sidewalk data provided by the
City and County of Denver was ten years old and at times did not offer an accurate
understanding of sidewalk presence and condition. In the assignment of traffic stress to
trips, extrapolating from the TAZ to the census tract diminished accuracy of the trips,
41


further exacerbated by the random selection of census tract or neighborhood centroid as
the start and end of each trip. In the development of the mode choice model, it was assumed
that the total of car, transit, walk and bicycle modes would equal100%, which is not
necessarily accurate as some people telecommute and work from home.
Despite these limitations and assumptions, the contribution of this work to
understanding how people will behave under a catastrophic gas event is critical. This
research offers an important approach to valuing multi-modal transportation and to
understanding the latent worth of this infrastructure, even if it is not heavily used today.
Future direction of this research is very promising. By utilizing the mode choice model to
understand where reductions in traffic stress offer significant shifts to these alternative
modes, we can better understand what infrastructure improvements to the current
bicycling, walking, and transit network will facilitate this resiliency. These improvements
can be readily determined by analyzing the traffic stress of streets and trips, thereby
ensuring that a lower stress environment exists through enhancements such as buffered
bicycle lanes or better bus service. Such future applications of this research can be utilized
to connect and improve bicycle, pedestrian, and transit networks, further strengthening
these alternative transportation modes so that they may support the communities that they
serve.
Our work reveals that to build more resiliency into communities and
neighborhoods, policy makers and leaders need to improve accessibility to low stress
alternatives to driving, particularly in areas that possess lower income households and are
farther from the central business district. Increasing the supply of affordable housing in
closer proximity to jobs is another possible solution. By better supporting the more
vulnerable neighborhoods, we are supporting improved resiliency and strength of the
community as a whole. These solutions will strengthen these communities by offering
42


adaptive and alternative transportation choices, supporting the economic and social
strength of cities and towns.
43


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