1. Why do Muslims oppose icons and depictions of people How is.docx

1. Why do Muslims oppose icons and depictions of people?
How is the Ka’ba a symbol of this idea?
2. How or why did calligraphy develop as an art form in Islam?
3. Why is math important in Islamic art?
4. What is the kiswa? Why has it been outlawed by the Saudi
government?
5. When Muslim artists began painting more realistic art
depicting people and animals (despite the prohibition against
it), what was one of the ways that they compromised with
religious scholars/clerics who were opposed to the depiction of
people?
6. How does the Shi’i approach to icons/depictions of people
differ from Sunnis?
7. In the second half of the video, they focus on a ruler named
Mansa Musa. Who was he? Why was his visit to Mecca
important?
8. The video visits a village in Egypt (near Luxor) that is known
for depicting images of hajj-visits on houses. Why is this
interesting? What was one way that one of the artists/experts
explained why this tradition came about?
9. Towards the end of the video, they show several examples of
contemporary art inspired by the Islamic world. Pick one
example and tell me why you thought it was interesting.
Importance of traveler attitudes in the choice of public
transportation to work: findings from the Regional
Transportation Authority Attitudinal Survey
Yasasvi Popuri • Kimon Proussaloglou • Cemal Ayvalik •

Frank Koppelman • Aimee Lee
Published online: 14 April 2011
� Springer Science+Business Media, LLC. 2011
Abstract The commute mode choice decision is one of the most
fundamental aspects of
daily travel. Although initial research in this area was limited to
explaining mode choice
behavior as a function of traveler socioeconomics, travel times,
and costs, subsequent
studies have included the effect of traveler attitudes and
perceptions. This paper extends
the existing body of literature by examining public transit
choice in the Chicago area. Data
from a recent Attitudinal Survey conducted by the Regional
Transportation Authority
(RTA) in Northeastern Illinois were used to pursue three major
steps. First, a factor
analysis methodology was used to condense scores on 23
statements related to daily travel
into six factors. Second, the factor scores on these six
dimensions were used in conjunction
with traveler socioeconomics, travel times, and costs to estimate
a binary logistic
regression of public transit choice. Third, elasticities of transit
choice to the six factors

were computed, and the factors were ranked in decreasing order
of these elasticities. The
analysis provided two major findings. First, from a statistical
standpoint, the attitudinal
factors improved the intuitiveness and goodness-of-fit of the
model. Second, from a policy
standpoint, the analysis indicated the importance of word-of-
mouth publicity in attracting
new riders, as well as the need for a marketing message that
emphasizes the lower stress
level and better commute time productivity due to transit use.
Y. Popuri (&) � K. Proussaloglou � C. Ayvalik
Cambridge Systematics, Inc., 115 South LaSalle Street, Suite
2200, Chicago, IL 60603, USA
e-mail: [email protected]
K. Proussaloglou
C. Ayvalik
F. Koppelman
Midwest System Sciences, Inc., 1122 Hinman Avenue,
Evanston, IL 60202, USA
A. Lee
Strategic and Long-Range Planning, RTA, 175 West Jackson
Blvd, Suite 1550,
Chicago, IL 60604, USA
123

Transportation (2011) 38:643–661
DOI 10.1007/s11116-011-9336-y
Keywords Mode choice � Public transportation � Attitudes and
perceptions �
Factor analysis � Logistic regression � Elasticities
Background and motivation
The choice of commute transportation mode is one of the most
fundamental aspects of
daily travel. Models of mode choice are perhaps as old as
discrete choice modeling theory
itself (Domencich and McFadden 1975). Early research on mode
choice had little or no
acknowledgment of the impact of attitudes on the mode choice
decision. Instead, the focus
was on readily observable travel times, costs, and trip maker
socioeconomics. However,
researchers soon appreciated the need to understand mode
choice as a behavioral process
informed by individual attitudes toward transportation.
One of the first bodies of research in this area was due to
Stopher (1967, 1969), who
recognized that attitudinal data related to comfort, convenience,

and safety of various
transportation modes may add to the predictive power of mode
choice models. However,
Stopher did not find a satisfactory way of quantifying these
variables. Beginning in the mid
1970s, researchers were able to successfully quantify and
include attitudinal data in mode
choice models. Spear (1976) compared models based only on
time and cost with those
including comfort, convenience, safety and reliability measures.
He concluded that such
attitudinal variables significantly improved the explanatory
power of mode choice models.
Recker and Golob (1976) included variables expressing
satisfaction or dissatisfaction with
mode features in their models of mode choice, and found that
the model’s performance was
at least as good as models using time and cost variables.
Research on the impact of
attitudes on transportation choice continued in the 1980s. One
such interesting paper by
Proussaloglou and Koppelman (1989) used attitudes of
commuter rail riders as explanatory
variables in a mode choice model to derive the relative

importance of different attitudes
and to inform service design improvements. Train et al. (1986)
explored the inclusion of
attitudes in econometric models of consumer choice.
More recently, Kuppam et al. (1999) found that the contribution
of attitudinal factors
was greater than that of demographic variables in explaining
mode choice behavior, and
emphasized the need for greater consideration of attitudinal and
preference variables in
travel demand modeling applications. In a study for San Diego’s
transit system, Prouss-
aloglou et al. (2001) and Lieberman et al. (2001) incorporated
attitudes into transit plan-
ning by developing attitudinal market segments and by using
segment-specific explanatory
variables and attitudinal factors within a mode choice model
framework. Golob (2001)
developed joint models of attitude and behavior to explain how
both mode choice and
attitudes regarding the San Diego I–15 Congestion Pricing
Project differ across the pop-
ulation. This study recognized that attitudes and behavior are
interdependent, and there-

fore, need to be analyzed simultaneously. In contrast to Kuppam
et al. (1999), this study
did not find any significant effects of attitude on choice, but
found causal links from choice
behavior to attitudes. Outwater et al. (2003) used stated-
preference and attitude informa-
tion from a survey of San Francisco Bay Area residents to
identify market segments, and
subsequently, to explain mode choice for each market segment.
Their study showed sig-
nificant differences in time/cost tradeoffs across these market
segments, reinforcing the
importance of attitude information in travel modeling. Zhou et
al. (2004) used a structural
equation modeling approach along with cluster analysis to
identify market segments in San
Mateo County, California. Although attitudes were not used as
explanatory variables in
644 Transportation (2011) 38:643–661
123
mode choice models, separate mode choice models for each
market segment were esti-

mated to quantify differences in behavior. Shiftan et al. (2008)
applied a similar approach
to an empirical study for the Utah Transit Authority (UTA).
Johansson et al. (2006)
discussed the importance of attitude and personality traits in
mode choice using a
sequential latent variable model and discrete choice model
estimation.
This paper extends the existing body of literature by examining
the choice to use public
transit in the Chicago area as a function of transportation level
of service, traveler
socioeconomics, and attitudes. Data from a recent Attitudinal
Survey conducted by the
RTA in Northeastern Illinois were used to pursue three major
steps. First, a factor analysis
methodology was used to condense scores on 23 statements
related to daily travel into six
underlying constructs or factors. Second, the factor scores on
these six dimensions were
used in conjunction with traveler socioeconomics, travel times,
and costs to estimate a
binary logistic regression for the choice of public transit. Travel
times and costs for auto

and transit were obtained directly from the Chicago
Metropolitan Agency for Planning
(CMAP) travel demand model for the entire Northeastern
Illinois region. Third, elasticities
were computed for the six factor variables to help rank the
factors in order of importance.
As a side note to the reader, the terms ‘‘public transportation,’’
‘‘public transit’’ and
‘‘transit’’ will be used interchangeably throughout this paper to
represent the three major
transit services in the region: Chicago Transit Authority (CTA)
buses and trains, Metra
commuter rail service, and Pace suburban bus service. In
developing models of public
transportation choice, we do not make a distinction between
these three services for two
reasons. First, a transit commute trip in the region may include
one or more of these
services. Second, the objective of this paper is to understand the
determinants of the choice
of public transportation versus the private automobile.
Therefore, the emphasis is on
broadly defined transit service competing with the automobile.

The remainder of this paper is organized as follows: ‘‘RTA
Attitudinal Survey overview
and data preparation’’ provides a brief overview of the RTA
Attitudinal Survey and
describes the data preparation process. ‘‘Attitudes and factor
analysis’’ section presents
results from the factor analysis of the travel-related statements.
‘‘Choice model method-
ology’’ section elaborates the choice model estimation process,
while ‘‘Model results’’
section presents the empirical results. Finally, ‘‘Summary and
conclusions’’ summarizes
the paper and presents the conclusions.
RTA Attitudinal Survey overview and data preparation
Transit in the Northeastern Illinois region faces both
challenging and growing needs. The
RTA’s Moving Beyond Congestion Strategic Plan (RTA et al.
2007) outlined a variety of
initiatives that seek to maintain, enhance and/or expand the
existing system. However, in
light of constrained resources, difficult decisions need to be
made about future investments.
As a result, the RTA sought to prioritize future investments on
the basis of market needs

and customer input through a comprehensive market analysis,
which had two major
components:
1. Development of a baseline understanding of the regional
travel patterns, and
documentation of the role of transit in serving different
geographic markets; and
2. Analysis of the attitudes and preferences of transit riders and
nonriders to categorize
them into distinct market segments, evaluate existing or
perceived barriers to transit
use, and identify potential target segments.
Transportation (2011) 38:643–661 645
123
The first component relied heavily on the recent CMAP Travel
Tracker Survey, while
the second component was based on an Attitudinal Survey of
riders and nonriders in the
six-county Northeastern Illinois region. This survey is the
principal source of data for the
research presented in this paper, and was conducted between

June and August 2009 via two
separate methods:
1. A Computer-Aided Telephone Interview (CATI) survey was
conducted with a random
sample of transit riders and nonriders. This survey generated
1,392 completed surveys.
2. A web-based survey was conducted to supplement the CATI
records. The sample for
this survey was drawn from respondents of a previous CTA on-
board survey and a
sample of Illinois Tollway users. The web-based survey
generated 897 completed
surveys.
The survey collected information on the following items:
• The socioeconomic attributes of the respondent and the
respondent’s household.
• The most frequent trip in a typical week including the mode
used, the origin and
destination, and the time-of-day of travel.
• Transit captivity in terms of the availability of a private
vehicle for their travel.
• Stated availability of and familiarity with transit services.
• Ratings for 23 statements capturing time sensitivity,
flexibility, travel experience,

safety, reliability, stress, social values, and cost associated with
travel.
• Relative prioritization among transit travel time, frequency of
service, cost of travel,
and information availability using Maximum Differential
Scaling (MaxDiff) experi-
mentation techniques. MaxDiff experimentation involved
providing the respondents
with varying levels of four transit attributes at a time, and then
asking them to choose a
‘‘most important’’ and ‘‘least important’’ for each set. As part
of the RTA Attitudinal
Survey, respondents were given eight sets of such experiments.
Using the ‘‘most
important’’ and ‘‘least important’’ features from the eight
experiments, a statistical
model was estimated to assign a utility value to each level of
each feature. These
utilities were then normalized to a scale of 100 and ranked in
descending order.
• Relative prioritization among transit travel times, frequency,
cost, bus stop features,
and on-board comfort for a proposed premium bus service for
the reverse commute and
suburban travel markets.

This paper studies the choice of public transportation to the
workplace. This endeavor is
meaningful only if public transportation is at least theoretically
available. Therefore, only
those trips that had both transit and highway options in the
CMAP regional travel demand
model were considered for analysis. The home and work
locations from the survey were
each geocoded and associated with a traffic analysis zone (TAZ)
from the CMAP regional
model and the level of service variables for highway and transit
were attached to each
survey record. Nonmotorized trips were excluded from the
analysis sample because these
generally tended to be intrazonal trips for which highway and
transit level of service data
were not available from the CMAP regional model. Figure 1
presents a summary of the
data assembly process.
Table 1 presents the unweighted frequencies of key variables in
the final data set.
Respondents residing in Cook county, which contains the City
of Chicago, constituted 80%
of the data sample, while the five suburban counties accounted
for the remaining 20%. The

sample consisted of respondents over 16 years of age (by
design), with all the major age
cohorts represented in the sample. Full- and part-time workers,
respondents from both
small and large households, and respondents from households
with different levels of
646 Transportation (2011) 38:643–661
123
vehicle ownership and number of workers were included. The
‘‘surplus’’ or ‘‘deficit’’ of
vehicles over workers has been found to be a significant
variable in mode choice, as will be
described in ‘‘Choice model methodology’’ section. Finally, the
sample had a high pro-
portion of transit riders (57%) compared to the observed transit
market share of 14% for all
work trips in the six-county region.
The RTA Attitudinal Survey was weighted to ensure that the
sample proportions
matched the observed proportions from the American
Community Survey (ACS) data for
key household and person-level attributes. Further, the weights

were developed so that the
fraction of trip interchanges between each county pair as a
percentage of total trip inter-
changes in the region matched the CMAP Household Survey
estimates. Finally, the
weights were also designed to match the transit market share in
the sample to the transit
share observed in the CMAP Household Survey. Both the factor
analysis results presented
in ‘‘Attitudes and factor analysis’’ section and the choice model
results discussed in
‘‘Summary and conclusions’’ section incorporate these weights.
Attitudes and factor analysis
As part of the RTA Attitudinal Survey, respondents were asked
to state their level of
agreement or disagreement with 23 statements pertaining to the
following key dimensions
of their day-to-day travel: time sensitivity, flexibility of
schedule, travel experience and
comfort, safety, reliability, stress, social perceptions, and cost.
Each of these statements
was rated on a scale of 0 to 10, with 0 indicating complete
disagreement and 10 reflecting

complete agreement. An average rating of 5.0 indicated that the
respondent was neutral to
the statement.
Table 2 presents a summary of the statements and the average
ratings provided by
transit riders and auto users. There were significantly different
perceptions regarding the
fastest mode to work. Transit riders rated the statement
‘‘Driving is the fastest way to get to
the destination’’ with 4.9 compared to the auto users who had a
very high rating of 8.9.
There were also major differences in respondents’ social
perceptions pertaining to transit.
Specifically, transit riders had much higher ratings of 8.8 and
6.4, respectively, for the
statements ‘‘I am the kind of person who rides transit’’ and
‘‘My family and friends
1. All Data Records
from RTA Attitudinal
Survey
N = 2,289
2. Select commute Trips
N = 1,335

3. Select commute trips
where both transit and
highway are available,
as per CMAP regional
model.
N = 1,095
4. Select trips for people
with valid ratings for all
attitude statements
N = 887
5. Select trips where
either transit or auto
was chosen, leaving out
short non-motorized
trips
N = 868
Fig. 1 Data preparation
Transportation (2011) 38:643–661 647
123
Table 1 Sample characteristics
Frequency Percent
Total sample size 868 100

Home county
Cook 695 80
DuPage 71 8
Lake 43 5
McHenry 15 2
Kane 24 3
Will 20 2
Age group
16–24 40 5
25–34 194 22
35–44 193 22
45–54 223 26
55–64 162 19
65–74 30 3
75 or older 8 1
Missing 18 2
Gender
Male 408 47

Female 460 53
Employment status
Employed full-time 780 90
Employed part-time 88 10
Household size
One person 201 23
Two people 316 36
Three people 130 15
Four people 135 16
Five people 60 7
Six or more people 26 3
Number of household vehicles
No vehicles 109 13
One vehicle 332 38
Two vehicles 298 34
Three or more vehicles 129 15
Number of household workers
No workers 14 2

One worker 339 39
Two workers 406 47
Three or more workers 109 13
Commuter type
Traditional commuter (suburbs to city) 101 12
City commuter (city to city) 645 74
Reverse commuter (city to suburbs) 40 5
648 Transportation (2011) 38:643–661
123
Table 2 Travel-related statement ratings—transit and auto users
Travel-related statement Transit users
N = 491
Auto users
N = 377
All
N = 868
Mean SD Mean SD Mean SD
The fastest way to get to work/school is driving 4.9 4.0 8.9 2.1
8.3 2.9

If it would save time, I would change my form of travel 6.5 3.2
6.6 3.5 6.6 3.4
More than saving time, I prefer to be productive
when traveling
5.4 3.0 5.7 3.2 5.7 3.1
During the day, I often make trips to a wide variety
of locations
3.8 3.3 4.8 3.5 4.7 3.5
I often need to change my daily travel plans at a
moment’s notice
3.5 3.2 4.4 3.7 4.2 3.6
I often make a lot of stops along the way to work/school 1.8 2.5
3.1 3.2 2.9 3.1
Privacy is important to me when I travel 5.2 3.0 5.5 3.2 5.5 3.2
I am willing to walk a few minutes to get to and
from transit
7.7 2.5 7.0 2.9 7.1 2.8
I don’t mind transferring between buses and trains 6.0 3.2 5.2
3.2 5.3 3.2
It is important to be able to control heat and air
conditioning when I travel
5.4 2.7 6.7 2.9 6.5 2.9

I feel safe walking near my work/school location 8.0 2.5 7.8 2.8
7.8 2.8
I feel safe on a bus or train 7.0 2.6 6.5 2.6 6.6 2.6
When I drive, I worry about getting into an accident 4.3 3.3 4.4
3.4 4.4 3.4
As long as I am comfortable, I can tolerate delays 4.8 3.0 5.7
3.0 5.5 3.0
Riding transit is more reliable than driving during
bad weather
7.6 2.7 6.4 3.0 6.6 3.0
Predictable travel time is more important
than a faster trip
7.4 2.3 6.8 2.5 6.9 2.5
To avoid highway congestion, I leave earlier
or later than usual
6.4 3.0 7.4 3.0 7.3 3.0
Riding transit is less stressful than driving
on congested highways
8.0 2.6 7.5 2.8 7.6 2.7
I am the kind of person who rides transit 8.8 1.9 4.4 3.2 5.1 3.4
My family and friends typically use public
transportation

6.4 2.9 4.5 3.2 4.7 3.2
Regardless of cost, I choose the fastest way to travel 4.3 3.1 6.5
3.0 6.2 3.1
Improving transit is as good a use of tax dollars
as improving roads
8.6 2.0 7.8 2.4 7.9 2.4
Increasing fares is necessary to avoid any cuts
in transit service
5.0 3.1 5.4 2.9 5.4 2.9
Table 1 continued
Frequency Percent
Suburban commuter (suburb-to-suburb) 63 7
Missing 19 2
Transit user?
Yes 491 57
No 377 43
Transportation (2011) 38:643–661 649
123
typically use public transportation,’’ compared to scores of only

4.4 and 4.5, respectively,
for auto users.
Transit riders appeared to be much less likely to pursue
intermediate stops en route to
work compared to auto users. Transit riders’ rating on the
statement ‘‘I often make a lot of
stops along the way to work/school’’ was only 1.8 compared to
3.1 for auto users. An
interesting finding relates to the scores on the statement
‘‘Improving transit infrastructure is
as good a use of tax dollars as improving roads.’’ Although
transit riders expectedly had a
higher score than auto users, both groups had notably high
ratings of 8.6 and 7.8,
respectively. This is an important finding with respect to the
public’s support for transit
improvements.
While the 23 statements presented in the table capture
information on various aspects of
daily travel, using all of these statements as variables in a
choice model is not advisable for
two reasons. First, there is a high degree of correlation between
these statements. Second,
from the standpoint of model parsimony, using 23 variables is

not desirable. To condense
the information captured by these 23 variables into a more
manageable and uncorrelated
set of variables, a factor analysis methodology was adopted.
Factor analysis assumes that
the ratings on the 23 statements are really ‘‘produced’’ by some
underlying and unobserved
attitudes (Lehmann et al. 1998). The basic form of the factor
analysis model is as follows:
Xji ¼
Xm
k¼1
kjk Fki
� �
þ eji; 8j ¼ 1; 2; . . .; J and 8i ¼ 1; 2; . . .; N ð1Þ
where, Xji is the rating on statement j for person i; Fki is the
value of the kth factor for the
person i; kjk is the relation of the jth variable with the kth
common factor, also known as the
loading; and eji represents the error term. The model in (1)
assumes that there are
J statements, m factors, and N observations in the sample. It
must be noted that the factor
scores, Fki, are not observed. Factor analysis computes both the
factor scores and the
loadings so as to maximize the information maintained from the
original statements.

The first step in factor analysis involved the computation of
pairwise Pearson cor-
relation coefficients between the 23 statements. The factor
loadings were then estimated
using principal component analysis (Lehmann et al. 1998). Six
factors were retained
based on a combination of professional judgment and
percentage of total variance in the
original variables explained by the factors. Figure 2, popularly
known as the Scree Plot,
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
%

V
a
ri
a
n
c
e
E
x
p
la
in
e
d
b
y
E
a
c
h
F
a
c
to

r
Factor
Fig. 2 Scree plot from principal components analysis
650 Transportation (2011) 38:643–661
123
shows the percentage of total variance explained by each
additional factor. The incre-
mental variance explained was very low beyond six factors. To
enable easy interpreta-
tion, the factors were ‘‘rotated’’ using the Varimax technique
(Kim and Mueller 1991)
Table 3 Rotated factor loadings for travel-related statements
Travel-related statement Factor 1 Factor 2 Factor 3 Factor 4
Factor 5 Factor 6
Riding transit is more reliable than driving
during bad weather
0.66 0.10 0.08 0.16 0.11 0.09
Improving transit is as good a use of tax
dollars as improving roads
0.66 -0.02 -0.12 0.09 -0.04 0.05

Riding transit is less stressful than driving on
congested highways
0.65 -0.15 0.04 0.03 0.11 0.28
If it would save time, I would change my form
of travel
0.45 0.02 0.04 0.19 0.00 -0.33
More than saving time, I prefer to be
productive when traveling
0.44 0.18 0.31 -0.22 0.09 -0.20
Predictable travel time is more important than
a faster trip
0.37 0.36 0.05 -0.09 0.33 0.30
Privacy is important to me when I travel -0.12 0.68 0.02 0.12
0.06 -0.25
It is important to be able to control heat and air
conditioning when I travel
-0.06 0.65 -0.03 -0.10 -0.08 -0.06
Regardless of cost, I choose the fastest way to
travel
0.04 0.59 0.14 -0.01 -0.14 0.19
To avoid highway congestion, I leave earlier
or later than usual

0.15 0.50 0.11 -0.14 0.26 0.11
As long as I am comfortable, I can tolerate
delays
0.13 0.49 -0.09 0.46 -0.21 0.24
When I drive, I worry about getting into an
accident
0.32 0.40 0.04 0.34 -0.12 -0.16
I often need to change my daily travel plans at
a moment’s notice
-0.03 0.04 0.86 0.10 0.02 0.01
During the day, I often make trips to a wide
variety of locations
0.00 -0.02 0.81 0.05 0.06 0.05
I often make a lot of stops along the way to
work/school
0.09 0.11 0.73 -0.08 -0.17 -0.10
I don’t mind transferring between buses and
trains
0.07 0.07 -0.02 0.73 0.19 0.18
I am willing to walk a few minutes to get to
and from transit
0.18 -0.22 0.12 0.65 0.13 -0.01

My family and friends typically use public
transportation
0.12 0.05 -0.06 0.26 0.66 0.05
I am the kind of person who rides transit 0.41 -0.36 0.04 0.27
0.58 0.02
The fastest way to get to work/school is
driving
-0.22 0.25 0.06 -0.22 -0.42 0.20
Increasing fares is necessary to avoid any cuts
in transit service
0.37 -0.04 0.02 0.25 -0.61 -0.03
I feel safe walking near my work/school
location
0.01 0.12 -0.05 0.11 0.02 0.66
I feel safe on a bus or train 0.32 -0.35 0.03 0.20 -0.01 0.59
Transportation (2011) 38:643–661 651
123
so that each variable loaded heavily onto a single factor. This
helped in the clear
identification of variables that measured each factor, and

minimized the overlap across
factors. Once the factor loadings kjk were obtained, the factor
scores Fki were computed
using the relationship in Eq. (1).
Table 3 presents the rotated factor loadings from the six-factor
solution. For each factor,
the statements with the highest loadings are highlighted. The
first factor captures the need
for reliable and stress-free commute. The second factor
represents the degree of intrinsic
need for privacy and comfort. The third factor captures the
extent of dynamism in the work
schedule and the complexity of trip-making behavior in terms of
number of intermediate
stops and need to pursue activities at multiple locations. The
fourth factor represents the
trip maker’s tolerance to the out-of-vehicle components of a
transit trip. The fifth factor
reflects the trip maker’s general attitude toward public
transportation. Finally, the sixth
factor reflects the perceived safety of the travel environment.
The attitudinal factors uncovered as part of this study were
compared to those reported
by other similar studies, specifically, Lieberman et al. (2001),
Proussaloglou et al. (2001),
and Shiftan et al. (2008). Table 4 provides a comparative
summary of attitudinal factors

from the current study and relevant past studies. Reliability of
travel, avoidance of stress,
privacy and comfort, and sensitivity to safety were the common
factors that emerged from
this paper as well as from the past studies. Proussaloglou et al.
(2001) and Lieberman et al.
(2001) also found concern for the natural environment as a key
attitudinal construct, a
finding supported by Shiftan et al. (2008). The RTA Attitudinal
Survey did not include
statements capturing the respondents’ concern for the natural
environment. The RTA
study, however, included statements pertaining to the social
perceptions of the respondents
and their immediate friends and family on the importance of
public transportation. Will-
ingness to use public transit was an important attitudinal
construct uncovered by the
analysis presented in this paper, a finding supported by Shiftan
et al. (2008).
Six standardized factor scores were computed for each
respondent in the data sample.
These scores were then used as explanatory variables in the
choice model estimation

described in ‘‘Choice model methodology’’ section.
Choice model methodology
A binary logistic regression methodology was used to model the
choice between public
transportation and the private automobile for the work trip.
Logistic regression is one of the
most commonly used statistical techniques in marketing
research and travel demand
forecasting (Ben-Akiva and Lerman 1985).
The basic principle behind the binary logistic regression is that
the trip maker associates
a certain utility to each transportation mode. These utilities are
not observed by the analyst
and are implicit to the decision-making process. Let Ui,PA
represent the utility that trip
maker i associates with the private automobile, and Ui,PT be
the utility associated with
public transportation. The utility of the public transportation
mode, Ui,PT, consists of a
deterministic component Vi,PT, and a random unobserved error
term ei,PT.
Ui;PT ¼ Vi;PT þ ei;PT ð2Þ
Similarly, the utility of the private automobile can be written as
follows:
Ui;PA ¼ Vi;PA þ ei;PA ð3Þ

652 Transportation (2011) 38:643–661
123
Also, since only the differences in the utilities matter and not
the absolute values
themselves, we assume Vi,PA = 0.
The deterministic term Vi,PT was modeled as a function of
three sets of attributes: first,
the socioeconomic characteristics of the trip maker, represented
by the column vector Si;
second, the in- and out-of-vehicle times, and costs for transit
and auto, represented by the
column vector Ti; and third, the attitudes of the trip maker, as
measured by the six factor
scores described in ‘‘Attitudes and factor analysis’’ section,
represented by the column
vector Fi. Therefore,
Vi;PT ¼ a þ b0Si þ c0Ti þ u0Fi ð4Þ
where, a is the alternative-specific bias constant; b, c, and u are
column vectors of
parameters corresponding to each constituent variable in Si, Ti,
and Fi, respectively.
The parameter estimates are the output of the binary logistic
regression methodology.
The trip maker i will choose public transportation over the
private automobile if the

utility associated with public transportation exceeds that
associated with the private
automobile:
Ui;PT [ Ui;PA ð5Þ
From Eqs. (2), (3), (4), and (5), the equation above can be
restructured as:
a þ b0Si þ c0Ti þ u0Fi þ ei;PT [ ei;PA ð6Þ
Table 4 Attitudinal factors from previous studies
Paper Major attitudinal constructs
Lieberman et al. (2001)
and Proussaloglou et al. (2001)
Factor 1: Need for flexibility and speed
Factor 2: Concern about natural environment
Factor 3: Sensitivity to personal travel experience
Factor 4: Sensitivity to personal safety
Factor 5: Sensitivity to travel time
Factor 6: Sensitivity to transportation costs
Factor 7: Sensitivity to crowds
Factor 8: Sensitivity to stress
Shiftan et al. (2008) Factor 1: Desire to help improve air quality

Factor 2: Desire for productivity and reliability
Factor 3: Sensitivity to time
Factor 4: Sensitivity to safety and privacy
Factor 5: Need for fixed schedules
Factor 6: Sensitivity to stress and comfort
Factor 7: Willingness to use transit
This paper Factor 1: Need for reliable and stress-free commute
Factor 2: Need for privacy and comfort
Factor 3: Dynamic work schedule and complexity of trips
Factor 4: Tolerance to walking and waiting
Factor 5: Attitude to importance of public transportation
Factor 6: Perceived safety of travel ambience
Transportation (2011) 38:643–661 653
123
The probability that the trip maker i will choose public
transportation is given by:
Pi PTð Þ¼ P a þ b0Si þ c0Ti þ u0Fi þ ei;PT [ ei;PA
� �

; ð7Þ
or
Pi PTð Þ¼ P ei;PT � ei;PA [ � a þ b0Si þ c0Ti þ u0Fið Þ
� �
ð8Þ
The error terms ei,PT and ei,PA are assumed to be independent
and identically distributed,
with a Gumbel distribution (Ben-Akiva and Lerman 1985). This
assumption results in the
following functional form for the probability of trip maker i
choosing public transportation
over private automobile:
Pi PTð Þ¼
exp a þ b0Si þ c0Ti þ u0Fið Þ
1 þ exp a þ b0Si þ c0Ti þ u0Fið Þ
ð9Þ
It follows that the probability of trip maker i choosing private
automobile is:
Pi PAð Þ¼
1
1 þ exp a þ b0Si þ c0Ti þ u0Fið Þ
ð10Þ
The parameters a, b, c, and u are estimated using a log-
likelihood maximization

approach (Lehmann et al. 1998), where the probabilities of the
actual choices c made by
each trip maker in the sample are multiplied to obtain the
likelihood function. The loga-
rithm of this function is then maximized with respect to a, b, c,
and u to obtain the
parameter estimates. This methodology is summarized below:
L ¼
YN
i¼1
PiðcÞ; c 2 PA; PTf g ð11Þ
In L ¼
XN
i¼1
In Pi cð Þð Þ; c 2 PA; PTf g ð12Þ
maxa;b;c; and u In L ¼
XN
i¼1
In Pi cð Þð Þ
!
; c 2 PA; PTf g ð13Þ
Two separate logistic regressions were estimated for the
purpose of this paper. The
dependent variable, reflecting the choice of public transit, had a
value of 1 if the person

chose public transportation, and 0 if the trip maker chose the
private automobile. The first
regression used the trip maker’s socioeconomic attributes (Si)
along with travel times and
costs of public transportation and private automobile modes
(Ti). The second regression
added the factor scores (Fi) to the first regression. The results
of the model estimation are
discussed in the next section.
It must be noted that a sequential estimation process where the
factor scores are gen-
erated using a separate model and are subsequently incorporated
into a discrete choice
model, results in inconsistent and inefficient estimates of the
parameters a, b, c, and u.
Consistent estimates of the parameters can be obtained using a
simultaneous estimation
approach, as proposed by Bolduc et al. (2008) or Ben-Akiva et
al. (2002). This paper does
not correct the parameter estimates obtained from the sequential
estimation process. The
authors will conduct a simultaneous estimation of parameters in
a future research paper.
Further, because the data were weighted to ensure that the
sample proportions matched the
observed proportions from ACS data for key household and
person-level attributes, a logit

estimation based on weights may result in inefficient estimates
of a, b, c, and u.
654 Transportation (2011) 38:643–661
123
Model results
Table 5 shows the results from the two model specifications
discussed above. A log-
likelihood test comparing the two models clearly bears out that
underlying dimensions of
travel perception and behavior have a key role to play in
determining the choice of public
transportation. Details of the test are as follows:
v2 ¼�2 � LLModel 1 � LLModel 2ð Þ ð14Þ
where, LLModel 1 and LLModel 2 represent the log-likelihood
values for Models 1 and 2,
respectively, as detailed in Table 5.
v2 ¼�2 � �177:795 þ 117:177ð Þ¼ 121:236 ð15Þ
The critical value of the v2 statistic with six restrictions, for the
six factor variables, at
the 95% confidence level is 12.59, which is much smaller than
the v2 test statistic com-

puted in (15). Therefore, we can safely reject the hypothesis
that the factor score variables
are not significant in explaining public transportation choice.
Both models indicated a strong effect of the ‘‘surplus’’ of
vehicles over workers on
transit choice. Specifically, both models indicated that as the
number of available vehicles
exceeds the number of workers in the household, the probability
of choosing transit falls
steeply. Both Model 1 and Model 2 indicated a strong
preference for transit among city-to-
city commuters. This is a fairly intuitive finding, because of the
high parking costs within
the city, as well as better familiarity with the use of transit
services for the city commuters.
However, Model 1 indicates that the suburb-to-city commuters
have a higher probability
of taking transit, all else being equal, than city-to-city
commuters. This is a counter-
intuitive finding given the competitiveness of public transit
within the city. The inclusion
of factor scores in Model 2 appears to rectify this issue, with
the preference towards transit
being much lower for suburb-to-city commuters than for city-to-

city commuters.
There were significant differences in the sensitivities to level of
service variables
implied by Models 1 and 2. Model 1 had statistically
insignificant coefficients for transit
in-vehicle time, transit fare and auto operating costs at the 90%
confidence level. More
importantly, this model implied an extremely high sensitivity to
out-of-vehicle time as
compared to the sensitivity to in-vehicle time. The model
implied that, ceteris paribus, a
minute of out-of-vehicle time was as onerous as 7.5 min of in-
vehicle time.
Model 2, on the other hand, had a statistically significant
coefficient for in-vehicle time
at the 90% confidence level. Although the transit fare and auto
operating costs continued to
be insignificant, the level of significance was markedly better
than for Model 1. The second
model also indicated a more reasonable sensitivity to out-of-
vehicle time relative to in-
vehicle time, implying that, ceteris paribus, a minute of out-of-
vehicle time was as onerous
as roughly 1.9 min of in-vehicle time.
Model 2 had a slightly higher estimate of implied value of time

for commute trips as
compared to Model 1. The Bureau of Labor Statistics reported
average weekly wages that
ranged between a low of $789 for McHenry County and a high
of $1,197 for Lake County
(Bureau of Labor Statistics 2010). For a typical 40-h work
week, these numbers translate to
$19.7 and $29.9 per hour, respectively. The values of time
implied by Models 1 and 2 were
between one-third and one-half of the hourly wage numbers,
which seemed reasonable.
A study of the implied elasticity of transit choice probability to
changes in level of
service measures from the two models yielded several
interesting observations. The
elasticities were computed for every trip maker in the
estimation sample using the values
Transportation (2011) 38:643–661 655
123
T
a
b

le
5
B
in
a
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–
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.
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iv
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ic
s
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f
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se
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a
ti
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s
(N
)
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8

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1
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In
it

ia
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k
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li
h
o
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ly
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9
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in
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c
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-
1
7
7
.7
9
5
-
1
1

7
.1
7
7
656 Transportation (2011) 38:643–661
123
T
a
b
le
5
c
o
n
ti
n
u
e
d
D
e
p
e
n

.
e
rr
o
r
z-
st
a
t
P
se
u
d
o
R
-s
q
u
a
re
d
w
.r
.t

.
c
o
n
st
a
n
ts
o
n
ly
m
o
d
e
l
0
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1
0
9
0
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4
5

8
T
im
e
a
n
d
c
o
st
se
n
si
ti
v
it
ie
s
R
a
ti
o
o
f
tr
a

n
si
t
o
u
t-
o
f-
v
e
h
ic
le
to
in
-v
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h
ic
le
ti
m
e
se
n

si
ti
v
it
y
7
.4
9
1
.8
5
Im
p
li
e
d
V
a
lu
e
o
f
T
im
e

(d
o
ll
a
rs
p
e
r
h
)
8
.6
8
1
1
.6
1
Transportation (2011) 38:643–661 657
123
of the level of service variables and the predicted probability of
transit choice. A weighted
average of these individual elasticities was then computed for

the entire data sample. These
elasticities are shown in Table 6, along with the weighted mean
values of the level of
service variables.
Model 1 indicated a very low own-elasticity of transit choice to
transit in-vehicle time
as compared to the cross-elasticity to auto travel time. This
implies that all else being
equal, a 1% increase in transit in-vehicle time will have a much
smaller impact on the
choice of transit than a 1% decrease in auto travel times. Model
2, however, appears to
show a higher own-elasticity to transit in-vehicle time compared
to the cross-elasticity to
auto travel time. While the relative magnitudes of transit and
auto travel time elasticities
are not exactly known, one would expect the own-elasticity of
transit choice probability to
transit in-vehicle time to be higher than the cross-elasticity of
transit choice probability to
auto in-vehicle time. In this sense, Model 2 appears to be more
intuitive than Model 1.
From a policy standpoint too, using Model 1 instead of Model 2
can lead one to estimate a

much lower change in transit ridership due to service cuts or
conversion from express to
local service.
Similar observations can also be made in relation to the
elasticity of transit choice to
transit fares and costs. Model 2 indicates that the fare
elasticities, while still very much in
the inelastic zone, are much higher than those implied by Model
1. Using Model 1 instead
of Model 2 for evaluating changes to fare policy can
accordingly imply smaller changes in
projected ridership.
Overall, it appears that explicitly accounting for traveler
attitudes through the inclusion
of factor scores improves the intuitiveness of the model. In
addition, there is a marked
improvement in goodness-of-fit measures for Model 2 as
compared to Model 1 (see
Table 5). The pseudo R-squared measure with respect to the
constants-only model,
increased from 0.31 for Model 1 to 0.55 for Model 2, indicating
an improvement of over
75%.

The coefficients of the factor variables in Model 2 were all
significant at the 90%
confidence level, and had intuitive signs. Factor 5, which
captures the trip maker’s attitude
toward transit and its importance to society, had the most
positive coefficient. Factor 1,
indicating the need for a reliable, stress-free, and productive
commute, had the next highest
coefficient. Tolerance to walking and waiting, represented by
Factor 4, also had a strong
positive sign.
Among the factors that reduced the likelihood of taking transit,
the need for privacy and
comfort represented by Factor 2 had the highest coefficient.
Factor 3, which represents the
extent of dynamism in the work schedule and the complexity of
trip-making in terms of the
Table 6 Elasticity of transit choice to transit and auto level of
service
Level-of-service variable Weighted
sample
mean
Model 1 Model 2
Elasticity Std. error z-stat Elasticity Std. error z-stat

Transit in-vehicle time (min) 43 -0.4819 0.543 -0.890 -1.2456
0.697 -1.790
Transit out-of-vehicle
time (min)
34 -2.9278 0.563 -5.200 -1.8706 0.587 -3.190
Transit fare (cents) 252 -0.1928 0.426 -0.450 -0.3704 0.519 -
0.710
Auto travel time (min) 42 0.9024 0.404 2.230 0.9623 0.498
1.930
Auto operating cost (cents) 206 0.0830 0.463 0.180 0.4780
0.578 0.830
658 Transportation (2011) 38:643–661
123
number of intermediate stops and need to pursue activities at
multiple locations, had the
next most negative impact. Perceived safety of the trip maker’s
ambience had the lowest
negative effect on transit choice.
To better quantify the effect of changes in attitudes on transit
choice, elasticities were
computed for each factor score. The elasticities of factor scores

are presented in decreasing
order of magnitude in Table 7. Since the factor scores are
standardized, their means across
the sample are close to 0 and their standard deviations close to
1.
Table 7 reinforces the observations made previously on the
relative impact of various
factors. Trip makers whose family and friends ride transit
regularly or who have a positive
perception toward transit themselves, appear to be the most
likely to choose transit. Despite
the seemingly obvious nature of this statement, it points toward
an interesting ‘‘networking
effect’’ for transit choice. It also appears that trip makers who
place a premium on reliable,
stress-free and productive commute are more likely to use
transit. This provides an insight
into the key messages that transit operators could consider in
their marketing campaigns.
Finally, although models with factor scores as explanatory
variables are not intended for
forecasting purposes, the evaluation of elasticities of transit
market share to travel times
and fares suggests that ignoring the impact of attitudes can lead

one to estimate a much
lower change in transit ridership due to service cuts or fare
changes.
Summary and conclusions
This paper demonstrated the importance of understanding trip
maker attitudes and
accounting for their impact on the choice of public
transportation to work. An important
caveat is in order here. Just as attitudes toward travel affect the
daily mode choice
behavior, the choice of public transportation could in turn affect
attitudes in the longer
term. We recognize the feedback between attitudes and behavior
but such an analysis
would require a more elaborate experimental set up than what
was developed for the RTA
Attitudinal Survey.
A factor analysis of the 23 statements from the RTA Survey
indicated the presence of
six broad constructs. These included the need for reliable and
stress-free commute, need
for privacy and comfort, the complexity of trip-making
behavior, tolerance to waiting and

walking, general attitude toward public transportation, and
finally, the perceived safety of
the travel environment. Reliability of travel, avoidance of
stress, privacy and comfort, and
sensitivity to safety were the factors that were common to this
study as well as similar
studies in the past (Proussaloglou et al. 2001; Lieberman et al.
2001; Shiftan et al. 2008).
Table 7 Elasticity of transit choice to factor scores
Factor Effect on
transit
choice
Weighted
mean
Elasticity Std. error z-stat
5. Attitude to importance of public transportation : 0.033 0.196
0.045 4.38
1. Need for reliable and stress-free commute 0.019 0.058 0.021
2.83
4. Tolerance to walking and waiting 0.009 0.037 0.017 2.20
6. Perceived safety of travel ambience ; 0.012 -0.016 0.012 -
1.40
3. Dynamic work schedule and
complexity of trips

0.010 -0.051 0.019 -2.61
2. Need for privacy and comfort 0.024 -0.095 0.027 -3.52
Transportation (2011) 38:643–661 659
123
Comparison of binary logistic regressions for the choice of
public transit with and
without attitudinal constructs yielded several insights. First, the
explicit treatment of
factors generated more intuitive estimates of the importance of
level of service charac-
teristics. The model with factor scores provided a more
reasonable ratio of sensitivities to
out-of-vehicle and in-vehicle times. Second, ignoring the impact
of attitudes could lead one
to estimate a much lower change in transit ridership due to
service cuts or conversion from
express to local service. Third, the model with factor scores
generated higher elasticities to
transit fares. Using the model without factor scores for
evaluating fare policy changes can
accordingly suggest a lower expected change in ridership.

Finally, the addition of factor
scores improved the goodness-of-fit of the choice model by
75%.
The choice model with factor scores helped identify a pecking
order of important
determinants of transit choice. The way transit is perceived by
the trip makers themselves
or by their immediate friends and family could have a major
impact on the choice of public
transportation. From a policy standpoint, this could mean that
an incentive program that
subsidizes current transit riders for inducing their friends to
ride transit could be effective
in increasing transit ridership. The authors are cognizant of the
operational and techno-
logical challenges of such a program. A small-scale experiment
to study the feasibility of
such a program could be very helpful.
Finally, another important finding is that traveler preferences
and behavior are affected
by the need for a reliable, stress-free and productive commute.
These are attributes where
transit often has an advantage over the automobile especially in
metropolitan areas where

fixed route transit service competes with autos facing congested
highway conditions.
Marketing campaigns should therefore emphasize all three of
these attributes to appeal to
commuters, stress the competitive advantages of transit, and
affect their mode choice
behavior.
Acknowledgments The authors would like to thank RTA for its
financial and managerial support on the
Market Analysis project, which is the source of the content and
data presented in this paper. The authors also
thank CMAP for providing highway and transit network skim
data from the regional travel demand model.
The authors acknowledge the efforts of Laurie Wargelin of Abt
SRBI, and Greg Spitz and Margaret
Campbell of RSG in administering the CATI and on-line
surveys, respectively.
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Author Biographies
Yasasvi Popuri is a Senior Associate in the Chicago office of
Cambridge Systematics with interest and
experience in the fields of Discrete Choice Modeling, Marketing
Research, and Transportation Demand
Forecasting. He is a Young Member of the Transportation
Research Board (TRB) Committee on
Transportation Demand Forecasting.

Kimon Proussaloglou is a Principal in the Chicago office of
Cambridge Systematics specializing in urban
and intercity travel demand analysis and forecasting. Over the
past two decades he has used Discrete Choice
Modeling and Marketing Research techniques to advise US
Federal and State transportation agencies on
important policy issues.
Cemal Ayvalik is an Associate in the Chicago office of
Cambridge Systematics with experience in the fields
of Marketing Research, Geographic Information Systems and
Transportation Demand Forecasting. He has
worked on several major transit market analysis projects in
Chicago over the past decade.
Frank Koppelman , professor emeritus of civil and
environmental engineering at Northwestern University
and founding principal of Midwest System Sciences, has been
active in academic and professional
education, research and consulting in travel behavior for more
than 35 years. Dr. Koppelman is the first
recipient of the Lifetime Achievement Award of the
International Association for Travel Behavior Research.
Aimee Lee is the Division Manager for Strategic Planning and
Policy at the Regional Transportation
Authority (RTA) in Chicago. She served as the Project Manager
for RTA on the recently concluded Market
Analysis study.
Transportation (2011) 38:643–661 661
123

ortStratPlan_FinalReplrez_031207.pdf
ortStratPlan_FinalReplrez_031207.pdf
Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.
c.11116_2011_Article_9336.pdfImportance of traveler attitudes
in the choice of public transportation to work: findings from the
Regional Transportation Authority Attitudinal
SurveyAbstractBackground and motivationRTA Attitudinal
Survey overview and data preparationAttitudes and factor
analysisChoice model methodologyModel resultsSummary and
conclusionsAcknowledgmentsReferences

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