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This study analyzes the satisfaction of the Nevadans with respect to their highway transportation system and the corresponding expenditures of Nevada Department of Transportation (NDOT). A survey questionnaire was designed to capture the opinions of the Nevadans (customers) about a number of characteristics of their transportation system. Data from the financial data warehouse of the NDOT was used to evaluate expenditures. Multinomial probit models were estimated to study the correlations between customers’ opinion and the government expenditures in transportation. The results indicate the customer satisfaction is decreasing with respect to traffic safety throughout Northwestern and Southern Nevada highways. In addition, users of Northwestern highways are more likely to be satisfied, compared to their counterparts, with increasing construction spending to reduce the time taken to complete construction projects. In Southern Nevada highways, customers’ satisfaction increases with the expenditures associated with reduction of congestion. These insights are examples of the conclusions that were obtained as a consequence of simultaneously considering customer satisfaction and the corresponding expenditures in transportation.
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Research Article
Highway Expenditures and Associated Customer Satisfaction:
A Case Study
Alexander Paz,1Hanns de la Fuente-Mella,2Ashok Singh,3
Rebecca Conover,1and Heather Monteiro3
1Department of Civil and Environmental Engineering, University of Nevada, Las Vegas, 4505 S. Maryland Parkway,
P.O. Box 454015, Las Vegas, NV 89154, USA
2Facultad de Ciencias Econ´
omicas y Administrativas, Ponticia Universidad Cat´
olica de Valpara´
ıso, Avenida Brasil 2830,
2340031 Valpara´
ıso, Chile
3William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas, 4505 Maryland Parkway, Box 6021,
Las Vegas, NV 89154-4015, USA
Correspondence should be addressed to Hanns de la Fuente-Mella; hanns.delafuente@ucv.cl
Received  January ; Accepted  March 
Academic Editor: Roberto Dominguez
Copyright ©  Alexander Paz et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
is study analyzes the satisfaction of the Nevadans with respect to their highway transportation system and the corresponding
expenditures of Nevada Department of Transportation (NDOT). A survey questionnaire was designed to capture the opinions of the
Nevadans (customers) about a number of characteristics of their transportation system. Data from the nancial data warehouse
of the NDOT was used to evaluate expenditures. Multinomial probit models were estimated to study the correlations between
customers’ opinion and the government expenditures in transportation. e results indicate the customer satisfaction is decreasing
with respect to trac safety throughout Northwestern and Southern Nevada highways. In addition, users of Northwestern highways
are more likely to be satised, compared to their counterparts, with increasing construction spending to reduce the time taken to
complete construction projects. In Southern Nevada highways, customers’ satisfaction increases with the expenditures associated
with reduction of congestion. ese insights are examples of the conclusions that were obtained as a consequence of simultaneously
considering customer satisfaction and the corresponding expenditures in transportation.
1. Introduction
Customer satisfaction surveys help reveal customer desires
and preferences and have been widely used by private
industries. e private sector typically allocates signicant
resources to learn what their customers want in order to
maximize prots, increase shareholder return, and gain
competitive advantage. is approach is rarely used by public
entities, even though residents are the main shareholders of
public services and deserve to be considered [].
Despite the immense amount of cost and time allocated
to public projects, the public sector has several reasons for
its lack of interest in customer input. ese reasons include
the monopoly these departments have on service regardless
of public support, laws dictating the department’s responsibil-
ities, and an “accountability to elected ocials,” according to
Sorel[].Inrecentyears,however,therehasbeenanincreased
desire to include the public in the decision-making process,
based on the desire to “build community, generate support,
agreement, and momentum for public actions, remediate
democratic and citizenship decits, address complex gover-
nanceproblems,andtakeadvantageoftransformationsin
the expectations and capacities of ordinary people”. Unfor-
tunately, the public does not have signicant condence in
government entities. According to Kline [], a Gallup survey
from  showed that
90 percent of respondents believed that people in
government waste a lot of money we pay in taxes,
and that 66 percent of respondents believed that
while the American system of government is good,
thepeoplerunningitareincompetent.’[2]
Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2016, Article ID 4630492, 9 pages
http://dx.doi.org/10.1155/2016/4630492
Mathematical Problems in Engineering
Without satisfaction surveys, problems such as these may
never be revealed [].
Customer satisfaction surveys have been used for a long
time to measure public opinion in industry. However, they
are a time consuming and expensive investment. Because
of this, it is important that the results of these surveys are
interpreted correctly and the results eectively integrated into
business operations. If customer input is deemed signicant
to an industry’s denition of success, it is important that input
is sought oen, that decision makers are constantly aware of
the eects of their decisions on the customer [], and that
the customer is made aware of those decisions that will aect
them [].
In , the Institute of Transportation Engineers (ITE)
and the Federal Highway Administration (FHWA) led a
national dialogue on transportation operations with state
Departments of Transportation (DOTs) across the country
to develop a denition of success and a list of perfor-
mance indicators, which included results from customer
satisfaction surveys []. In , the National Cooperative
Highway Research Program (NCHRP) and the Transporta-
tion Research Board (TRB) commissioned a manual of
guidelines for performing customer satisfaction surveys for
DOTs across the country. In total,  states were represented
in the review, which highlighted the methods, purposes,
successes, and failures they experienced when utilizing a
survey []. Since this manual was published, DOTs from
over a dozen states throughout the US have conducted
similar satisfaction surveys, including Nevada in  [],
for the purpose of tracking progress over time or as a
means of comparing strengths and weaknesses with those of
neighboring states. Additionally, the TRB manual provides
information on various survey methods available to designers
including the pros and cons of each []. e most expensive
option, the personal interview, requires a huge investment in
training interviewers but provides the most in-depth results
[].
Independent of initiatives from the public sector, there
can be inconsistent results from customer surveys. For
example, customers may feel that safety is the greatest priority
on Nevada roads; simultaneously, they may think that safety
expenditures may appear to be an inexcusable drain on
resources. is disagreement could cause one analyst to
promote increased spending on road safety features and
anothertopromotereducedspendingonsafetyinfavorof
other projects, such as adding new roads.
us, the intent of this research is to combine the
results of the  Customer Satisfaction Survey and the
Business Intelligence Review of the Nevada Department of
Transportation (NDOT) nancial database. Combining the
survey responses with the annual expenditure of NDOT has
the potential of showing where spending has been successful
and where it has been wasteful. With this information,
NDOT could plan budgets and allocate funds to address
the needs of Nevada residents in a more optimal manner.
Additionally, the analysis has the potential of being applied to
other public departments with limited budgets where public
opinion, not sales, prots, or shareholder constraints, is the
priority.
2. Methodology
A questionnaire was designed and implemented to survey the
Nevadans on their opinions about congestion, construction,
maintenance, safety, and funding for state roads. For this
survey, it was important to determine the dierences among
the three districts within NDOT’s jurisdiction, District :
Southern Nevada; District : Northwestern Nevada; and Dis-
trict : Northeastern Nevada. Moreover, occupational sub-
populationsweretargetedinthequestionnaireandincluded
business executives, school district employees, reghters,
police ocers, tourists and tourism workers, commercial
truck drivers, and warehousing and distribution managers.
e respondents answered  questions related to experi-
ences on the Nevada roads, ve demographic questions for
weighing and location purposes, and one open-ended ques-
tion that allowed for additional suggestions and concerns.
A total of , survey responses were collected by the
Cannon Survey Center (CSC), operated within the University
of Nevada, Las Vegas (UNLV). CSC collected , responses
by online and telephone surveys. In addition, to reach the
occupational subpopulation goals, , responses were col-
lected from several smaller population groups independently
from the CSC surveys, using a small subcontractor and
UNLV’s Transportation Research Center (TRC). e sample
size of each of these subpopulations was determined to obtain
a representative sample for the corresponding size of the
subpopulations using information from the US Bureau of
Labor Statistics [].
Considering the discrete characteristic of the opinions,
various discrete choice model specications were tested seek-
ing to better capture the interdependencies between opinions
and potential explanatory variables. Some of the specica-
tions that were tested included logit, mixed-logit, and probit
models. In this study, the best model results in terms of
higher explanatory power were obtained using probit spec-
ications. Probit models are characterized by the assumption
of normally distributed error terms. at is, the distribution
oftheunobservedfactorsisnormal.isassumptionmakes
probit models very attractive and able to capture complex
interdependencies. Other alternative models such as logit
are very restrictive because they imply the assumption of
independent and identically distribute extreme value error
terms. Most discrete choice models can handle multinomial,
discrete-ordered, and binary specications []. When the
data includes an increasing or decreasing order of choices, a
discrete-ordered specication enables using this information
for potentially increasing the explanatory power of the model
[].
e probit models that were developed in this study
provided insights into how the Nevadans felt regarding
their highway system. ese insights were correlated with
the expenditure trends that may or may not have aected
these feelings or opinions. e nancial database of Nevada
Department of Transportation (NDOT) was used for study-
ing this potential correlation. is database provides infor-
mation about how resources were used across the highway
system.eanalysisperiodincludedscalyearsthrough
.
Mathematical Problems in Engineering
T : Congestion acceptability as a function of travel time satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Con Satised . . . 1.14𝑒 − 10∗∗∗
Con Neutral . . . <𝑒−16
∗∗∗
Con Dissatised . . . <𝑒−16
∗∗∗
Con Very Dissatised . . . <𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Congestion acceptability as a function of congestion reduction satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Con Satised . . . 0.0194
Con Neutral . . . 5.78𝑒 − 12∗∗∗
Con Dissatised . . . <𝑒−16
∗∗∗
Con Very Dissatised . . . <𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Congestion acceptability as a function of travel time satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Con Satised . . . 0.00145∗∗
Con Neutral . . . 4.92𝑒 − 13∗∗∗
Con Dissatised . . . 7.14𝑒 − 12∗∗∗
Con Very Dissatised . . . .
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Congestion acceptability as a function of congestion reduction satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Con Satised . . . 0.11
Con Neutral . . . 5.85𝑒 − 05∗∗∗
Con Dissatised . . . 1.46𝑒 − 11∗∗∗
Con Very Dissatised . . . 9.80𝑒 − 09∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
2.1. Results from Probit Models for Congestion. ree ques-
tions were asked in the Congestion section of the survey:
() How satised are you with your highway travel time?
() How satised are you with the eorts being made to
reduce congestion on freeways?
() Is the level of congestion on Nevada highways accept-
able?
e rst two questions were asked on a ve-point scale
from “Very Satised” to “Very Dissatised.” e last question
was a simple binary “yes or no” question. All three questions
had a majority of positive responses; however, to improve
satisfaction, we must determine what factors or combination
of factors led some respondents to answer that they were
either “Dissatised” or “Very Dissatised.”
A probit model was developed to understand how the
responses of the two prefacing questions inuenced the
response of the nal question: “Is the level of congestion on
Nevada highways acceptable?” A model was developed for
each of NDOT’s three districts.
In Table , “Con Satised” represents a “Satised”
response for the rst question about Congestion. A model,
with all coecients equal to zero, represented the likelihood
of a respondent indicating that they were “Very Satised”
with either their highway travel time (Congestion Question
1) or the eorts being made to reduce congestion on freeways
(Congestion Question 2)whilesimultaneouslyresponding
that the level of congestion on Nevada highways was not
acceptable.
As the level of satisfaction decreased with each question,
shown in Tables –, the likelihood of responding that the
Mathematical Problems in Engineering
T : Perception of safety as a function of debris removal satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.000382∗∗∗
Safe  Neutral . . .<𝑒−16
∗∗∗
Safe  Dissatised . . . <𝑒−16
∗∗∗
Safe  Very Dissatised . . . <𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of roadway striping satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . .
Safe  Neutral . . . 5.38𝑒 − 09∗∗∗
Safe  Dissatised . . . <𝑒−16
∗∗∗
Safe  Very Dissatised . . . <𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of signage satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.0737
Safe  Neutral . . . 6.65𝑒 − 08∗∗∗
Safe  Dissatised . . . <𝑒−16
∗∗∗
Safe  Very Dissatised . . .<𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
levelofcongestiononNevadahighwayswasunacceptable
increased. A strong trend could not be discerned from Dis-
trict . In Districts  (Southern Nevada) and  (Northwestern
Nevada), a decreased level of satisfaction was indicated for
both prefacing questions. Likely, the lack of a trend may be
duetothesmallsamplesizeofDistrict,asthepopulation
is roughly % of the entire state. Another possibility was the
strong rural inuence in this district; that is, congestion was
less of an issue for drivers in District .
InbothDistrictsand,analysisshowedthatdissat-
isfaction with highway travel time was the most inuential
predictorofa“No”responsefortheacceptabilityofthe
levelofcongestiononNevadahighways.isimpliedthat
if highway travel time were addressed by NDOT, a greater
number of respondents who are currently dissatised would
feel that the level of congestion on Nevada highways was
acceptable.
2.2. Results from Probit Models for Safety. A similar analysis
was performed within the “Safety” segment of the survey.
In that section, there were six prefacing questions and
one summary question. e prefacing questions sought the
respondent’s level of satisfaction regarding debris removal,
roadway striping, signage, lighting, drainage, and snow and
ice removal. e respondents answered on a ve-point
scale from “Very Satised” to “Very Dissatised.” e nal
summary question was: “Overall, how safe do you feel when
traveling on highways in Nevada?” e last question was
askedonafour-pointscaletocreateadenitivepositiveor
negative response ranging from “Very Safe” to “Very Unsafe.”
For each district, dierent prefacing questions were sig-
nicant when predicting a respondent’s overall perception
of safety on Nevada highways. In the following models, the
initial intercept estimate was the likelihood that a “Very
Satised” respondent considered that he or she was safe
(either “Safe” or “Very Safe”). erefore, as satisfaction levels
decreased, it was expected that the estimates would decrease
toward a negative perception of safety.
In determining the overall level of safety that users
perceive regarding Nevada highways, the models for District
, found in Tables –, showed that all factors were at least
partially signicant. Safety Question 1, concerning debris
removal, showed the greatest eects implying that better
removalofdebrisbyNDOTwouldhaveagreatereectonthe
overall perception of safety. e two weather-related topics,
drainage and snow and ice removal, were not signicant for
themodel,mostlikelybecausetheseissuesaremoreprevalent
in the northern districts.
e models for District , shown in Tables –, showed
weaker correlations than did the models for District .
e model for signage, shown in Table , appears to be
themostsignicantpredictoroftheperceptionofsafety
Mathematical Problems in Engineering
T : Perception of safety as a function of lighting satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.116
Safe  Neutral . . . 8.96𝑒 − 06∗∗∗
Safe  Dissatised . . .    5.27𝑒 − 13∗∗∗
Safe  Very Dissatised . . . <𝑒−16
∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of drainage satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.6505
Safe  Neutral . . . 0.0392
Safe  Dissatised . . . 1.35𝑒 − 09∗∗∗
Safe  Very Dissatised . . .   9.80𝑒 − 15∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of snow and ice removal satisfaction. District  (Southern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.4027
Safe  Neutral . . . 0.0425
Safe  Dissatised . . . 2.97𝑒 − 08∗∗∗
Safe  Very Dissatised . . . 3.27𝑒 − 06∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of debris removal satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . <𝑒−16
∗∗∗
Safe  Satised . . . 0.7483
Safe  Neutral . . . 0.1287
Safe  Dissatised . . . 8.67𝑒 − 05∗∗∗
Safe  Very Dissatised . . . .#
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of roadway striping satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . 5.14𝑒 − 13∗∗∗
Safe  Satised . . . 0.21049
Safe  Neutral . . . 0.02522
Safe  Dissatised . . . 0.00724∗∗
Safe  Very Dissatised . . . 0.00406∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
and was signicant at all satisfaction levels. Also in this
model, weather-related questions were not more signicant
to residents of District  than to those of District , even
though rain and snow were more common in the north.
In District , none of the primary questions provided a
signicant estimate of the users’ perceptions of safety. Again,
this may be due to the small sample size coming from District
.
Because there is strong evidence to indicate that cus-
tomers are highly concerned with poor safety conditions on
Nevada highways, the opinions of the greatest number of
drivers might be improved by addressing safety issues on
Mathematical Problems in Engineering
T : Perception of safety as a function of signage satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . 4.86𝑒 − 16∗∗∗
Safe  Satised . . . 0.041306
Safe  Neutral . . . 0.000959∗∗∗
Safe  Dissatised . . . 0.002131∗∗
Safe  Very Dissatised . . . 3.51𝑒 − 07∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of lighting satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . 9.10𝑒 − 10∗∗∗
Safe  Satised . . . 0.1099
Safe  Neutral . . . .#
Safe  Dissatised . . . 0.0167
Safe  Very Dissatised . . . 2.84𝑒 − 06∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of drainage satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . 2.39𝑒 − 11∗∗∗
Safe  Satised . . . .
Safe  Neutral . . . .#
Safe  Dissatised . . . 0.001785∗∗
Safe  Very Dissatised . . . 0.000805∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Perception of safety as a function of snow and ice removal satisfaction. District  (Northwestern Nevada).
Variable Likelihood estimate Standard error 𝑍value Pr(>|𝑧|)
(Intercept) . . . 1.1𝑒 − 14∗∗∗
Safe  Satised . .. 0.554384
Safe  Neutral . . . 0.663941
Safe  Dissatised . . . 0.002260∗∗
Safe  Very Dissatised . . . 0.000162∗∗∗
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
roads, specically deploying countermeasures in the loca-
tions with the most safety issues. Clearly, this requires a com-
plete trac-safety management process involving network
screening, diagnosis, countermeasure selection, appraisal,
and evaluation.
2.3. e Relationship between Customer Satisfaction and Gov-
ernment Spending. e expenditure data that was accessible
covered scal years  through . Expenditures were
separated both by district and category. ere were 
possible expenditure categories, only ve of which could be
directly related to a given question regarding customer sat-
isfaction and had consistent data across the time period and
districts. ese ve categories were construction, construc-
tion engineering, maintenance, roadway design, and trac.
Regarding an expenditure trend analysis, the most appro-
priateoneforthisstudyandthemethodusedinthisresearch
was a time-series regression []. Unfortunately, a time-
series regression did not yield statistically signicant trends
for all ve expenditure categories (see Table ). Signicant
results included maintenance and roadway design and trac
for District  and maintenance for Districts  and . In
large part, expenses were random over the time period
and had too large variance to condently assign a mean
annual expense or average rate of change in expenditures (see
Table).us,theaverageannualgrowth(oraverageannual
decline) could not be included as a variable in a potential
model measuring the relationship between satisfaction and
expenditure. erefore, the true value of the expense was
compared to the levels of satisfaction.
Mathematical Problems in Engineering
T : Estimates for the expenditure trend categories.
Mean Standard deviation Trend estimate Standard error 𝑡-statistics Pr(>|𝑡|)
District 1
Construction . . . . . .
Construction engineering   . . . .
Maintenance   . . . .∗∗
Roadway design . . . . . .∗∗∗
Trac . . . . . .∗∗
District 2
Construction . . . . . .
Construction engineering  . . . . .
Maintenance    . . .∗∗∗
Roadway design . . . . . .
Trac . . . . . .
District 3
Construction . . . . . .
Construction engineering  . . . . .
Maintenance   . . . .
Roadway design . . . . . .
Trac . . . . . .
Signicance codes:  = ∗∗∗,.=∗∗,.=,.=.
T : Comparison of survey question categories to expenditure
categories.
Survey question category Expenditure category
Construction
Construction
Construction engineering
Maintenance
Safety Roadway design
Congestion Trac
Data for nancial expenditure originally was divided into
 expenditure categories. Survey questions were divided into
groups comparing relevant expenditure data, either directly
or indirectly. ese groups were construction, safety, and
congestion. e relationships between the survey question
categories and the expenditure categories are shown in
Table .
To measure and conrm the relationships between the
two data sets, nonparametric correlations were established
using Spearman’s Rho and factor analysis. Aer performing
the factor and correlation analyses, one survey question
showed a signicant correlation with the given expenditures.
A -tailed 𝑝value of . was used to determine statistical
signicance.
Table  shows the relationships between construction
expenditures for District  and the ve survey questions.
Signicant Spearman’s Rho correlation coecient was
found between Question 4of the survey, “how satised are
you with the amount of time it takes to complete construction
projects?,” and construction expenditures. us, as construc-
tion spending increased in District , dissatisfaction with
construction times decreased.
T : Construction correlation for District .
Construction
survey question
Construction
expenditures
Const 
Correlation coecient .
Signicance .
𝑁
Const 
Correlation coecient .
Signicance .
𝑁
Const 
Correlation coecient .
Signicance .
𝑁
Const 4
Correlation coecient 0.471
Signicance 0.065
𝑁16
Const 
Correlation coecient .
Signicance .
𝑁
Table  provides the relationships between congestion
(trac) expenditures for District  and the three questions
of the survey about this criterion.
In Table , signicant Spearman’s Rho correlation coef-
cient was observed between Question 2, “how satised
are you with the eorts being made to reduce congestion
on freeways?,” and congestion (trac) expenditures. us,
as congestion (trac) spending increased in District ,
dissatisfaction decreased.
Mathematical Problems in Engineering
T : Congestion correlation for District .
Trac survey
question
Tra c
expenditures
Tra  
Correlation coecient .
Signicance .
𝑁
Tra 2
Correlation coecient 0.422
Signicance 0.100
𝑁16
Tra  
Correlation coecient .
Signicance .
𝑁
T : Congestion correlation for District  (factor analysis).
Spearman’s Rho Trac expenditures
Factor
Correlation coecient 0.452
Signicance 0.079
𝑁16
Table  shows the relationship between congestion
(trac) expenditures for District  and the factor analysis
created for the three congestion questions using Spearman’s
Rho coecients as a measure of nonparametric correlation
and Cronbachs Alpha coecient as a measure of reliability.
CronbachsAlpha(.)indicatesaconsistencysuitablefor
the new factor [].
Table  shows signicant Spearmans Rho correlation
coecient for the relationship between congestion (trac)
expenditures and the factor analysis created for the three con-
gestion questions in District . us, as congestion (trac)
spending increases in District , dissatisfaction decreases.
Finally, Table  displays the relationships between safety
expenditures for District  and the survey questions about
these criteria, using Spearmans Rho criteria.
Table  shows signicant Spearmans Rho correlation
coecient for the relationship between Safety Question 1and
Safety Question 2of the survey, concerning debris removal
and road striping, respectively, and roadway design expen-
ditures. us, additional spending increased dissatisfaction
with both debris removal and roadway striping in District .
3. Conclusion
is study provides evidence of the level of satisfaction of the
residents of Nevada decreases related to the safety conditions
on Northwestern and Southern Nevada highways. Analy-
sis conrmed several statistically signicant relationships
between customer satisfaction and government spending.
In District , increasing construction spending resulted in
increasing customer satisfaction with the amount of time
it takes to complete construction projects. In District ,
the analysis conrmed that additional trac expenditures
increased satisfaction. In District , roadway design expen-
ditures had an eect on user perception of both debris
T : Safety correlation for District .
Safety survey
question
Roadway design
expenditures
Safe 1
Correlation coecient 0.497
Signicance 0.050
𝑁16
Safe 2
Correlation coecient 0.487
Signicance 0.056
𝑁
Safe 
Correlation coecient .
Signicance .
𝑁
Safe 
Correlation coecient .
Signicance .
𝑁
Safe 
Correlation coecient .
Signicance .
𝑁
Safe 
Correlation coecient .
Signicance .
𝑁
Safe 
Correlation coecient .
Signicance .
𝑁
removal and roadway striping. However, these roadway
design expenditures had the unintended eect of making
users less satised with these issues. is may be an eciency
issue because roadway design dollars are not going toward
debris removal or roadway striping projects.
Several problems arose in the latter half of this research.
First, condence in the expenditure data was limited. Because
there was only a nite breakdown of expenditure types,
assumptions had to be made both when entering expenditure
data at the source and when analyzing the data in the present.
Second, comparing direct expenditures between districts was
potentially misleading. Most of Nevada’s population lives
in District ; moreover, there are dierent types of roads
throughout the state. Attempts were made to account for this
issue by weighting expenditure data using such factors as
district population, Average Annual Daily Trac (AADT),
and road miles in each jurisdiction. Finally, only the raw
data values for expenditures could be used when measuring
the correlations. Had there been a time-series relationship,
variables such as rate of growth of expenditure or rate of
decline of expenditure could been condently used in a
model.
Further analysis should be conducted to determine if and
where NDOT funding is being expended to better address the
needs and perceptions of the transportation users in Nevada.
It is recommended that additional studies be conducted
to measure respondent satisfaction over time. Additional
Mathematical Problems in Engineering
questions could include the duration of state or regional resi-
dency, the perception of changing conditions (e.g., improving
conditions), and additional ranking questions.
Competing Interests
e authors declare that they have no competing interests.
References
[] T. Sorel, “Great expectations,Public Roads,vol.,no.,pp.
–, .
[] J. J. Kline, “How quality award-winning governments handle
customer service,Journal of Organizational Excellence,vol.,
no. , pp. –, .
[] R. Sloane and K. Stein, Using Customer Needs to Drive
Transportation Decisions, Report , Transportation Research
Board, .
[] Institute of Transportation Engineers, Transportation
Operations—e National Dialogue Continues,Instituteof
Transportation Engineers, Washington, DC, USA, .
[] NuStats, “Putting Customer Research into Practice: Guidelines
for Conducting, Reporting, and Using Customer Surveys
Related to Highway Maintenance Operations,” , http://
onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP-()
FR Volu m e . p d f .
[] e Center for Research Design and Analysis, Nevada Depart-
ment of Transportation Customer Satisfaction Survey 2009,
University of Nevada, Reno, Reno, Nev, USA, .
[] J. T. Israel, Alternative Designs and Methods for Customer Sat-
isfaction Measurement, SatisFaction Strategies, Portland, Ore,
USA, .
[] United States Department of Labor, Bureau of Labor Statistics,
.
[] S.P.Washington,M.G.Karlais,andF.L.Mannering,Statisti-
cal and Econometric Methods for Transportation Data Analysis,
Chapman&Hall,BocaRaton,Fla,USA,.
[] UCLA: Institute for Digital Research and Education, R
Data Analysis Examples: Ordinal Logistic Regression,,
http://www.ats.ucla.edu/stat/r/dae/ologit.htm.
[] B. Kittel and H. Obinger, “Political parties, institutions, and the
dynamics of social expenditure in times of austerity,Journal of
European Public Policy,vol.,no.,pp.,.
[] M. C. Rodriguez and Y. Maeda, “Meta-analysis of coecient
alpha,Psychological Methods,vol.,no.,pp.,.
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Preface Introduction Transportation is integral to developed societies. It is responsible for personal mobility which includes access to services, goods, and leisure. It is also a key element in the delivery of consumer goods. Regional, state, national, and the world economy rely upon the efficient and safe functioning of transportation facilities. In addition to the sweeping influence transportation has on economic and social aspects of modern society, transportation issues pose challenges to professionals across a wide range of disciplines including transportation engineers, urban and regional planners, economists, logisticians, systems and safety engineers, social scientists, law enforcement and security professionals, and consumer theorists. 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Transportation-system managers and governmental agencies face similar stochastic problems in determining how to measure and compare system measures of performance, where to invest in safety improvements, how to efficiently operate transportation systems and how to estimate transportation demand. As a result of the complexity, diversity, and stochastic nature of transportation problems, the methodological toolbox required of the transportation analyst must be broad. Approach The third edition of Statistical and Econometric Methods offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics, to address reader and reviewer comments on the first and second editions, and to provide an increasing range of examples and corresponding data sets. This book describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. Every book must strike an appropriate balance between depth and breadth of theory and applications, given the intended audience. This book targets two general audiences. First, it can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. There is sufficient material to cover two 3-unit semester courses in statistical and econometric methods. Alternatively, a one semester course could consist of a subset of topics covered in this book. The publisher’s web-site contains the numerous datasets used to develop the examples in this book so that readers can use them to reinforce the modeling techniques discussed throughout the text. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Sufficient analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. Data-Driven Methods vs. Statistical and Econometric Methods In the analysis of transportation data, four general methodological approaches have become widely applied: data-driven methods, traditional statistical methods, heterogeneity models, and causal inference models (the latter three of which fall into the category of statistical and econometric methods and are covered in this text). Each of these methods have an implicit trade-off between practical prediction accuracy and their ability to uncover underlying causality. Data-driven methods include a wide range of techniques including those relating to data mining, artificial intelligence, machine learning, neural networks, support vector machines, and others. Such methods have the potential to handle extremely large amounts of data and provide a high level of prediction accuracy. On the down side, such methods may not necessarily provide insights into underlying causality (truly understanding the effects of specific factors on accident likelihoods and their resulting injury probabilities). Traditional statistical methods provide reasonable predictive capability and some insight into causality, but they are eclipsed in both prediction and providing causal insights by other approaches Heterogeneity models extend traditional statistical and econometric methods to account for potential unobserved heterogeneity (unobserved factors that may be influencing the process of interest). Causal-inference models use statistical and econometric methods to focus on underlying causality, often sacrificing predictive capability to do so. Even though data-driven methods are often a viable alternative to the analysis of transportation data if one is interested solely in prediction and not interested in uncovering causal effects, because the focus of this book is uncovering issues of causality using statistical and econometric methods, data-driven methods are not covered. Chapter topics and organization Part I of the book provides statistical fundamentals (Chapters 1 and 2). This portion of the book is useful for refreshing fundamentals and sufficiently preparing students for the following sections. This portion of the book is targeted for students who have taken a basic statistics course but have since forgotten many of the fundamentals and need a review. Part II of the book presents continuous dependent variable models. The chapter on linear regression (Chapter 3) devotes additional pages to introduce common modeling practice—examining residuals, creating indicator variables, and building statistical models—and thus serves as a logical starting chapter for readers new to statistical modeling. The subsection on Tobit and censored regressions is new to the second edition. Chapter 4 discusses the impacts of failing to meet linear regression assumptions and presents corresponding solutions. Chapter 5 deals with simultaneous equation models and presents modeling methods appropriate when studying two or more interrelated dependent variables. Chapter 6 presents methods for analyzing panel data—data obtained from repeated observations on sampling units over time, such as household surveys conducted several times to a sample of households. When data are collected continuously over time, such as hourly, daily, weekly, or yearly, time series methods and models are often needed and are discussed in Chapters 7 and 8. New to the 2nd edition is explicit treatment of frequency domain time series analysis including Fourier and Wavelets analysis methods. Latent variable models, discussed in Chapter 9, are used when the dependent variable is not directly observable and is approximated with one or more surrogate variables. The final chapter in this section, Chapter 10, presents duration models, which are used to model time-until-event data as survival, hazard, and decay processes. Part III in the book presents count and discrete dependent variable models. Count models (Chapter 11) arise when the data of interest are non-negative integers. Examples of such data include vehicles in a queue and the number of vehicle crashes per unit time. Zero inflation—a phenomenon observed frequently with count data—is discussed in detail and a new example and corresponding data set have been added in this 2nd edition. Logistic Regression is commonly used to model probabilities of binary outcomes, is presented in Chapter 12, and is unique to the 2nd edition. Discrete outcome models are extremely useful in many study applications, and are described in detail in Chapter 13. A unique feature of the book is that discrete outcome models are first considered statistically, and then later related to economic theories of consumer choice. Ordered probability models (a new chapter for the second edition) are presented in Chapter 14. Discrete-continuous models are presented in Chapter 15 and demonstrate that interrelated discrete and continuous data need to be modeled as a system rather than individually, such as the choice of which vehicle to drive and how far it will be driven. Finally, Part IV of the book contains massively expanded chapter on random parameters models (Chapter 16), a new chapter on latent class models (Chapter 17), a new chapter on bivariate and multivariate dependent variable models (Chapter 18) and an expanded chapter on Bayesian statistical modeling (Chapter 19). Models that deal with unobserved heterogeneity (random parameters models and latent class models) have become the standard statistical approach in many transportation sub-disciplines and Chapters 16 and 17 provide an important introduction to these methods. Bivariate and multivariate dependent variable models are encountered in many transportation data analyses. Although the inter-relation among dependent variables has often been ignored in transportation research, the methodologies presented in Chapter 18 show how such inter-dependencies can be accurately modeled. The chapter on Bayesian statistical models (Chapter 19) arises as a result of the increasing prevalence of Bayesian inference and Markov Chain Monte Carlo Methods (an analytically convenient method for estimating complex Bayes’ models). This chapter presents the basic theory of Bayesian models, of Markov Chain Monte Carlo methods of sampling, and presents two separate examples of Bayes’ models. The appendices are complementary to the remainder of the book. Appendix A presents fundamental concepts in statistics which support analytical methods discussed. Appendix B provides tables of probability distributions used in the book, while Appendix C describes typical uses of data transformations common to many statistical methods. While the book covers a wide variety of analytical tools for improving the quality of research, it does not attempt to teach all elements of the research process. Specifically, the development and selection of research hypotheses, alternative experimental design methodologies, the virtues and drawbacks of experimental versus observational studies, and issues involved with the collection of data are not discussed. These issues are critical elements in the conduct of research, and can drastically impact the overall results and quality of the research endeavor. It is considered a prerequisite that readers of this book are educated and informed on these critical research elements in order to appropriately apply the analytical tools presented herein. Simon P. Washnington Mathew G. Karlaftis Fred L. Mannering Panigiotis Ch. Anastasopoulos
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Recognizing that customer service is becoming an increasingly important function of government, quality award-winning governments, at all levels and around the world, are not only seeking customer feedback, but are incorporating customer satisfaction measures into their strategic plans. Thus, they are setting customer standards that are evaluated regularly, benchmarking customer service procedures against the best in business, and including customer service training, benchmarking, and customer satisfaction performance measures in labor contracts. This article, which examined the customer service practices of quality award-winning governments at the federal, state, and local level in the United Kingdom, United States, and Australia, demonstrates the worldwide nature of the quality improvement and customer satisfaction effort. © 2001 John Wiley & Sons, Inc.
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The meta-analysis of coefficient alpha across many studies is becoming more common in psychology by a methodology labeled reliability generalization. Existing reliability generalization studies have not used the sampling distribution of coefficient alpha for precision weighting and other common meta-analytic procedures. A framework is provided for a statistically grounded meta-analysis of coefficient alpha using its sampling distribution. Two empirical examples are offered to illustrate these methods, and limitations of reliability generalization are described.
Putting Customer Research into Practice: Guidelines for Conducting, Reporting, and Using Customer Surveys Related to Highway Maintenance Operations
  • Nustats
NuStats, "Putting Customer Research into Practice: Guidelines for Conducting, Reporting, and Using Customer Surveys Related to Highway Maintenance Operations, " 2009, http:// onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP20-07(260) FR Volume1.pdf.
Using Customer Needs to Drive Transportation Decisions
  • R Sloane
  • K Stein
R. Sloane and K. Stein, Using Customer Needs to Drive Transportation Decisions, Report 487, Transportation Research Board, 2003.
Alternative Designs and Methods for Customer Satisfaction Measurement, SatisFaction Strategies
  • J T Israel
J. T. Israel, Alternative Designs and Methods for Customer Satisfaction Measurement, SatisFaction Strategies, Portland, Ore, USA, 2002.