Model of personal attitudes towards transit service quality

JOURNAL OF ADVANCED TRANSPORTATIONJ. Adv. Transp. 2011; 45:271–285Published online 12 July 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/atr.106

Model of personal attitudes towards transit service quality

Khandker M. Nurul Habib1y, Lina Kattan2*,y and Md. Tazul Islam1z

1Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S1A4, Canada2Department of Civil Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW,

Calgary, Alberta, Canada

SUMMARY

This paper presents a critical investigation of reasons for using transit by residents of the City of Calgary,Canada. Reasons for using transit are expressed as functions of people’s perceptions and attitudes towardstransit service quality and attributes. A multinomial logit model combined with latent variable models isdeveloped to capture unobserved latent variables in defining perceptions and attitudes. Using data from atransit customer satisfaction survey conducted in 2007 by Calgary Transit, this approach models the reasonsfor choosing transit and tests the significance of two individual specific latent variables: perceptions of‘reliability and convenience’ and ‘ride comfort’. Many behavioural details are revealed that have importantpolicy implications. Most importantly, it is found that the people of Calgary value ‘reliability andconvenience’ over ‘ride comfort’. As for policy implications of the findings, it is clear that improvingthe connectivity of train service, reducing multimodal transfers, and increasing dedicated right-of-ways fortransit would effectively increase transit ridership in Calgary. Copyright # 2010 John Wiley & Sons, Ltd.

KEY WORDS: Transit service quality, Mode choice, Discrete choice model, Latent variable, Attitude,Multimodal transportation

1. BACKGROUND

Understanding the public’s attitudes, perceptions and knowledge in relation to transport measures is

important for the formulation and monitoring of transportation policies. Demand for an efficient transit

system in any urban area stems from mobility, environmental and energy objectives. Measurements of

a transit system’s service quality is a challenging research theme and of great importance to the transit

service providers and regulatory agencies [1]. The actual performance of a transit system should be

considered from the transit users’ perspective [2]; and, although there has been demand for analysing

transit service quality based on how the users perceive it [3], very few studies truly concentrate on

attitudes or psychological factors behind the users’ perceptions of transit service quality.

Attitude towards transit is an important element influencing people to choose transit in our auto-

oriented North American cities. Attitude, often expressed as perception, is an abstract and

psychological term and plays the major role in governing behaviour and defining action [4]. Actions

towards transit usage can be traced back to the users’ perceptions of the service quality of a transit

system. In order to influence peoples’ actions in terms of transit over automobile usage, it is germane

that we understand the factors influencing formation of attitudes or perceptions towards transit,

because people’s attitudes are reflected in their travel related actions [5]. This paper follows this

principle of investigation in order to identify the factors influencing transit modal share. However,

other than focusing on a conventional mode choice model of transit modal share, we have taken a

different approach.

*Correspondence to: Dr. Lina Kattan, Department of Civil Engineering, Schulich School of Engineering, University ofCalgary, 2500 University Drive NW, Calgary, Alberta, Canada. E-mail: [email protected] Professor.zGraduate Student.

Copyright # 2010 John Wiley & Sons, Ltd.

272 K. M. N. HABIB ET AL.

We have focused only on transit users and investigated the factors influencing their reasons for using

transit. It is a fact that, in many auto-oriented North American cities, public transit is in no way a true

competitor to the automobile [6]; therefore, concentrating only on transit users in order to find

influential factors is more effective than focusing on both users and non-users. In this regard, this

investigation breaks ground in terms of not asking why people should use transit and instead

investigating the reasons why some people use transit. We have concentrated on people’s perceptions

or attitudes towards transit service, in order to model the reasons for using transit. Although criticism of

this study can be pointed at the avoidance of transit non-users or neglect of modal competition, the

detailed investigation of only users’ behaviours provided a rich information set on how to design better

transit service targeting specific population segments.

As a case study, this investigation concentrated on the major Canadian city of Calgary, which is

rapidly growing and has become a major centre of national economic activities due to the recent oil

boom in Alberta. This investigation used the data set collected by a customer satisfaction survey

conducted in Calgary in 2007, which was a non-research oriented survey. However, this paper shows

how a non-research based survey can be used in researching critical elements.

The paper is arranged as follows: the next section provides a literature review on investigating transit

usage, followed by descriptions of the study area, survey, data set and mathematical model, and

discussions and analysis of the estimated model results. The paper ends with a conclusion and

recommendations for future research.

2. LITERATURE REVIEW

Extensive literature reviews are available on transit modal share, transit market segmentation, transit

service marketing, etc. [7–9]. Shiftan et al. [7] is the most recent review and is a comprehensive

assessment of users’ behaviour towards transit usage. The authors focused mainly on market

segmentation of transit users by using factor analysis and structural equation modelling. However, their

approach was the investigation of population segments with different attitudes towards transportation

systems as a whole, in order to identify the potential market for transit. This is an indirect approach to

finding direct factors that influence people’s choice of transit.

Datz [10] investigated attitudes and perceptions towards transit within the wider issue of teenage

mobility; however, this analysis was mostly exploratory, and no analytical modelling approach was

taken to unravel the inherent latent behaviour.

A number or researchers have focused on the relationship between transit service quality and

people’s accessibility [11]. Hensher et al. [1] identified the fact that transit service quality should be

evaluated from the perspective of transit users’. The authors designed a survey to identify users’

perspectives in terms of evaluating service quality along arbitrary scales. They used a simple

multinomial logit model to identify relationships between users’ evaluation grades and different

attributes. They did not consider the fact that users’ evaluation grades are reflections of latent attitudes

and perceptions. A simple multinomial logit or nested logit model cannot capture such latent

behaviour. What is most important in the investigation of behaviours is the examination of the motives

for using transit. Steg [12] systematically investigated different motives for car use. To our knowledge,

nobody has yet investigated motives for transit use.

Considerable emphasis has been placed on transit oriented development. There is no debate on the

effectiveness of transit in shaping our transportation system into a more sustainable one; however, the

discussion on whether a built environment influences transit usage or better transit service influences

development towards a sustainable city is a long standing debate [13]. Increase in transit usage is

sought from even public health points of view, such as reduction of obesity [14].

The primary focus has shifted towards transit passengers’ satisfaction, and efforts and

recommendations are being made to increase passenger satisfaction in order to improve transit

ridership (Center for Urban Transportation Research, 2000).

Many transit models have been proposed that inherently assume that people’s attitudes are known

[15–20]. However, unless we really identify the factors that affect attitude formation towards transit

service, we cannot understand the elements influencing passenger satisfaction; and, consequently, we

cannot effectively influence large numbers of people to shift towards transit usage.

Copyright # 2010 John Wiley & Sons, Ltd. J. Adv. Transp. 2011; 45:271–285

DOI: 10.1002/atr

MODE OF PERSONAL ATTITUDE TOWARD TRANSIT 273

In this paper, we use an innovative latent variable discrete choice model in order to identify the

factors behind formation of attitudes or preferences towards transit service. The latent variable discrete

choice model is a very powerful tool for unravelling many factors that are apparently indiscernible

[21]. We used transit passenger satisfaction survey data to estimate our model. The next sections

describe the study area and data set in detail.

3. STUDY AREA

The study area was the City of Calgary, the business capital of Alberta, Canada. The population of

Calgary has been growing at a very fast rate in the last few years; in fact, the population has grown from

876 519 residents in 2002 to more than a million in 2007 (average growth rate of 3%). According to the

2008 Calgary civil census, the population of Calgary was 1 019 942. Additionally, Calgary has the

highest Canadian average annual employment growth at 3.5% over the last 10 years (1998–2007)

(Calgary Economic Development website). As a city that has experienced rapid growth in recent years,

urban sprawl has become a major concern. The area of Calgary is the size of New York City, while

housing only one-tenth of that city’s population [22]. Calgary’s population growth has been absorbed

by new developments that spread in every direction, creating new neighbourhoods of single-family

residences with low population densities. The dispersed location of single-family housing makes it

very uneconomical to be adequately served with good quality public transport.

The City of Calgary has a comprehensive, integrated transit network consisting of CTrain (light rail

transit) service, regular bus, rapid bus transit and community shuttle service within the city. Calgary

Transit, which is owned and operated by the City of Calgary, is the public transit service that is

responsible for planning, operating and maintaining the transit network. To keep pace with the rapid

city-wide expansion, Calgary Transit underwent a major expansion and improvement of its transit

service between the years 1995 and 2004. The CTrain system was expanded by 9 km with 5 new

stations. The transit vehicle fleet was increased by 25% with 31 new CTrain cars and 175 buses.

Moreover, affordable transit fare options were created for post-secondary students and low-income

Calgarians. Additionally, transit signal priority was added at a number of intersections to provide travel

time advantages for transit vehicles in the city [23].

Despite the fact that the transit service was expanded by 43%, it still has not kept pace with

downtown ridership. Transit demand has recently exceeded the capacity for downtown travel.

Downtown Calgary has one of the highest peak time transit usage rates in North America. The AM

peak hour work trip transit modal split to the downtown is as high as 42% [23]. Other than peak period

downtown trips, the overall modal split in the city are the auto at 77%, public transit at 8.6%, walking at

12.4% and bicycle at 1.9% [24]. This overall low transit share can be explained by the predominance of

low-density areas underserved by public transport; however, even when the low-density suburbs are not

considered, the overall transit modal share in the city is still very low. There is considerable scope to

attract more people to transit and increase its modal share.

4. THE SURVEY AND DATA SET

The investigation in this paper is based on the 2007 transit customer satisfaction survey (TCSS)

conducted by Hargroup Management Consultants on behalf of Calgary Transit. The survey was

conducted in December 2007 by telephone with 500 randomly selected regular users of Calgary

Transit. Regular users of transit are defined as Calgarians who were at least 15 years of age and had

ridden Calgary Transit buses or CTrains at least once a week on average [25]. The survey starts by

asking the responders about the reasons that they use the transit. The data files also included basic

information on the main purpose of the trip, transit use, income group, age group, payment methods,

and a variety of attitudinal information on how people perceive comfort and convenience of using

transit.

Table 1 shows the comparative picture of demographic characteristics of the survey respondents and

the actual demographic pattern drawn from the Calgary 2007 civil census. It is clear that the survey

respondents included a higher proportion of females and younger people (i.e. those aged 19 and under)

than the overall population of Calgary, because traditionally female and younger people are considered


DOI: 10.1002/atr

Table 1. Sample characteristics.

Descriptions 2007 census % 2007 survey %

GenderMales 50 43Female 50 57

Age group (years)15–19 9 1520–24 9 1225–34 20 1735–44 23 1645–54 18 1655–64 9 13Over 64 11 10Refused – 1

Household income groupLess than $15 000 – 5$15 000 to <$25 000 – 5$25 000 to <$35 000 – 6$35 000 to <$45 000 – 5$45 000 to <$55 000 – 8$55 000 to <$65 000 – 5$65 000 to <$75 000 – 5$75 000 to <$85 000 – 5$85 000 to <$100 000 – 5$100 000 or more – 16Refused – 35


to be captive users of transit. It is worth noting that the proportion of the respondents with an annual

household income in excess of $100 000 was considerably high, and that the proportion of high-income

households using transit has been steadily rising since 2004 [24].

Almost half (47%) of Calgarians aged 15 and over are regular transit users (i.e. they use the transit at

least once per week). Among these regular users, 16% said that they use the transit 1–3 times a week,

25% use the public transit 4–7 times a week, 47% use the transit 7–10 times a week, and 12% use the

public transit more than 10 times a week [25].

A series of questions were asked regarding people’s responses to transit service attributes; however,

two major sets of questions were asked regarding transit service quality, which is of relevance to this

paper. One set of questions was related to the specific aspect of the transit service, and the other set

was related to the overall statement on the transit service quality. Table 2 summarizes the specific

questions and other personal and travel related information collected in the survey that is relevant to

this study. F1 and F2 with subscripts were the specific questions; and the respondents were required to

rate their satisfaction along a scale of 1–5: a response of 1 indicated excellent; 2, good; 3, satisfactory;

4, poor and, 5, very poor. X with subscripts relates the personal and choice related variables collected in

the survey.

Despite its richness, the shortcomings of this survey were the limited amount of socio-economic and

locational attributes. Indicators of transit accessibility were also very limited. Access from home to bus

or rail stops was recorded during the survey, but there is no way to measure transit network

accessibility, due to retrieval difficulties related to the respondents’ privacy issues. The data set was not

collected for research purposes; however, in this investigation, we used the data set to explore rich

behavioural details.

In addition to many other questions, the respondents were asked to identify the main reason for using

transit over any other modes of transportation:

Reason for choosing transit, y

1. N

Copy

o particular reason.

2. L
ess expensive/save gas/high gasoline prices.
3. N
o car available.
right # 2010 John Wiley & Sons, Ltd. J. Adv. Transp. 2011; 45:271–285

DOI: 10.1002/atr

Table 2. Specific questions regarding transit service.

Quality of Calgary transit service: ride comfort (Z1)F11 Manners of bus driversF12 Information availability from bus driverF13 Safety while ridingF14 Reliability and flexibilityF15 Professionalism of fare inspectors in trainF16 Overall passenger behaviourF17 Giving sufficient stop time to board and alight bus/trainF18 Maintaining arrival schedule at the stopF19 Security while ridingF110 Sufficient parking at park-and-ride locationsF111 Overall safety of bus and trainF112 Seat availability while riding trainF113 Smoothness of ridingF114 Pleasant experienceF115 Comfortability of seatsF116 Inside temperature of bus/trainF117 Cleanliness of disembarking area of bus/ride

Attributes of transit service: reliability and convenience (Z2)F21 Helpful and courteous staffF22 Schedule delayF23 Overall cleanlinessF24 Not overcrowdedF25 Service frequencyF26 Value for moneyF27 Length of travel timeF28 Route layoutF29 Convenience of connections and transfersF210 Providing customer safety and securityF211 Providing scheduling and route informationF212 Convenience of purchasing tickets and passesF213 Easy to access vehiclesF214 Easy to access bus stops

Attribute of the person and choiceX1 Average weekly ridesX2 Main mode of transit: bus, train, both bus and trainX3 Main purpose of trips: work, school, shopping, personal business, social/recreationX4 Payment method: adult monthly pass, ticket from a book of tickets, othersX5 Gender: male, femaleX6 Income group: <15k, 15–25k, 25–35k, 35–45k, 45–55k, 55–65k, 65–75k, 75–85k, 85–100k, 100kþX7 Age group: 15–19, 20–24, 25–34, 35–44, 45–54, 55–64, 65þ


4. A

Copy

void traffic.

5. A
void parking.
6. D
on’t drive.
7. C
onvenient service.
8. F
aster travel time.
9. C
omfortable/relaxing.
10. E
nvironmental reason.
For our research purposes, it may be more appropriate to illustrate reasons 7 and 9 with more specific

variables related to transit comfort and convenience, such as location of bus stops, adherence to the

schedule, frequency of trips, availability of seats, etc. Moreover, each individual was asked to identify

only one main reason. Rather than asking the respondents to select only one reason, a 10-point scale

can be used to rank the reasons of transit riders for choosing transit. Using ordered statistics would also

capture the sensitivity of choices to the analysed variables, such as the trip and socio-economic

characteristics.

right # 2010 John Wiley & Sons, Ltd. J. Adv. Transp. 2011; 45:271–285

DOI: 10.1002/atr


The reason for using transit is related to the respondents’ perceptions and attitudes towards transit.

This hypothesis forms the basis for the investigation presented in this paper. The challenge was to

model the reason for choosing transit as a function of perception or attitude, which we cannot observe.

However, using the scaling of transit service by the respondents, we can use a latent variable model to

link the reason for choosing transit over other modes of transportation and variables that define

perception or attitude towards transit service. The next section describes the modelling framework.

5. MODELLING STRUCTURE

The straight-forward way to investigate the relationship between the reason for choosing transit and

socio-economic and other variables of concerns is to use a simple multinomial logit model by selecting

one out of the ten above-mentioned reasons. This exercise was done, resulting in all variables (X1 to X7

in Table 2) having completely statistically insignificant coefficients. This finding indicates that

omitting latent variables that may be statistically significant in the model may affect the t-statistics of

the parameters. This finding might be attributed to the fact that that omitting significant variables

(which is, in our case, the latent variables) may lead to bias and inconsistency in the estimated

parameters [26].

The hypothesis, therefore, is that the reason for choosing transit over other modes of transportation

can also defined by latent perceptions or attitudes towards transit service. We cannot observe such

latent perceptions or attitudes directly, but such variables can be reflected in indicator type questions. In

our case, two sets of ratings were given by the respondent, reflecting two types of latent perceptions or

attitudes. In Table 2, indicators F11 to F117 and F21 to F214 indicate perceptions of the overall ride

comfort (Z1) and overall reliability and convenience (Z2), respectively. However, heterogeneity in

perceptions of overall comfort and overall reliability and convenience may depend on socio-economic

and other variables, such as those listed in Table 2 as X1 to X7. If we consider another latent variable,

utility (U), which defines the final choice, the overall analysis framework stands as shown in Figure 1.

Figure 1 presents the schematic diagram of the latent variable model. This framework indicates two

types of latent variables: utility and perceptions. The hypothesis is that an individual chooses the reason

which gives maximum utility. The utility is a function of latent perceptions and many other socio-

economic variables, where the latent perceptions are reflected by various indicators. The observable

choices of using transit are the expressions of these utilities.

Mathematically, the overall framework can be divided into two parts: the structural model

(represented by solid arrows) and the measurement model (represented by dashed arrows). The

structural model defines the relationship between latent variables and other observed variables (i.e.

cause and effect relationship). The measurement model defines the measurement of indicators by

the latent perception variables and selection of one reason as a function of latent utility. Considering the

stochastic nature of perceptions and utility and, at the same time, our inability to identify all factors

involved, in both structural and measurement models, random errors (v, e and y) with specific

distributional assumptions have been added.

Utility U

Service Quality Z1

Safety & Reliability, Indicators, F Riders

Attributes, X

Reason for Choosing, y

Figure 1. Schematic diagram of latent variable model.


DOI: 10.1002/atr


Structural model:

Zl ¼ llxn þ vl; l ¼ 1; 2; vl~Nð0;Sv diagonalÞ

Un ¼ blxn þ b2Zl þ "n; n ¼ 1; . . . ; 10; "n ~IID Gumbellð0; 1Þ

Measurement model:

Flp ¼ Zlap þ yp; l ¼ 1; p ¼ 1; 2; . . . ; 17; yp ~Nð0;Syp diagonalÞ

Flq ¼ Zlaq þ yq; l ¼ 2; q ¼ 1; 2; . . . ; 14; yq ~Nð0;Syq diagonalÞ

y ¼ i; if Ui; j 6¼ i

Considering the error term specification of the utility function, the probability of choosing one reason

becomes a multinomial logit equation. As the utility function itself is a function of two other latent variables,

the logit equation is conditional on the probability of having certain values of the other latent variables and

the corresponding indicator variables. In the case of indicators, it is customary to consider them as continuous

variables [21], which are only a function of latent perceptions. Again, considering the latent perception

variables as continuous variables, the likelihood function (L) of any individual observation becomes:

L ¼Z Z

Zl

expðb1xn þ b2lZlÞiP10

n¼1

expðb1xn þ b2lZlÞn

0BBB@

1CCCA�

Y17

p¼1

1

snpf

F1p � Z1ap

snp

� ��Y14

q¼1

1

snqf

F2p � Z2aq

snq

� �

�Y2

l¼1

1

s$l

fZl � llxn

s$l

� �dZl

where w[�] indicates probability density function (PDF) of normal distribution, ap and aq are

parameters to be estimated in assition to b1 and b2l, syp, syq and s l are the corresponding variance of

indicator functions and latent perceptions.

In this equation, the overall likelihood function is integrated over the distributions of two latent

perception variables; hence, it does not have a closed form. One way to estimate the likelihood function

is to use a simulation method [27].

where D indicates the number of iterations in simulation.

The overall log likelihood (LL) function of the sample of 500 observations becomes:

LL ¼X500

i¼1

Li

In this paper, we used a Halton sequence and 1000 iterations for simulation estimation. For a description

of a Halton sequence and simulation estimation, the reader is referred to Train [27]. Schematically, the

overall model takes the form illustrated in Figure 2. In this figure, the observed variables, X1 to X7, are not

directly connected to the utility function,U, because these variables are found to be statistically insignificant

if used as a direct variable in the utility function. The reason for using indicator variables is that, while the

latent perceptions or attitudes are not observable, their effects on corresponding indicators are observable.

These indicators allow identification of the latent constructs. They also contain enough information to

potentially provide for increased efficiency in model estimation [21].


DOI: 10.1002/atr

F11

F12

F13

F14

F15

F16

F17

F18

F19

F110

F111

F112

F113

F114

F115

F116

F117

F21

F22

F23

F24

F25

F26

F27

F28

F29

F210

F211

F212

F213

F214

ν1

ν2

ν3

ν4

ν5

ν6

ν7

ν8

ν9

ν10

ν11

ν12

ν13

ν14

ν15

ν16

ν17

ν18

ν19

ν20

ν21

ν22

ν23

ν24

ν25

ν26

ν27

ν28

ν29

ν30

ν31

X1 X2 X3 X4 X5 X6 X7

ω1 ω2

Reason for Choosing Transit:

Latent Utility, U

ε

Latent Perception,

Z1

Latent Perception,

Z2

Figure 2. Schematic diagram of latent choice model.


The direction and type of arrow used in Figure 2 explain the causal relationships. Solid lines indicate

direct and observable variables and relationships, and dotted lines imply latent and indirect or not fully

observable relationships. The directions of the arrows clarify that observable variables (Xi) directly

influence latent perceptions and latent perceptions directly influence the utility of having a specific

reason for choosing transit. It is clear that the indicators (F1i, F2i) do not have causal relationships

that influence behaviour, and this is reflected by showing the directions of arrows as from the latent

perceptions to the indicators.

The advantage of this model is that, although the indicator functions are used to estimate the model

parameters during the application of the model, one does not need to forecast the indicators variables. As

indicator variables are not part of the causal relationships, they are only used during the estimation stage. If

we did not have any indicator variables, the whole modelling structure would convert into a mixed

multilevel logit model of the reason for choosing transit as a function of observable variables (Xi). However,

in that case, the model would suffer an identification problem, and the estimation would not be efficient.

The model is estimated by using code written in GAUSS using the BFGS algorithm [28]. However,

given the high number of indicator variables, the model becomes complex, but convergence is very

quick. (For 1000 iterations for simulation estimation and 500 observations, it takes around 18 hours to

converge the model, depending on starting values.) The standard errors of the parameters are calculated

using the inverse of the Hessian procedure. As the model becomes very complex, the variances of the

error terms of latent and indicator variables are restricted to unity, which is a common practice in this

type of complicated situation [21]. The next section discusses the estimated model parameters.

6. ESTIMATED MODEL

The model had a total of 103 estimated parameters, which are presented in Table 3. The coefficients

of the two indicators of the two corresponding latent variables were restricted to unity for the purpose


DOI: 10.1002/atr

Table 3. Estimation results of transit usage reason with latent attributes.

Explanatory variables Parameter t-stat

ConstantReason 2 2.464 2.28Reason 3 3.016 2.86Reason 5 1.393 1.35Reason 6 2.732 2.45Reason 7 3.247 3.11Reason 8 1.620 1.08Reason 10 4.016 3.20

Z1 ride comfort (latent)Reason 2 �0.870 �1.85Reason 3 �0.648 �1.40Reason 5 �0.764 �1.75Reason 6 �1.135 �2.29Reason 7 �0.654 �1.44Reason 8 �0.745 �1.11Reason 9 �1.289 �1.55Reason 10 �0.690 �1.35

Z1 ride reliability and convenience (latent)Reason 2 1.284 3.16Reason 3 0.866 2.17Reason 4 0.866 3.56Reason 5 1.796 4.73Reason 6 1.377 3.27Reason 7 0.813 2.08Reason 8 0.927 1.59Reason 9 1.636 1.93

Latent variable model

Comfort Z1 Reliability-convenience Z1

Parameter t-stat Parameter t-stat

Structural modelAverage weekly rides 0.330 12.660 0.37 13.76Main mode

Bus 0.216 4.983 0.34 8.07Train 0.451 9.410 0.66 12.94Bus–train combined – – – –

Main purpose of tripWork 0.834 11.873 0.52 7.32School 1.384 20.554 1.11 17.06Shopping 0.686 7.782 0.68 8.35Personal business 1.047 11.800 0.79 8.50Social/recreation – – – –

Payment methodAdult monthly pass – – 0.18 3.21Ticket 0.111 2.649 0.41 8.24Others – – – –

GenderMale 0.272 6.970 0.22 5.44Female – – – –

Income group�25k 0.213 3.049 0.16 2.2915–25k 0.383 4.887 0.37 4.7125–35k �0.110 �1.186 0.13 1.5735–45k 0.021 0.275 0.10 1.3145–55k 0.416 5.150 0.17 2.1755–65k 0.159 1.750 0.24 2.6965–75k 0.336 3.946 0.40 5.16

(Continues)


DOI: 10.1002/atr


Table 3. (Continued)

Latent variable model

Comfort Z1 Reliability-convenience Z1

Parameter t-stat Parameter t-stat

75–85k 0.804 7.807 0.78 7.8185–100k 0.667 9.870 0.63 9.62100kþ – – – –

Age group65þ 1.251 15.170 1.22 14.7255–64 0.822 9.669 0.83 10.2145–54 0.929 11.727 0.71 9.0335–44 1.053 12.935 0.69 8.5025–34 1.048 13.295 0.82 10.4620–24 0.694 9.137 0.26 3.5015–19 – – – –

Latent variable model:comfort Z1

Parameter t-stat

Measurement modelManners of bus drivers 1.00 –Information availability from bus driver 1.07 41.02Safety while riding 0.73 33.00Reliability and flexibility 1.13 42.02Professionalism of fare inspectors in train 1.28 43.85Overall passenger behaviour 0.84 35.95Giving sufficient slop time to board and alighting bus/train 1.20 43.11Maintaining arrival schedule at the stop 0.94 38.29Security while riding 1.34 44.64Sufficient parking at park-arid-ride locations 1.61 47.31Overall safety of bus and train 0.67 31.18Seal availability while riding train 1.00 39.51Smoothness of riding 0.76 33.93Pleasant experience 0.75 33.74Comfortability of seats 0.80 34.89Inside temperature of bus/train 0.76 33.99Cleanliness of disembarking area of bus/ride 1.10 41.46

Measurement model:reliability-convenience Z2

Parameter t-stat

Measurement modelHelpful and courteous staff 1.00 –Schedule delay 1.04 42.86Overall cleanliness 1.00 42.18Not overcrowded 1.23 46.24Service frequency 1.04 42.85Value for money 0.87 39.10Length of travel time 0.96 41.23Route layout 0.95 41.01Convenience of connections and transfers 1.20 45.72Providing customer safety and security 0.95 41.11Providing scheduling and route information 0.94 40.72Convenience of purchasing tickets and passes 0.82 37.83Easy to access vehicles 0.90 39.92Easy to access bus stops 0.95 41.12

Likelihood at convergence: �25760.600; number of observations: 500.


DOI: 10.1002/atr



of model identification. Considering the relatively small data set compared to the large number of

parameters to be estimated, the estimated coefficients were considered statistically significant if the

corresponding two-tailed t-statistics satisfy the 90% confidence interval (t� 1.64). However, some

variables with statistically insignificant parameters were also retained in the model, because they

provide considerable insight into the behavioural process. Retention of some of the insignificant

variables was also due to the expectation that, if a larger data set was available, these parameters may

show statistical significance.

Surprisingly, trip timing was not found to be a statistically significant variable. Obviously, attitudes

towards transit are strongly influenced by trip timing as it relates to frequency of service and crowding

and, thus, comfort level; however, trip timing is highly correlated with trip purpose. In other words,

when trip purpose is work or school, the trip often takes place during a peak period. This may explain

the insignificance of trip timing. In order to avoid multicolinearity, this variable was dropped from the

model.

There was an alternative specific constant of the main utility function with respect to reasons 1, 4 and

9, which are ‘no particular reason’, ‘avoid traffic’ and ‘comfortable/relaxing’.

Reason 10, ‘environmental reason’, had the highest value, followed by reason 7, ‘convenient

service’, and 3, ‘no car available’. People with no car available are the most captive transit users;

however, it is clear that being a captive transit user is not the prime reason for using transit in Calgary. It

is encouraging that most important reason was the environmental reason, which indicates Calgarians’

understanding and dedication towards sustainable development. This is a very interesting finding that

reveals that publicity or campaigns for the environment together with a convenient transit service may

significantly increase transit usage. Interestingly, all alternative specific constants were above 1,

compared to those of the reference reasons. So, there were very few people who chose transit over other

modes of transport without any specific reason, in order to avoid traffic or for better comfort/relaxation.

Two latent variables were used in the model: ‘ride comfort’ and ‘ride reliability and convenience’.

The coefficients of these two parameters had opposite signs for all reasons. In this case, the reference

reason was ‘no particular reason’. The results indicate that perceptions towards ‘ride comfort’ had a

negative effect on all specific reasons for choosing transit, whereas perception towards ‘reliability and

convenience’ had a positive effect on having a particular reason, rather than having no specific reason

for choosing transit. This indicates ‘reliability and convenience’ is the most important factor for transit

users. Calgary transit users do not look for much comfort compared to the reliability and convenience

of the transit service.

It is clear that perceptions towards ‘reliability and convenience’ of transit service had the highest

influence when choosing transit due to ‘environmental reason’. This finding is interesting and reveals a

very basic nature in Calgarians’ behaviour. Although it has been noted that the main reason for

Calgarians’ choosing transit is environmental, transit service should also have considerable reliability

and convenience. Without reliable and convenient service, it will be difficult to attract the majority of

the transit users over the long term. It indicates that people are concerned about the environment and

the issue of future sustainability, but present demand/need has to be fulfilled. People may sacrifice ride

comfort for the environmental friendly nature of transit, but reliability and convenience are also

important.

It is also clear that the second and third most important influences on perceptions of reliability and

convenience were reasons 5 and 6, which are ‘avoiding parking’ and ‘don’t drive’. The growing

economy and population of Calgary has resulted in a number of issues: (i) increase in parking costs,

(ii) reduced availability of parking spaces in the urban core, and (iii) growing level of urbanization and,

thus, congestion that makes employees avoid driving to downtown. The findings of this survey indicate

that people tend to avoid driving due to parking costs and difficulty. The next important influence of

perception towards reliability and convenience was the less expensive nature of transit service.

Although transit fare was perceived as out of pocket cost, increasing fuel prices, travel time due to

traffic congestion, and parking costs have made transit service less costly than driving. Having a

reliable and convenient transit service may help people choose this less expensive mode of transport

compared to driving.

The latent variables representing perceptions towards ‘ride comfort’ and ‘reliability and

convenience’ were modelled as functions of a number of variables, as presented in Table 3 under


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the heading of ‘latent variable model’. These are the variables we directly observed, but found not to be

directly related to the reason for choosing transit. It is clear that the main purpose of trip was a very

important variable defining perception. For ‘ride comfort’, a school trip as the main purpose for travel

on transit had the highest influence on defining a positive perception over all other variables. Personal

business as the main purpose of the trip gave the second highest positive perception towards ‘ride

comfort’. This was followed by people aged over 65 and then by the age group of 25 to 35. The rest of

all the variables had coefficients of less than 1 for the latent perception of ‘ride comfort’.

It is interesting that males had more positive perceptions towards ‘ride comfort’ than females. Thus,

despite the fact that a high proportion of bus riders were females (as indicated in Table 1), a high

proportion of these users had lower perceptions toward transit comfort, but as they were captive riders,

they had no choice.

Retail ticket users also had more positive perceptions towards ‘ride comfort’ than the people using

monthly, senior citizen or university transit passes. Average weekly riders were positively influenced

by their perceptions toward ‘ride comfort’, but the coefficient was less than 1. People using either buses

or trains had more positive perceptions towards ‘ride comfort’ than people needing to use both bus and

train, which is intuitive. Using multiple modes of transit increases delays and variations in the

judgement of perceiving overall comfort level.

Work trips were more positively influenced by ‘ride comfort’ than shopping trips. Social or

recreation trips did not significantly contribute to the perception formation towards ‘ride comfort’.

People in different income groups had different levels of perceptions towards ‘ride comfort’.

Compared to people with an annual income level of over C$100 000, all other groups, with the

exception of the group with an annual income between C$25 000 and 35 000, had more positive

perceptions towards ‘ride comfort’. It is interesting that a higher income group (C$75 000–100 000)

had more positive perceptions than lower income groups. This finding complies with recent statistics

that, in Calgary, higher income people are increasingly moving towards transit usage. This may also be

explained by the fact that a large portion of low income groups are not really convinced to use transit,

but as captive transit users, they do so temporarily until they can afford to drive their own car. In

addition, most captive transit users have not experienced the unpleasant experience of driving on

congested roads, whereas people who have encountered both transportation modes may find transit use

relatively more comfortable.

In the case of perceptions towards ‘reliability and convenience’, older people (age 65þ) had the

most positive perceptions, followed by people using transit mostly for school trips. It is interesting that

the number of coefficients higher than 1 was lower in perceptions towards ‘reliability and convenience’

than towards ‘ride comfort’. People seemed to be more conservative in perceiving ‘reliability and

convenience’ than ‘ride comfort’.

Train users had much higher positive perceptions towards ‘reliability and convenience’ compared to

the people using only buses or combined bus and train service. Train users do not face traffic

congestion; and, train service is more frequent, which obviously makes it more reliable and convenient.

Dedicated right-of-ways for transit service may be a very effective tool in increasing positive

perceptions towards ‘reliability and convenience’ and, thus, increasing transit ridership.

Retail ticket users find the transit service more reliable and convenient than the people using other

modes of payment. This may be explained by the fact that pass users are more frequent users than retail

ticket users; therefore, they may be more familiar with the transit schedule and coverage. All other

income groups positively perceived ‘reliability and convenience’, compared to the highest income

group (over C$100 000). Similar to perceptions towards ‘ride comfort’, people within a higher income

group (C$75 000 to 100 000) had more positive perceptions than lower income groups. The influences

of age were more uniform in the perceptions towards ‘reliability and convenience’ than the perceptions

towards ‘ride comfort’. The perceptions towards ‘reliability and convenience’ decreased gradually

with decreasing age, except the 25–34 age group, who had more positive perceptions towards

‘reliability and convenience’ than adjacent age groups.

As mentioned in the previous section, the measurement model was not part of the causal relation

process, and explanations of the coefficients were not important in this case. However, incorporation of

the measurement model obviously contributes to the above findings, which was proven by estimation

of a simple multinomial logit model of the reason for choosing transit as a function of variables used in


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the structural model. All of the parameters became statistically insignificant in that case (we found it

unnecessary to report that model, as it would overwhelmingly increase the size of the paper), whereas

almost all variables were statistically significant in this joint model.

7. CONCLUSION AND RECOMMENDATION

This paper presented a critical investigation of reasons for using transit by the residents of the City of

Calgary. The main objective was the identification of critical factors that influence people to choose

transit over other modes of transport. Rather than using a mode choice model to identify variables

affecting transit choice, this paper took a different approach. In this research, we concentrated on

transit users only and critically investigated the factors that affected their choice to use transit. Using a

transit customer satisfaction survey data, conducted in 2007 for Calgary Transit, we modelled the

reason for choosing transit as a function of different attributes.

Given the complexity of behavioural processes, a simple multinomial logit model simply cannot

capture the causal relationship between the reason for choosing transit and different socio-economic

variables. However, application of a latent choice model proved very effective and revealed much

behavioural information that has implications for policy making. Although we had limitations in

finding more detailed service and household level variables, a higher number of indicator variables

(total 17þ 14¼ 31) included in the latent choice model, as a measurement equation, apparently

corrected the data deficiency biases.

The results of the analysis showed that Calgarians value ‘reliability and convenience’ over

‘ride comfort’. Different variables affect the perceptions towards ‘reliability and convenience’ and

‘ride comfort’ almost similar ways but with different magnitude. However, it is clear that older people

(age over 65) perceive most positively towards both ‘reliability and convenience’ and ‘ride comfort’. In

general, higher income people perceive mode positively towards both ‘reliability and convenience’ and

‘ride comfort’ compared to the lower income people. Payment method used by the users have different

influence, in general the retail ticket users perceive more positively towards both ‘reliability and

convenience’ and ‘ride comfort’ compared to the people using monthly passes of different types.

Males’ perception towards both ‘reliability and convenience’ and ‘ride comfort’ was more positive

than females. The main purpose for the trips also influenced perception building. Users making school

trips had more positive perceptions towards both ‘reliability and convenience’ and ‘ride comfort’ than

other types of trip takers.

The implications of these findings in determining policy are that increasing transit service reliability,

in terms of reducing scheduling delays, and convenience, in terms of frequency complying with peak

demand during the times of the day, can significantly improve transit ridership. It is also clear that

improving connectivity of train service, reducing multimodal transfers, and increasing dedicated right-

of-ways for transit would effectively improve ‘reliability and convenience’ and, accordingly, increase

transit ridership in Calgary. In addition, the application of an intelligent transportation system (ITS), in

terms of transit signal priority, advance travellers’ information system, and real-time bus arrival time

information to reduce schedule delay and waiting times, would definitely increase positive perceptions

towards ‘reliability and convenience’, thereby increasing transit ridership. Application of priority

measures, for example, transit signal priority, real-time passenger information, etc. would be very

effective transportation system management (TSM) tools to improve transit ridership.

In terms of population segmentation, it seems that Calgary Transit still has to increase the

confidence in its service by woman, people making social/recreational trips, lower income people, and

the younger population (age below 15). Despite the fact that transit users mostly belong to this category

of users, apparently a significant portion of these users are not really satisfied with the quality of service

provided, but have no other choice since they are likely captive users.

This paper has also shown how general marketing survey data can be used for behavioural research.

The data used in this paper was not intended for this type of research. A rather simple statistical

frequency analysis was the main target, in order to evaluate transit service quality. However,

application of an advanced econometric modelling technique proved that this type of data set, which

was collected for a different purpose, can be used to reveal very rich behavioural details. In this way,


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this investigation has also shown an approach for increasing the use of data collected for different

purposes in research, thereby increasing collaboration between researchers and industry.

In terms of future extension of this study, one possible avenue is to conduct panel data studies to

trace changes in perception over the years, given that the same people are surveyed in successive years.

In an alternative case that lacks panel data, meta analysis of perception changes over the years

compared to the changes in the service area and quality of Calgary Transit would be an another

extension. Additionally, Calgary Transit is in the process of implementing some ITS instrumentation to

its transit network, that is, GPS-GIS for bus tracking, real-time information provision, smart card, etc.

It would be interesting to conduct a stated preference survey to identify the impacts of these ITS related

implementations on the perceptions of ‘reliability and convenience’ and, accordingly, predict the likely

impact on transit ridership.

ACKNOWLEDGEMENTS

An earlier version of this paper was presented at 88th Annual Meeting of the Transportation Research

Board TRB, 11–15 January 2009, Washington, DC. The authors also acknowledge the help of Calgary

Transit and Hargroup Management Consultants for making the data set available for this study. This

research was funded by university start-up funds for the first two authors.

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