Model of Personal Attitudes towards Transit Service Quality

Dr. Khandker M. Nurul Habib

Assistant Professor

Department of Civil & Environmental Engineering

School of Mining and Petroleum Engineering (SMPE)

University of Alberta

Edmonton, Alberta, Canada T6G 2W2

E-mail: khandker.nurulhabib@ualberta.ca

Phone: 1-780-492-9564

Fax: 1-780-492-0249

Dr. Lina Kattan

Assistant Professor

Department of Civil Engineering

Schulich School of Engineering

University of Calgary

2500 University Drive NW

E-mail: lkattan@ucalgary.ca

Phone: 1-403-220-3010

Fax: 1-403-282 7026

Md. Tazul Islam Graduate Student

Department of Civil & Environmental Engineering

School of Mining and Petroleum Engineering (SMPE)

University of Alberta

Edmonton, Alberta, Canada T6G 2W2

E-mail: mdtazul@ualberta

Fax: 1-780-492-0249

2

Abstract: 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 towards transit service quality and attributes. A multinomial logit model combined with latent

variable models is developed to capture unobserved latent variables in defining perceptions and attitudes.

Using data from a transit customer satisfaction survey conducted in 2007 by Calgary Transit, this

approach models the reasons for 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 important policy implications. Most importantly, it is found that the people of

Calgary value ‘reliability and convenience’ over ‘ride comfort’. As for policy implications of the findings,

it is clear that improving the connectivity of train service, reducing multimodal transfers, and increasing

dedicated right-of-ways for transit would effectively increase transit ridership in Calgary.

3

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 (TRB, 2003).

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 (Hensher et al., 2003). The actual

performance of a transit system should be considered from the transit users’ perspective (Fu and Xin,

2007); and, although there has been demand for analyzing transit service quality based on how the users

perceive it (Joewono and Kubota, 2007), 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 (Yilmaz and Celik, 2008). 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 (Elander et al., 2003). 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.

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 (Day, 2008); 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

4

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.

Literature Review

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

service marketing, etc. (Shiftan et al., 2008; Guliano and Hayden, 2005; Cronin and Hightower, 2004).

Shiftan et al. (2008) 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.

5

Datz (2005) 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 (Minocha et al., 2008). Hensher et al. (2003) 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 (2005) 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 (Cervero, 2002). Increase in transit

usage is sought from even public health points of view, such as reduction of obesity (Edwards, 2007).

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 (Chang

and Schofeld, 1980; Chien and Schonfeld, 1998, 1997; Wirasinghe and Liu, 1995; Wirasinghe and

Seneviratne, 1986; Hurdle and Wirasinghe, 1980). However, unless we really identify the factors that

affect attitude formation towards transit service, we cannot understand the elements influencing passenger

6

satisfaction; and, consequently, we cannot effectively influence large numbers of people to shift towards

transit usage.

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 (Walker,

2001). We used transit passenger satisfaction survey data to estimate our model. The next sections

describe the study area and data set in detail.

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 (Grabeland, 2006). Calgary’s population growth has been

absorbed by new developments that spread in every direction, creating new neighborhoods 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

7

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 (Calgary Transportation Plan, 2005).

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% (Calgary Transportation Plan, 2005). 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% (Backgrounder on Traffic, 2008). 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.

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 (Hargroup Management Consultants,

2008). 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.

8

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 nineteen and

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

considered 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 (Backgrounder on Traffic, 2008).

Table 1: Sample Characteristics

Descriptions 2007 Census

%

2007 Survey

%

Gender Males 50 43

Female 50 57

Age Group 15 to 19 years 9 15

20 to 24 years 9 12

25 to 34 years 20 17

35 to 44 years 23 16

45 to 54 years 18 16

55 to 64 years 9 13

Over 64 years 11 10

Refused ---- 1

Household Income

Group less than $15,000 ---- 5

$15,000 to < $25,000 ---- 5

9

$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 ---- 16

Refused ---- 35

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 to 3 times a week, 25%

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

public transit more than 10 times a week (Hargroup Management Consultants, 2008).

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 to 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.

10

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.

Table 2: Specific Questions Regarding Transit Service

Quality of Calgary Transit Service: Ride

Comfort (Z1)

F11 Manners of bus drivers

F12 Information availability from bus driver

F13 Safety while riding

F14 Reliability and flexibility

F15 Professionalism of fare inspectors in train

F16 Overall passenger behaviour

F17 Giving sufficient stop time to board and alight

bus/train

F18 Maintaining arrival schedule at the stop

F19 Security while riding

F110 Sufficient parking at park-and-ride locations

F111 Overall safety of bus and train

F112 Seat availability while riding train

F113 Smoothness of riding

F114 Pleasant experience

F115 Comfortability of seats

F116 Inside temperature of bus/train

F117 Cleanliness of disembarking area of bus/ride

Attributes of Transit Service: Reliability and

Convenience (Z2)

F21 Helpful and courteous staff

F22 Schedule delay

F23 Overall cleanliness

F24 Not overcrowded

F25 Service frequency

11

F26 Value for money

F27 Length of travel time

F28 Route layout

F29 Convenience of connections and transfers

F210 Providing customer safety and security

F211 Providing scheduling and route information

F212 Convenience of purchasing tickets and passes

F213 Easy to access vehicles

F214 Easy to access bus stops

Attribute of the person and choice

X1 Average weekly rides

X2 Main mode of transit: bus, train, both bus and

train

X3 Main purpose of trips: work, school,

shopping, personal business, social/recreation

X4 Payment method: adult monthly pass, ticket

from a book of tickets, others

X5 Gender: male, female

X6

Income group: <15k, 15-25k, 25-35k, 35-45k,

45-55k, 55-65k, 65-75k, 75-85k,85-100k,

100k+

X7 Age group:15-19, 20-24, 25-34, 35-44, 45-54,

55-64, 65+

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. No Particular Reason

2. Less Expensive/Save Gas/High Gasoline Prices

3. No Car Available

4. Avoid Traffic

12

5. Avoid Parking

6. Don't Drive

7. Convenient Service

8. Faster Travel Time

9. Comfortable/Relaxing

10. Environmental 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 analyzed variables, such as the trip and socio-economic

characteristics.

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 modeling framework.

Modeling 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

13

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

(Washington et al., 2003).

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: Schematic Diagram of Latent Variable Model

Utility

U

Service Quality Z1

Safety & Reliability, Z2

Indicators, F Riders Attributes, X

Reason for Choosing, y

14

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 (ω, ε, and υ) with specific distributional assumptions have been

added.

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 (Walker, 2001), which are only a function of latent perceptions. Again,

15

considering the latent perception variables as continuous variables, the likelihood function (L) of any

individual observation becomes:

sperceptionletent and functionsIndicator of variancesingcorrespondtheareσandυq

σ,υp

σ

2and oassition tin estimated be toparameters are and

ondistributi normal of Function)Density ty (Probabili pdf indicatesφ[.]where

2

1

1*

2214

1

1*

1117

1

1*

10

1

)21

exp(

)21

exp(

1

l

lqp

ldZ

l

nx

llZ

l l

q

qZ

pF

q qp

pZ

pF

p p

lZ

nnl

Zln

x

ilZ

lnx

L

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 (Train, 2002).

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

16

In this paper, we used a Halton sequence and 1,000 iterations for simulation estimation. For a description

of a Halton sequence and simulation estimation, the reader is referred to Train (2002). 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 (Walker, 2001).

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 (Aptech Systems,

2006). However, given the high number of indicator variables, the model becomes complex, but

convergence is very quick. (For 1,000 iterations for simulation estimation and 500 observations, it takes

17

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 (Walker, 2001). The next section discusses the

estimated model parameters.

Figure 2: Schematic Diagram of Latent Choice Model

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: y

Latent

Utility, U ε

Latent

Perception,

Z1

Latent

Perception,

Z2

18

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 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.

Table 3: Estimation Results of Transit Usage Reason with Latent Attributes

19

Table 3 Continued………….

20

Table 3 Continued………….

21

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.

22

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

23

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 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

24

annual income between C$25,000 to 35,000, had more positive perceptions towards ‘ride comfort’. It is

interesting that a higher income group (C$75,000 to 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 65+) 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

25

‘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 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.

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 17+14 = 31) included

in the latent choice model, as a measurement equation, apparently corrected the data deficiency biases.

26

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 signficantly 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, e.g.

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

27

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 modeling 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, 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, i.e.

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.

Acknowledgement

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

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

28

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.

References

- Aptech Systems, GAUSS User’s Manual. Maple Valley, CA, 2006.

- Backgrounder on Traffic: Current and Future Scenarios Calgary and Region, City of Calgary:

http://www.calgary.ca/DocGallery/BU/trans_planning/forecasting/backgrounder_region_travel_5

of5.pdf, Accessed June 2008.

- Calgary Economic Development website:

http://www.calgaryeconomicdevelopment.com/relocateAndExpand/RECalgaryEconomy/labourfo

rce.cfm, Accessed in June 2008.

- Calgary Transportation Plan (2005):

http://www.calgary.ca/DocGallery/BU/trans_planning/CTP_2005/ctp_2005_main_report.pdf

Accessed May 2008.

- Cervero, R. Built environment and mode choice: towards a normative framework. Transportation

Research Part D 7, 2002, pp. 265-284.

- Chang SK and Schonfeld P. “Integration of Fixed-and Flexible-route Bus Systems”

Transportation Research Record, 1980, pp. 51-57.

- Chien S and Schonfeld P. “Joint optimization of a Rail Transit line and its feeder bus system”,

Journal of Advanced Transportation, 1998, pp. 253-284.

- Chien S. and Schonfeld P. “Optimization of Grid Transit System in Heterogeneous Urban

Environment”. Journal of Transportation Engineering, Vol. 123, No. 1, 1997, pp. 28-35.

- Cronin, J.J. and Hightower, R. “An evaluation of role of marketing in public transit

organizations”. Journal of Public Transportation 7(2), 2004, pp. 17-36.

29

- Datz, A. “Teenage attitudes and perceptions regarding transit use”. NCTR Project 576-14, FDOT

Project BD 549-7, National Center for Transit Research, Center for Urban Transportation

Research, University of South Florida, 2005.

- Day, N. Joint Modelling of Work Trip Timing and Mode Choice. M.Sc Engineering Thesis,

Department of Civil Engineering, University of Toronto, 2008.

- Edwards, R. D. “Public transit, obesity and medical cost: assessing the magnitude”. Preventive

Medicine 46, 2007, pp. 14-21

- Elander, J., West, R. and French, D. “Behavioural correlation of individual differences in road-

traffic crash risk: an examination of methods and findings”. Psychological Bulletin 113(2), 1993,

pp. 279-294.

- Fu, L. and Xin, Y. “A new performance index for evaluating transit quality of service”. Journal of

Public Transportation 10(3), 2007, pp. 47-69.

- Grabeland B. “Calgary Battles Urban Sprawl”, Globe and Mail, Canadian Press, Monday, June 5,

2006, p. A9.

- Guliano, G. and Hayden, S. Marketing public transport. In Handbook of Transportation vol. 6:

635-649, In: Hensher, D., and Button, K.J. (Eds). Pergamon Press, Oxford, 2005.

- Hargroup Management Consultants: “Final Report: Calgary Transit Customer Satisfaction

Survey, 2007”, 2008.

- Hensher, D., A., Stopher, P. and Bullock, P. “Service quality-developing a service quality index

in the provision of commercial bus contract”. Transportation Research Part A 37, 2003, pp. 499-

517.

- Hurdle, V.F. and Wirasinghe, S.C., “Location of Rail Stations for Many to One Travel Demand

and Several Feeder Modes”, Journal of Advanced Transportation, Vol. 14-1, 1980, pp. 29-46.

30

- Joewono, T., B. and Kubota, H. “User perception of private paratransit operation in Indonesia”.

Journal of Public Transportation 10(4), 2007, pp. 99-118.

- Minocha, I., Sriraj, P., S., Metaxatos, O. and Thakuriah, P. “An analysis of transit quality of

service and employment accessibility for the greater Chicago Region”. 87th Annual Meeting of

Transportation Research Board, Washington DC, 2008, CD-ROM.

- Shiftan, Y., Outwater, M., L. and Zhou, Y. “Transit market research using structural equation

modelling and attitudinal market segmentation”. Transport Policy (forthcoming), 2008.

- Steg, L. “Car use: lust and must. Instrumental, symbolic and effective motives for car use.”

Transportation Research Part A, 39, 2005, pp. 147–162.

- Train, K. Discrete Choice Method with Simulation. Cambridge University Press, 2002.

- Transit Capacity and Quality Service Manual 2nd

Edition. Transit Cooperative Research Program

(TCRP) Report 100, Transportation Research Board, Washington DC, 2003.

- 1999 Transit Customer Satisfaction Index. Final report for DOT State of Florida, prepared by

Center for Urban Transportation Research, College of Engineering, University of South Florida,

2000.

- Walker, J. Extended Discrete Choice Models: Integrated Framework, Flexible Error Structures

and Latent Variables. PhD thesis, MIT, 2001.

- Washington, Karlaftis and Mannering. “Statistical and Econometric Methods for Transportation

Data Analysis” Chapman & Hall/CRC, 2003.

- Wirasinghe, S.C. and Liu, G. “Optimal Schedule for a Transit Route with One Intermediate Time

Point.” Transportation Planning and Technology 19, 1995, pp. 121-145.

Wirasinghe, S.C. and Seneviratne P., “Rail Line Length in an Urban Transportation Corridor”,

Transportation Science, Vol. 20-4, 1986, pp. 237-245.