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Students’ mobility attitudes and sustainable transport mode choice
Mattia Cattaneoa,b, Paolo Malighettia,c, Chiara Morlottia,d, Stefano Palearia,e
a Department of Management, Information and Production Engineering; University of
Bergamo; Dalmine; Italy
b mattia.cattaneo@unibg.it
c paolo.malighetti@unibg.it
d chiara.morlotti@unibg.it
e stefano.paleari@unibg.it
Paper submitted on 15 August 2017
Paper revised on 05 March 2018
Abstract
Purpose
This study explores the propensity of university students to use different sustainable transport
modes, taking into account individual and specific trip characteristics, as well as students’
psychological traits (i.e. attitudes).
Methodology
Using the transport mode preferences of 827 students who responded to a travel survey, a two-
step analysis is conducted. The first step examines the effects of individual characteristics,
travel experience, and origin or destination features on students’ stated preferences (i.e. self-
selected values assigned to personal attitudes). The second step analyses students’ travel mode
choices, given their intrinsic mobility attitudes.
Findings
The results suggest that informing students about environmental issues increases their
propensity to use sustainable mobility, leading to an average decrease in private transport usage
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of 5.8%. Interestingly, improving the public transport service and promoting sustainable
transport mobility have different impacts on individual campus areas. For campuses located in
the city centre and in the historical hamlet, improvements in public transport are found to
decrease solo driving by 3.3% and 5.3%, respectively. In suburban areas, this value increases
to 9.5%.
Originality
This work makes two contributions to the literature. First, it focuses on an unexplored setting,
namely, that of a multi-campus university, with districts located in three different areas. This
is used to explain how students are influenced by their travel experience and the cultural
framework in which they are embedded. Second, the two-step analysis leads to a deeper
understanding of the differences between attitudes and ‘intrinsic attitudes’, and their relative
influence on the preferred alternative.
Keywords: mode choice; sustainability; multi-campus university; transport mode; higher
education
Acknowledgements: We wish to thank all of the participants at the XXVII RSA AiIG 2016 in
Bergamo for their comments and ideas.
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1. Introduction
European legislation is currently proposing several programmes to increase the use of
sustainable transport modes in order to limit CO2 emissions caused by passive means of
transport (European Commission, 2016). For example, the Urban Mobility Package (2013)1
aims to accelerate sustainable urban mobility plans and to implement new sustainable transport
modes. This topic is attracting greater academic attention, as scholars focus increasingly on
sustainable mobility (e.g. Dong, Ma, and Broach, 2016; M.V. Johansson, Heldt, and Johansson,
2006; Zhou, 2014) in order to propose policy suggestions that can help institutions promote the
so-called ‘non-driving-alone’ solutions (Zhou, 2016).
In the past decade, the literature has focused on university students’ travel behaviour
(e.g. Limanond, Butsingkorn, and Chermkhunthod, 2011; Páez and Whalen, 2010; Rotaris and
Danielis, 2014; Whalen, Páez, and Carrasco, 2013; Zhou, 2012), as students represent a
significant percentage of a regions’ travelling population (Khattak, Wang, Son, and Agnello,
2011), with important characteristics. For example, compared with other categories of
passengers, students are known to be more flexible with respect to the variety of transport
modes they use (Limanond et al., 2011; Whalen et al., 2013): their class schedules are not fixed,
attendance is often non-mandatory (E.M. Delmelle and Delmelle, 2012). Furthermore, policies
encouraging shared, public, and active transport modes (e.g. walking and cycling) aimed at
university students have a positive impact in the short term (e.g. reducing air pollution,
congestion, and related health consequences) and in the long term (e.g. shaping students’
attitudes towards a future responsible and eco-friendly commuting choice) (Limanond et al.,
2011; Shannon et al., 2006).
When implementing sustainable policy decisions, universities and local and regional
stakeholders need to consider several factors (Soria-Lara et al., 2017). Among these, the
structure of the local physical environment is recognized as having a crucial influence on the
4
choice of travel mode (e.g. Cervero, 2002; Dong et al, 2016; Rodrı́guez and Joo, 2004). This
influence inevitably increases the interest in the specific geo-localization of a university and its
facilities in the area where they are located. However, the literature on students’ accessibility
has considered only universities as a whole, without focusing on their distribution across
territories. Indeed, not all universities have a large campus established in a homogeneous area,
especially in some university systems, such as those in Italy (Goglio and Parigi, 2016; Rotaris
and Danielis, 2014). Being more spread out enables projects that favour sustainable transport
modes, which vary according to the campus’s location, but at the same time, can be promoted
and implemented by a single athenaeum.
Therefore, this study investigates a representative sample of students enrolled at the
University of Bergamo. This university, located in the north of Italy, is an appropriate
framework for a number of reasons. First, its campuses are situated in three different areas,
facilitating an understanding of students’ transport preferences based on territories’
characteristics. The three areas are as follows: 1) the historical hamlet, located in the old city
centre, with some controlled traffic zones and a few expensive paid car parks; 2) the city centre,
which is more easily accessible by public transport and, similar to the historical hamlet, is
characterized by scarce paid car parks; and 3) the industrial district, located in a suburban area,
which is well equipped with free car parks, but is less accessible by public transport. Second,
the university hosts various faculties, including humanities, law and economics studies, and
engineering. This is not a negligible distinction, because students attending different faculties
(e.g. engineering and social sciences) are known to have different attitudes and personalities,
which can affect their mobility patterns (e.g. Kafetsios, Maridaki-Kassotaki, Zammuner,
Zampetakis, and Vouzas, 2009; Kim, Schmöcker, and Fujii, 2016; Sánchez-Ruiz, Pérez-
González, and Petrides, 2010). Many scholars have indeed highlighted that psychosocial
factors (associated with students’ disciplinary attitude) can influence students’ preferred means
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of transport (e.g. Bamberg, Hunecke, and Blöbaum, 2007; Heinen, Maat, and Wee, 2011; V.M.
Johansson et al., 2006; Kerr, Lennon, and Watson, 2010; Şimşekoğlu, Nordfjærn, and Rundmo,
2015). Third, the University of Bergamo faced 12.9% growth in enrolled bachelors and masters
students in the period 2008–2015, as compared to a decrease of 13.6% in the Italian system
overall.2 This countertrend allows the authors to properly account for the transport factors
related to this growth. An increase in the number of enrolled students can introduce positive
impacts to the community and to the university itself. However, negative externalities can also
arise in terms of transport, generally because the local infrastructure is inadequate. As such,
analysing students’ transport mode choices at a growing university would favourably help
policymakers to understand the factors that contribute to adopting sustainable transport.
Relying on an online survey conducted on 827 students (6% of the entire student body) at
the University of Bergamo during September 2012, this study estimates students’ travel mode
choices among various levels of sustainable alternatives at a multi-campus university. In doing
so, the study considers not only territorial and individual characteristics, but also students’
psychological traits.
The reminder of this paper is organized as follows. The next section briefly introduces
previous studies on students’ transport mode choices. Section 3 describes the research design
and methodology, and Section 4 reports preliminary results. Section 5 presents the outcomes
of the empirical analyses and the relative policy implications. Finally, Section 6 summarizes
the conclusions.
2. Literature Review
2.1 University students’ mobility
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There are several reasons why the mobility of university students has proved to be an
interesting topic for scholars. From a higher education perspective, investigating students’
behaviour is essential in a context where universities must increasingly compete for students
in order to attract financial resources (e.g. Cattaneo, Malighetti, Meoli, and Paleari, 2017;
Long, 2004; Teixeira, Rocha, Biscaia, and Cardoso, 2014; Wilkins, Shams, and Huisman,
2013). Notwithstanding several university-level factors that might increase the attractiveness
of a university to students (e.g. prestige, internationalization, or tuition fees), scholars have
largely ignored the costs associated with transport services (Cattaneo, Malighetti, Paleari, and
Redondi, 2016). However, these costs are some of the most crucial determinants of university
choice (e.g. Alm and Winters, 2009; Kenyon, 2011; Long, 2004) and a major reason
discouraging enrolment or the continuation of studies at distant universities (Gibbons and
Vignoles, 2012; Kenyon, 2011).
From a transport perspective, university students are recognized as an important part of the
travelling population. At the same time, they have several unique travel behaviours (Khattak
et al., 2011), owing to greater flexibility (Limanond et al., 2011) and more alternatives from
which they can benefit (Whalen et al., 2013). University students’ mobility has become
increasingly important as universities, known as big trip attractors (Whalen et al., 2013), try to
develop a more sustainable environment (e.g. Balsas, 2003; E.M. Delmelle and Delmelle,
2012; Gombert-Courvoisier, Sennes, Ricard, and Ribeyre, 2014; Hancock and Nuttman, 2014;
Lo, 2015; Miralles-Guasch and Domene, 2010; Pàez and Whalen, 2010; Shannon et al., 2006;
Van Weenen, 2000), given the increasing air pollution and traffic congestion caused by
students’ car usage (Klöckner and Friedrichsmeier, 2011; Limanond et al., 2011; Rotaris and
Danielis, 2014). Therefore, the need to develop management policies that are more student-
oriented is currently a priority for higher education institutions.
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2.2 Towards students’ sustainable transport modes
Several policies promote university students’ sustainable mobility, including an increase in
parking fees (or reducing parking permits) and improving public transport services (e.g. in
terms of safety, punctuality, convenience, and coverage). These policies have been studied and
suggested for numerous universities in different case studies, including at the University of
Western Australia (Shannon et al., 2006), the University of Idaho in the US (E.M. Delmelle
and Delmelle, 2012), the Suranaree University of Technology (Limanond et al., 2011), the
University of Trieste in Italy (Rotaris and Danielis, 2014), the Deakin University in Australia
(Hancock and Nuttman, 2014), the University of Beirut (Danaf, Abou-Zeid, and Kaysi, 2014),
the University of North Carolina at Greensboro (Sultana, 2015), and the University of Coimbra
in Portugal (Cruz, Barata, Ferreira, and Freire, 2017). Dedicated programmes have also been
promoted by scholars to discourage the use of private means of transport rather than active
modes (e.g. cycling and walking), as in the case of the Ohio State University (Akar, Fischer,
and Namgung, 2013), the McMaster University in Canada (Lavery, Páez, and Kanaroglou,
2013; Whalen et al., 2013), and the Aristotele University of Thessaloniki in Greece (Pitsiava-
Latinopoulou, Basbas, and Gavanas, 2013). In this regard, analysing the University of
Michigan-Flint, Rybarczyk and Gallagher (2014) highlighted that the efficacy of these
programmes can be enhanced by increases in street lighting and in traffic enforcement. Zhou
(2012, 2014, 2016) suggests that a cheaper and more frequent transit system at the University
of California -Los Angeles- would allow students to reach bus stops more easily. Interestingly,
given the trend to reduce carbon-intensive travel activity, carpooling programmes are often
proposed as sustainable incentives as well, as in the case of the University of Maryland
(Erdoğan, Cirillo, and Tremblay, 2015).
To the best of the authors’ knowledge, the above-mentioned policies are all based on studies
that focus on single-campus universities. An exception is the work of Rotaris and Danielis
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(2014), who identify five different campuses at the University of Trieste. Each location has its
own characteristics, such as the number of parking lots and the availability of public transport
services. Notwithstanding the importance of discerning between locations (e.g. Dong et al.,
2016; Rodrı́guez and Joo, 2004), the authors do not examine the individual traits (physical and
psychological) of the students enrolling in each of the different faculties (e.g. engineering and
social sciences). These aspects play an important role in students’ transport mode choices. For
instance, engineering students are found to be more aware of environmental issues than are
students enrolled in the social sciences (Kim et al., 2016). As such, analysing students’
attitudes, that is, the subjective importance that individuals assign to a specific item (M.V.
Johansson et al., 2006), may help to explain why people implement eco-friendly behaviour
(e.g. P. Whannell, Whannell, and White, 2012; Willis, Manaugh, and El-Geneidy, 2015),
which, in turn, is known to be a determinant of transport mode choice. Therefore, investigating
people’s attitudes towards, for instance, safety and comfort, is crucial in transport mode choice
studies, because this can aid policymakers in promoting the use of sustainable transport modes
(Bopp, Kaczynski, and Wittman, 2011; Duque, Gray, Harrison, and Davey, 2014; Fürst, 2014;
M.V. Johansson et al., 2006; Klöckner and Friedrichsmeier, 2011; Schwanen and Mokhtarian,
2005).
Therefore, this study aims to contribute to the present literature by investigating different
levels of students’ sustainable transport mode choice in a multi-campus university framework.
Here, the authors consider territorial characteristics, both at the origin and at the destination, as
well as individual characteristics. In the latter case, these include physical (e.g. sex and age)
and psychological characteristics (e.g. attitudes towards safety, comfort, and, in particular,
sustainability).
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3. Data and Methodology
3.1. Data collection
The data used in this study were collected in an online survey conducted on students of the
University of Bergamo during September 2012 using the Qualtrics Online Survey Software.
The survey investigates students’ transport mode choices from private (cars and motorcycles),
shared (carpooling), public (buses, trams, and trains), and active (cycling and walking)
transport options. An invitation to participate in the online survey was sent by email to all
students enrolled at the University of Bergamo at the beginning of September 2012. Students
were asked to complete a form anonymously on their transport preferences before 15 October
2012. The choice of an online survey was governed by several advantages that this method
provides. For example, in addition to convenience in terms of cost and time (e.g. Evans and
Mathur, 2005; Wright, 2005), the method enables the elimination of the so-called ‘interviewer-
bias’ (e.g. Evans and Mathur, 2005) and provides access to a greater number of individuals. In
particular, using the university’s email system, an online survey can be used to obtain
information from students who do not attend the university every day or who want to protect
their anonymity.
3.2. Survey structure and validity
The survey is divided into two main sections. The first section includes questions on a
respondent’s profile (i.e. age, sex, and field of study), whereas the second identifies students’
intrinsic attitudes to mobility (e.g. safety, comfort, and sustainability) and maps respondents’
transport mode choices.3 Overall, the response rate is 9%, which, after cleaning, yielded 827
responses (6% of the 14,341 students enrolled at the University of Bergamo).4 The majority of
respondents (460) attend the campus located in the historical hamlet. The campuses located in
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the city centre and in the industrial district are represented by 30% and 14% of the respondents,
respectively.
A validity check is used to test whether the data are representative of the population in
terms of campuses attended. As in Zhou (2016), the expected number of students from each
district () is computed and compared with the observed number (). Specifically,
= ∙ ,
where and represent the number of respondents (827) and the percentage of the total
number of students on each campus, respectively. Table 1 shows the comparison between the
observed number of respondents, the total student population, and the expected number of
respondents. The observed values () are very close to the expected values (), and the Chi-
square test suggests that the data are not statistically significantly different from the population
of reference.
Table 1 – Observed and expected respondents per district location
Campus Student
population
%
Population
(
)
Respondents
()
Respondents
(%)
Expected
Respondents
(
)
Industrial
District
2,081 15 117 14 120
City Centre
4,756
33
250
30
274
Historical
Hamlet
7,504 52 460 56 433
3.3. Methodology
Studying transport mode choice in real life is complex, because it requires dealing with
individual preferences (M.V. Johansson et al., 2006), which are, in turn, influenced by
individual characteristics. In this study, Safety, Comfort, and Sustainability are considered as
preferences that may impact transport mode choice; these preferences are called ‘attitudes’ in
the literature (e.g. Akar et al., 2013; Bopp et al., 2011; M.V. Johansson et al., 2006). M.V.
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Johansson et al. (2006) define an attitude as the subjective importance that an individual assigns
to a specific item. Based on this definition, this study tries to disentangle the components of
the values attributed to Safety, Comfort, and Sustainability, namely, the attitudes influenced by
individual characteristics, travel experience, and origin or destination features, as well as those
of ‘intrinsic attitudes’, considered to be the tendency or predisposition of individuals to a
specific concept (LaPiere, 1934).
For this purpose, the analysis is conducted in two different steps. First, three two-limit Tobit
regressions are performed to understand the effects of individual characteristics, travel
experience, and origin or destination features on students’ stated preferences (i.e. self-selected
values assigned to personal attitudes) for Safety, Comfort, and Sustainability. Second, in trying
to understand which factors influence the sustainable transport mode choices of students at a
multi-campus university, the analysis relies on multinomial logit regressions,5 which include
the unexplained parts of Safety, Comfort, and Sustainability (‘intrinsic attitudes’). Statistically,
these are represented by the residuals from the two-limit Tobit analyses.6 The chosen reference
case is driving solo, denoting the choice to travel to university by a private means of transport,
such as a car or a motorcycle. This is compared to three levels of increasingly sustainable
transport modes (i.e. shared, public, and active modes). At the first level, the shared solution
implies the use of a private car that is shared with other students. The second level includes
public transport services. These are motorized forms of transport but can accommodate a
considerable number of people, thus reducing the level of released pollution per capita.
Furthermore, these are generally powered by lower polluting fuels. Finally, the third level
considers active mobility (i.e. moving by bicycle or walking). This mode of transport does not
release any air pollutants and is acknowledged as having positive health benefits for people
(e.g. Pucher, Buehler, Bassett, and Dannenberg, 2010; Saelens, Sallis, and Frank, 2003;
Sælensminde, 2004).
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To perform the analysis, this study first considers the University of Bergamo as a whole,
and then conducts the same multinomial regression analyses for the three specific Bergamo
campuses in order to take into account the different infrastructures of each district (Figure 1).
Figure 1 – Transport infrastructures around the campuses of the University of Bergamo (1-
industrial district; 2-city centre; 3-historical hamlet)
The separation of the analysis into two steps is crucial, both from a methodological point
of view and from a policymaker’s perspective. Indeed, the two-step analysis prevents the
double counting of characteristics that may affect both attitudinal factors and transport mode
choice. Further, considering ‘intrinsic attitudes’ enables the proposal of effective policy
decisions (Schwanen and Mokhtarian, 2005) that may impact students’ eco-friendly behaviour
in the medium/long term. Currently, policy implications are based only on observable
characteristics that may be effective in the short term as temporary solutions.
3.4. Definition of the variables
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As independent variables, the individual characteristics of the respondents, such as sex
(Male) and age (Age), and other variables referring to the trip and the mode of transport are
considered.7 These variables are defined as follows:
- Comfort is a five-point Likert-scale variable, based on the question ‘How much does
comfort influence your transport mode choice?’ Here, a value of one indicates a very
low influence and five indicates a very high influence;
- Sustainability is a five-point Likert-scale variable, based on the question ‘How much
does the ecology of a means of transport influence your choice?’ Here, a value of one
indicates a very low influence, and five indicates a very high influence;
- Safety is a five-point Likert-scale variable, based on the question ‘How much does
safety influence your transport mode choice?’ Here, a value of one indicates a very low
influence and five indicates a very high influence;
- Traffic Congestion is the ratio between the maximum time and the average time
(without traffic) taken to travel from the town of origin to the university district by car.
To gather this information, the travel times from the town of origin to the university
districts during daily peak hours are recorded for a month using Google Maps;
- Distance is the road distance in kilometres from the municipalities where students live
to the destination university district;
- Public Availability is a five-point Likert-scale variable, based on the question ‘How
would you rate the availability of public transport service to reach the university?’
Here, a value of one is ‘absent’, and five is ‘very efficient’;
- Private Availability is a three-point Likert-scale variable, based on the question ‘How
would you rate your car/motorbike availability?’ Here, a value of one represents ‘not
available’ and three denotes ‘totally available’.
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- Pollution stands for the average level of carbon monoxide of students’ municipalities
of origin, measured in micrograms per cubic meter. To measure the levels of carbon
monoxide, daily information about its concentration in the atmosphere were collected
for a month from ilmeteo.it, a website of Italian weather conditions.
Following the theory that the faculty attended could impact students’ attitudes (e.g. Kim et
al., 2016; Tikka, Kuitunen, and Tynys, 2000), two dummy variables are considered in the first
step of the analysis, representing the field of study (i.e. Engineering and Law & Economics),
where the faculty of humanities represents the reference case. In the multinomial logit
regressions, the analysis considers the sizes of parking lots (Parking Lots), measured in squared
kilometres, within a range of 500 m from the university location. The availability of parking
lots is acknowledged to have a strong impact on students’ choices of transport modes (e.g. E.M.
Delmelle and Delmelle, 2012; Shannon et al., 2006).
4. Preliminary Results
The results of the descriptive analysis suggest that 4% of students go to university using an
active mode (cycling or walking), 7% use carpooling, and 56% rely on public transport.
Therefore, sustainable transport modes are used by 77% of students in the sample. The
remaining 281 students prefer to travel by car or motorcycle. Interestingly, there is
heterogeneity in the share of each transport mode according to the districts to which students
are traveling. In particular, 58% of students (68 out of 117) whose districts are located outside
the city centre use private modes of transport, 23% use the first level of sustainable transport
(carpooling), and none travel by bicycle or on foot. The city centre and the historical hamlet
have similar percentages of students using the highest level of sustainable transport mode.
Public transport is used predominantly for the campus located in the historical hamlet (70% of
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the sample), and it is preferred by 117 students out of 260 in the case of the city centre
university campus (see Figure 2). Figure 3 shows the percentages of sustainable transport
modes (shared, public, and active mobility) used in the municipalities around the University of
Bergamo.
Figure 2 – Share of University of Bergamo’s students using each transport mode per district
location
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Figure 3 – Percentage of sustainable transport mode use around the University of Bergamo
With regard to the variables representing students’ attitudes, Comfort is, in general, more
important to students, with an average value of 3.17. This is followed by Sustainability and
Safety, with average values of 2.39 and 2.66, respectively (see Figure 4). These values vary
slightly by faculty. Specifically, engineering students consider comfort and safety to be more
important, with values of 3.55 and 2.70, respectively, but show the lowest value for the
Sustainability variable. Humanities students care more about the ecology of the means of
transport (2.58) and prefer feeling safe to being comfortable. Finally, law & economics students
have the lowest average score on Safety, whereas their scores for the other variables fall
between those of the engineering and humanities populations.
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Figure 4 – Stated preferences towards safety, comfort, and sustainability
Table 2 shows the descriptive statistics of the data. The sample is 77% female. Despite an
age range of 19 to 63 years of age, only 7% of respondents are over 28 years old, and the
average age is 23. The average distance is about 25 km, ranging from 1 to 133 km. Only 83
students live further than 50 km from the university location, and 29% of the respondents travel
from outside the province of Bergamo. Traffic congestion causes an average travel time
increase of 13%, and only in rare cases does traffic lengthen the travel time by more than 30%
(24 cases out of 827). The availability of public and private transport modes have average
values of 3.8 and 2.2, respectively, indicating high availability of both sustainable and non-
sustainable transport modes. For public transport modes, only 46 students report not having a
public transport service in their town that can connect them to the university, and 40 evaluate
public transport as being scarce. Similarly, 80% of students can use a private transport mode
to travel to university. The level of carbon monoxide is heterogeneous, with an average value
of 371.237 µg/m3, ranging from a minimum of 100.600 µg/m3 to a maximum of 702.600 µg/m3.
As shown in Figure 5, the municipalities with the highest levels of carbon monoxide are
concentrated next to the city of Bergamo and are moving closer to the provinces of Milan and
Monza Brianza (to the West of Bergamo). Finally, the Parking Lots variable specifically
18
represents the availability of parking at the three university campuses. The industrial district
has the largest parking area (3.492 km2), followed by the historical hamlet (1.144 km2) and the
city centre (0.339 m2).
Table 2 – Descriptive statistics of variables
Mean
Std. Dev.
Min
Max
Male
0.334
0.472
0
1
Age
23.267
4.739
19
63
Safety*
2.661
1.283
1
5
Comfort*
3.174
1.337
1
5
Sustainability*
2.392
1.230
1
5
Traffic Congestion
1.128
0.719
1
1.562
Distance
25.137
19.159
1.250
132.997
Public Availability
3.805
1.097
1
5
Private Availability
2.192
0.738
1
3
Pollution
371.237
86.857
100.600
702.600
Parking Lots
1.232
0.985
0.339
3.492
Industrial District
0.141
0.349
0
1
City Centre
0.302
0.460
0
1
Historical Hamlet
0.556
0.497
0
1
*To ease interpretation, the table considers the stated preferences’ value
Figure 5 – Carbon monoxide levels of municipalities around the University of Bergamo
5. Results and Discussion
19
Table 3 presents the results of the two-limit Tobit regressions with robust standard errors.
Interestingly, Age positively affects all of the stated preferences considered. Specifically, five
additional years of age increases the importance given to Safety, Comfort, and Sustainability
by around 0.230, 0.175, and 0.275 points, respectively. Even without examining the extent to
which age enhances attitudes towards sustainability, literature interestingly explores the effect
of education on individuals’ eco-friendly behaviour. Assuming that older students have studied
for a larger number of years, the outcome is confirmed by scholars who show that people with
more years of schooling have a greater awareness of environmental issues (Ostman and Parker,
1987; Scott and Willits, 1994). Although distance from the place of study affects the
importance students place on Safety (+0.009), Traffic Congestion positively affects the interests
of respondents only in terms of the sustainability of the transport mode used (+2.084). The
availability of private and public transport modes have opposite influences on Comfort;
students served by a better public transport service place a lower importance on comfort (-
0.140), whereas private availability has a positive effect on comfort (+0.634). Furthermore,
having a car available results in students being less concerned about the sustainability of the
means of transport (-0.321). Interestingly, living in a municipality with a high concentration of
carbon monoxide increases the importance students place on Safety and Sustainability. This
result suggests that the environment where people live affects their psychological traits. In
these terms, prior studies have shown that experiencing environmental pollution leads to eco-
friendlier behaviour (e.g. Finger, 1994; Nilsson and Küller, 2000). Finally, Engineering and
Law & Economics have interesting influences on students’ preferences. Specifically, the
weights placed on Comfort increase by 0.500 and 0.288 points, respectively, for these students
relative to Humanities students. In contrast, students from the humanities consider the
sustainability aspect more than their colleagues in engineering and law & economics do by
20
0.624 and 0.511 points, respectively. This confirms that the field of study may impact
individuals’ awareness of environmental issues (Kim et al., 2016).
Table 3 – Two-limit Tobit regression results with robust standard errors
Safety
Comfort
Sustainability
Male
0.203
-0.084
-0.016
(0.143)
(0.150)
(0.139)
Age
0.046***
0.035**
0.055***
(0.016)
(0.017)
(0.019)
Traffic Congestion
-1.400
-0.990
2.084**
(0.905)
(0.938)
(0.964)
Distance
0.009**
-0.002
0.000
(0.004)
(0.004)
(0.004)
Public Availability
-0.073
-0.140**
0.025
(0.063)
(0.068)
(0.064)
Private Availability
0.079
0.634***
-0.321***
(0.096)
(0.100)
(0.096)
Pollution
0.002**
0.001
0.002**
(0.001)
(0.001)
(0.001)
Engineering
0.095
0.500**
-0.624***
(0.211)
(0.217)
(0.211)
Law & Economics
-0.041
0.288*
-0.511***
(0.151)
(0.156)
(0.152)
Constant
0.818
1.384**
0.688
(0.601)
(0.619)
(0.636)
Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively. Robust standard errors are in parenthesis.
After estimating the stated preferences for Comfort, Sustainability, and Safety, the
respective intrinsic attitudes’ values (the residuals of the two-limit Tobit regression) are added
to the multinomial logit analysis in order to compute their effects on the levels of sustainable
transport mode choice. Table 4 shows these regression results, where the private transport mode
is used as the reference case.8 With regard to individual characteristics, Age is negatively
associated with the use of sustainable transport modes, especially in the case of carpooling and
public transport services. Older students are indeed recognized to be less likely to use
alternative modes. This is mainly because they are more independent, and regularly use their
own cars, needing to be flexible for potential job opportunities and household responsibilities
(Zhou, 2012). Moreover, this result suggests that even though the importance of Safety,
21
Comfort, and Sustainability increase with age (see Table 3), students prefer travelling via a
comfortable rather than a sustainable mode of transport. This finding is consistent with the
values assigned by students to their stated preferences. Indeed, as shown in Figure 4, the value
given to Comfort is nearly one point higher than that given to Sustainability on the five-point
Likert scale (3.17 and 2.39, on average, respectively).
Table 4– Multinomial regression results with robust standard errors
Shared Mode Public Mode Active Mode
Male
0.419
0.214
-0.153
(0.320)
(0.224)
(0.456)
Age
-0.254***
-0.118***
0.024
(0.066)
(0.034)
(0.033)
Safety
-0.082
0.090
0.059
(0.145)
(0.099)
(0.208)
Comfort
-0.06
-0.710***
-0.468**
(0.128)
(0.099)
(0.212)
Sustainability
0.167
0.493***
0.709***
(0.147)
(0.096)
(0.203)
Traffic Congestion
0.109
-0.100
-0.867
(2.156)
(1.763)
(4.186)
Distance
0.015
0.042***
-0.139*
(0.009)
(0.008)
(0.075)
Public Availability
-0.106
0.584***
0.336*
(0.127)
(0.106)
(0.199)
Private Availability
-1.001***
-2.495***
-1.766***
(0.283)
(0.190)
(0.363)
Pollution
-0.003
0.004***
0.014*
(0.002)
(0.001)
(0.008)
Parking Lots
0.290**
-0.483***
-0.825***
(0.136)
(0.105)
(0.237)
Constant
7.106***
4.510***
-3.268
(1.889)
(1.070)
(3.887)
Observations
827
Pseudo R-squared 0.336
Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Robust standard errors are in parenthesis.
The majority of the variables related to trip characteristics are significant. In particular,
when distance and public transport service availability are considered as determinants of the
choice of transport mode, the use of buses and trains is preferred to private cars or motorcycles.
On the other hand, as the availability of private transport to students increases, the probability
22
of going to university using a sustainable mode of transport decreases, thus confirming the
results of Danaf et al. (2014) and Limanond et al. (2011). Distance, usually defined in the
literature as a major incentive for using private transport (Shannon et al., 2006), is a strong
negative determinant of using an active transport mode; the longer the home–university trip,
the lower the probability is that students choose to travel to campus by bicycle or on foot. Of
the factors, Parking Lots is found to influence all levels of the sustainable transport modes
considered in the sample; when the square metreage of parking increases (decreases), the
probability of preferring to travel to university by car increases (decreases). This is consistent
with the findings of previous studies in the literature (e.g. Erdoğan et al., 2015; Rotaris and
Danielis, 2014; Zhou, 2014; Whalen et al., 2013), where the availability of parking and parking
permits increase the probability of driving to the university. Additionally, the variable related
to the pollution level of a student’s municipalities of origin (Pollution) has a positive impact
on the use of both public (+0.004) and active (+0.014) transport modes. Evaluating the effects
of Pollution on attitudes and mode choice, it is clear that experiencing environmental pollution
affects both attitudes towards sustainability and intrinsic attitudes, which leads to a sustainable
transport mode choice. Indeed, the level of pollution in the municipality of origin has a positive
effect on the public and active mode choices (Table 4) and on the students’ attitudes towards
sustainability (Table 3). These results corroborate the theory that experiencing pollution leads
to eco-friendlier behaviour (e.g. Finger, 1994; Nilsson and Küller, 2000), and the theory that
the environment has a crucial influence on travel mode choice (e.g. Cervero, 2002; Dong et al,
2016; Rodrı́guez and Joo, 2004), documenting the importance of considering the impact of
environmental characteristics (both individual and geographical) in transport mode choice.
Intrinsic attitudes are found to significantly impact students’ transport mode choices. The
only exception is Safety, which does not seem to be relevant in the case of the University of
Bergamo. This result could be explained by the multiple meanings that ‘feeling safe’ could
23
represent. As stated in M.V. Johansson et al. (2006), safety may include both personal and
traffic traits. While the former would increase the use of private transport, especially cars, the
latter would promote the use of public transport, specifically in the case of trams and trains. In
contrast, the attitude towards Comfort is significant and interesting. The effect of comfort on
transport choice is a controversial issue. On the one hand, a common belief is that comfort is
one of the main reasons why students choose to drive their own car, rather than using more
sustainable transport modes (e.g. Kaplan, 2015). On the other hand, public transport provides
travellers with an opportunity to rest, work, or even move around (M.V. Johansson et al., 2006),
making it the more comfortable than driving. In addition, there is no consensus in the literature
on how comfort impacts the mode choice between a car and a bus service. Using cycling as the
base case, Whalen et al. (2013) shows how comfort is decisive in the choice of both a car and
a bus service. The results of the present study confirm the hypothesis of Kaplan (2015),
showing that when the propensity towards Comfort increases, people tend to prefer private
means of transport. In contrast, the attitude towards Sustainability works as a positive incentive
to use buses or trains or to travel to the university using an active mode of transport. This result
improves on the findings of M.V. Johansson et al. (2006), who limit the effects of the attitude
towards sustainability to a choice between levels of sustainable transport modes, without
considering the impact of solo driving. Consistent with the theory of planned behaviour (e.g.
Bamberg and Schmidt, 1998), this finding shows that students’ use of sustainable modes of
transport is a direct outcome of their intentions (Klöckner and Friedrichsmeier, 2011).
Next, the study considers each university campus separately, applying the same analyses
(Table 5). Although some results are homogeneous across the three districts, important
differences become evident. With regard to the industrial district outside the city centre,
Pollution and Private Availability are no longer significant factors influencing the choice of
private transport mode compared to that of the first level of sustainable transport modes.
24
Furthermore, the results suggest that males use public transport more often than females do.
This result is of great interest to scholars investigating whether gender affects transport mode
choice, especially in the case of sustainable modes. Polk (2003) shows that females are more
sensitive to environmental issues and, thus, are more prone to reducing their use of cars in
favour of sustainable transport modes. This result corroborates the findings of Akar et al.
(2013), Danaf (2014), E.M. Delmelle and Delmelle (2012), and Zhou (2014; 2016), who show
that, ceteris paribus, women tend to consider the alternatives to driving solo as less valid.
Furthermore, Traffic Congestion is significant and positive in terms of students’ choices
between public and private modes of transport. This result is influenced by the availability of
trains and trams, which allow students coming from the suburbs to reach the university in the
industrial district by crossing the city of Bergamo, without being subjected to the typical
congestion characterizing the area. In the historical hamlet, Sustainability assumes greater
importance, having a positive influence on all levels of sustainable transport. In addition, within
this framework, the more congested the journey, the lower the probability is of choosing to use
carpooling. This result is reasonable, because carpooling generally increases the time and,
sometimes, the distance travelled, because the driver has to collect other students. For students
attending this campus, Safety influences their choice of transport mode, and is significant when
choosing between private cars and carpooling. Specifically, as the humanities students’
intrinsic attitudes towards Safety increase, the probability of choosing to drive solo is higher.
Accordingly, the topic of ‘trust’ when dealing with carpooling is crucial (e.g. Furuhata et al.,
2013), because people tend not to trust individuals other than family members (e.g. Mayer,
Davis, and Schoorman, 1995). Trust certainly affects students’ perceptions of feeling safe when
choosing carpooling as a transport mode.
Unlike in the other areas, students attending lectures in the city centre register a positive
influence of Traffic Congestion on the use of active transport modes. The more congested the
25
traffic, the higher the probability is that a student attending the university in this district will
choose to avoid the traffic and opt to cycle or walk. Along with the impact of congestion on
the attitude towards sustainability (Table 3), this outcome corroborates the findings of Lavery
et al. (2013), who report that people who consider limiting their travel by car in order to
improve the quality of the environment are more likely to use sustainable transport modes.
Table 5 – Multinomial regression results by university campus location
Industrial District
City Centre
Historical Hamlet
Shared
Mode
Public
Mode
Shared
Mode
Public
Mode
Active
Mode
Shared
Mode
Public
Mode
Active
Mode
Male
0.585
1.417*
0.202
0.015
1.008
0.852
0.167
-1.068
(0.553)
(0.828)
(0.556)
(0.461)
(0.755)
(0.641)
(0.324)
(0.760)
Age
-0.341**
-0.147
-0.280**
-0.532***
-0.129
-0.225
-0.084***
0.037
(0.150)
(0.254)
(0.118)
(0.125)
(0.155)
(0.159)
(0.027)
(0.039)
Safety (res.)
0.067
-0.199
0.207
0.002
0.167
-0.563*
0.149
0.046
(0.268)
(0.463)
(0.229)
(0.224)
(0.431)
(0.308)
(0.156)
(0.312)
Comfort (res.)
-0.328
-1.208***
0.135
-0.961***
-0.886
-0.087
-0.744***
-0.453
(0.239)
(0.309)
(0.185)
(0.213)
(0.754)
(0.238)
(0.157)
(0.311)
Sustainability
(res)
-0.188
0.995***
-0.098
0.834***
0.335
0.931***
0.562***
0.980***
(0.318)
(0.296)
(0.252)
(0.217)
(0.633)
(0.341)
(0.153)
(0.289)
Traffic
Congestion
4.199
11.099***
-4.325
2.975
14.316**
-13.909***
-2.278
-6.441
(4.184)
(4.144)
(5.090)
(3.828)
(6.490)
(5.085)
(1.890)
(6.157)
Distance
0.074**
0.093**
0.012
0.088***
-0.638*
-0.010
0.024***
-0.101
(0.031)
(0.045)
(0.023)
(0.021)
(0.343)
(0.016)
(0.007)
(0.062)
Public
Availability
0.262
1.964*
-0.025
0.688***
0.324
-0.212
0.351**
0.123
(0.301)
(1.016)
(0.211)
(0.203)
(0.454)
(0.295)
(0.140)
(0.254)
Private
Availability
-0.917
-2.714***
-1.000**
-2.852***
-1.544**
-1.832***
-2.514***
-1.809***
(0.560) (0.545) (0.456) (0.480) (0.730) (0.560) (0.266) (0.481)
Pollution
-0.005
0.002
-0.003
0.005*
0.076
0.001
0.007***
0.012
(0.005)
(0.005)
(0.004)
(0.003)
(0.062)
(0.004)
(0.002)
(0.009)
Constant
7.549*
-4.661
8.765**
11.617***
-28.919
8.720*
4.511***
-1.447
(3.856)
(7.372)
(3.593)
(2.938)
(28.130)
(5.243)
(1.250)
(3.787)
Observations
117
250
460
Pseudo R-
squared 0.427 0.463 0.360
Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are in parenthesis.
5.1. Policy implications
To understand the policy implications of the results of this study, it is important to emphasize
those variables that influence university students and their travel mode choices. Given the
strong impact of distance and parking lots as determinants of private transport, policymakers
use these findings to enhance sustainable transport. Indeed, because a scarcity of parking spaces
available to students would lead to a greater use of sustainable transport modes, dedicating one
26
squared kilometre for residential use would decrease the probability that students drive alone
to the university by 4.5%, and increase the number in favour of public (+6.0%) and active
(+1.5%) modes of transport.
Additionally, public availability and the ecological importance of the means of transport
positively influence the sustainable transport mode choices of buses and trains with respect to
cars at all three university locations. Because public transport availability plays an important
role in determining the use of sustainable transport, providing a better public transport service
would reduce the use of cars by 5.5%, increasing the probability of using busses or trains by
7.6%. Furthermore, based on the importance of the Sustainability attitude, informing students
about environmental issues would help to increase the proportion of all sustainable transport
modes (+5.1% and +1.1% for public and active mode, respectively), thus decreasing private
transport usage by 5.8%. Addressing specific sustainable programmes on this topic would have
an impact in the short term, influencing students’ choices of transport to the university, and in
the long term, because students are recognized as ‘future transport decision makers’ (Kim et
al., 2016). Interestingly, the results also suggest that improving the public transport service and
promoting sustainable transport mobility have different impacts when considering the campus
areas separately. In the case of faculties located in the city centre and in the historical hamlet,
an improved public transport service would lead to a reduction in private mobility of 3.3% and
5.3% respectively. In the case of the industrial district, this decrease is 9.5%. These outcomes
should not be neglected by policymakers, who should consider increasing the bus service
networks towards areas located further from the city centre.
6. Conclusion
27
Our work sheds light on the factors affecting the choice of sustainable transport modes of
students attending a growing Italian university, namely, the University of Bergamo. Because
investigating people’s attitudes is considered crucial in transport mode choice studies, this
study performs a two-step analysis in order to develop policy suggestions that address the
promotion of sustainable transport modes (e.g. Bopp et al., 2011; M.V. Johansson et al., 2006;
Schwanen and Mokhtarian, 2005). First, three transport features that may influence students’
mobility (Safety, Comfort, and Sustainability) are identified and analysed to understand which
individual and local characteristics impact students’ stated preferences. Second, the
unexplained values of Safety, Comfort, and Sustainability are used to estimate the probability
of choosing a private or a sustainable (shared, public, or active) transport mode to travel to
university. This two-step analysis allows the authors to disentangle the components of students’
stated preferences into 1) attitudes strictly related to students’ individual characteristics and
experiences, and 2) ‘intrinsic attitudes’.
The results show that, in terms of stated preferences, students are more interested in the
safety and the ecology of the transport mode if they experience a more polluted environment.
Interestingly, differences emerge across attended faculties; engineering and law & economics
students consider Sustainability (Comfort) to be less (more) important than humanities students
do. The results of the coefficients of students’ intrinsic attitudes in the second step of the
analysis show that in a growing university, with campuses located in different areas, the higher
the propensity towards comfort, the lower the probability is of choosing a sustainable transport
mode. In contrast, the more students there are who care about the sustainability of a means of
transport, the more they travel by carpooling, buses, trains, or active transport. The main factors
that increase students’ choices of private modes are distance and parking lot availability.
Oppositely, both public availability and the ecological importance of the means of transport
28
have a positive effect on the sustainable transport mode choices of buses and trains, with respect
to cars, at all three university locations.
This study shows that informing students about environmental issues helps to increase the
proportion of all sustainable transport modes, driving a decrease in private transport usage.
Additionally, improving the public transport service may have a significant impact, especially
when considering the campus areas individually, up to a decrease in private transport choice of
9.5%.
This study does not come without limitations that offer avenues for future research.
Although the structure of the transport infrastructure around the University of Bergamo has not
radically changed during the period under investigation, implementing a longitudinal set of
surveys might provide similarities and differences with our results, while exploring the
changing behaviour of students over time. Furthermore, along with the pressure on local
policymakers with regard to cost-effective policies, and on university managers who need to
attract students from different locations (and other countries) at the highest level of
accessibility, it would be intriguing to examine the variation in students’ transport mode
choices when simulating a different scenario, that of aggregating all the university campuses
in a single location in the city centre.
29
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Authors’ Bio
Mattia Cattaneo is Assistant Professor at the Department of Management, Information and
Production of the University of Bergamo. Member of the Higher Education Research group
(HERe) and scientific coordinator of the ICCSAI (International Center for Competitiveness
Studies in the Aviation Industry). His research interests include Higher Education, Regional
Development and Transport Economics. He is author of articles in journals such as Research
Policy, Studies in Higher Education, Higher Education, Regional Studies, Transportation
Research Part A: Policy and Practice, Transport Policy, Tourism Management and Stata
Journal. He is often reviewer for several journals in the field of economics and higher
education.
Paolo Malighetti is Associate Professor at the University of Bergamo, Department of
Management, Information and Production. He taught Business economics and Transport
management at University of Bergamo. His main research interests relate to transport
management with a specific focus on network evolution and pricing strategy. Recently his
research interested widen to management issues in healthcare, focusing on the aspects related
to the adoption of new technology within clinical practice. Since 2013 he is director of the HTH
center (Human factors and technology in healthcare) at University of Bergamo.
Chiara Morlotti is a PhD student in Economics and Management of Technology (DREAMT)
at the Department of Management, Information and Production of the University of Bergamo
and the Department of Economics and Management of the University of Pavia. She is
researcher collaborator of the ICCSAI (International Center for Competitiveness Studies in the
Aviation Industry). Her research interests include transportation economics, airlines’ dynamic
pricing and the territorial impact of airports.
Stefano Paleari is Full Professor at the University of Bergamo, Chairman of the Coordination
Committee of Human Technopole, Special Commissioner of Alitalia, and former President of
the Italian Council of the Rectors of Italian Universities. He has held several positions related
to transportation, both in public and private entities: he has been scientific director of the
ICCSAI (International Center for Competitiveness Studies in the Aviation Industry) and
member of the Airneth Scientific Board. Currently, he is the scientific director of the Higher
Education Research group (HERe) affiliated to the University of Bergamo and the CRUI
Foundation.
37
1 Further information is available at https://ec.europa.eu/transport/themes/urban/urban_mobility/ump_en.
2 Data are gathered from the Ministero dell’Istruzione, dell’Università e della Ricerca (http://statistica.miur.it).
3 The survey question on transport mode choice: ‘Which means of transport do you use to go to university?’.
4 According to Smith (1979), a sample size () is considered reasonable when the number of respondents is
higher than =2∙ 2/ 2, where is the normal variate, equal to 1.645 for a confidence interval of 90%; is
the accuracy level, usually set at 5%; and is the standard deviation. The value of is equal to �∙(1 −p),
where is the estimated transit rate of students in the Bergamo area, which according to the ISTAT (Istituto
Nazionale di Statistica) commuting data set of 2011 is around 0.50. Given that is 271, the study sample size is
sufficiently large to estimate the transport mode choices of students at the University of Bergamo.
5 To evaluate whether the multinomial logit regression can be used, the independence of irrelevant alternatives
(IIA) condition is tested, which has to be met when using this model (e.g. Greene, 2012). The results of the
Hausman–McFadden test show that the null hypothesis H0, which states that the odds (alternative/outcome j vs
alternative/outcome k) are independent of other alternatives, cannot be rejected and, therefore, the condition is
satisfied.
6 The same approach is used as that in Pollock, Chen, Jackson, & Hambrick et al. (2010).
7 Although cost is recognized as one of the main variables influencing people’s transport mode choice, it is
omitted from this analysis. There are two reasons for this. First, cost would be strictly correlated with distance,
especially for private and shared transport alternatives. Secondly, public transport for students of the University
of Bergamo has a fixed price of 200€ per year. Given the lack of information about trip frequency, this makes it
difficult to uniformly compute a unitary cost.
8 Hereafter, all comments should be interpreted in relation to the reference case, that is, the private transport
mode.
