1. Introduction
In a sustainable urban environment, the anthroposphere [1] is created by the integrating of land use, urban structures, and travel behavior. Considering mobility, public transport is favored over private transport, and the focus is on reducing car use for daily trips. This requires effective tools to promote the use of sustainable transport modes, as well as training activities for the entire community.
In recent decades, significant efforts have been made in transport and environmental sciences to reduce the environmental impact of commuting. This is closely related to the implementation of strategies to reduce dependence on the private car on the one hand and to increase the use of active modes of transport on the other. In addition to walking and bicycling, public passenger transport (PPT) also fits the definition of an active mode, as it usually involves walking or bicycling at both ends of the trip [2].
Due to their pro-active educational milieu, universities are privileged places to communicate sustainability and contribute to the transformation of societal transport, e.g., [3,4,5]. Consequently, the investigation of alternative modes of transport has become an important item in the sustainability plans of universities around the world, [2,6,7,8,9,10,11,12,13]. Shifting the campus mode of transport from cars to bicycles, walking, and PPT can provide numerous environmental as well as economic and social benefits. In addition to reducing the impacts on ambient air and the anthroposphere, the personal health, recruitment, and retention of students and staff, the public image of the university, and personal attitudes toward sustainable transport can be improved, which in turn can be transferred to the surrounding communities [1,3,14,15,16,17,18,19]. The changes described are not simple, as they require the removal of numerous attitudinal and physical barriers to the adoption of sustainable transport at universities, depending on historical tradition, lifestyle, environmental awareness, economic situation, location, size, accessibility, characteristics of transport infrastructure and facilities, and PPT services [20,21,22,23,24,25,26].
Specific transport demand management (TDM) policies should be developed to address commuter needs and economic and environmental challenges. In this context, the individual policies to stimulate a modal shift from private cars were studied. Initially, they mainly focused on parking management and PPT regulation and subsidy [2,14,27,28,29,30,31]. However, it should be emphasized that a mix of measures including a combination of limited parking spaces and permit requirements, increased parking fees, improved interchanges for pedestrians and bicycles, new secure bicycling storage, a subsidized bicycle purchase scheme, a car sharing scheme, and free university buses or reduced fare tickets proved to be much more effective [2,30,32,33,34]. The aforementioned policy concept should be complemented by analyses of the commuting behavior of university students and staff, because only by taking into account segment-specific attitudes and preferences it is possible to develop effective policies [32,35,36,37,38,39,40,41,42]. Moreover, the efficiency, acceptability, and enforceability of policy measures should not be ignored [32]. They are specific to each university environment.
The University of Maribor (UM) places the principles of social responsibility and sustainable development at the forefront of its activities. It is one of the main traffic generators in the city of 110,000 inhabitants, where the share of students is about 10%. Nevertheless, the development of sustainable mobility at UM has so far depended only on the transport policy of the Municipality of Maribor (MOM). Based on the results of a MOM household survey in 2016, it was found that the car is the predominant mode of transport in the city, and as much as 12% of all car trips are shorter than 1 km [43]. This led us to investigate the situation of the commuting behavior at UM, and the transport policies implemented at the study site from 2010 to 2020, to find out how they affect the transport mode choice and to understand the existing weaknesses, strengths, and opportunities for university mobility. The first aim of our investigation was to determine the shifts in transport modes among the university’s students and staff over a decade period through two questionnaire surveys and to elucidate their origins in relation to the implemented transport policies. Particular attention was paid to measures to reduce the use of the private car and to the share of alternative modes of transport as a function of distance. The second aim was to identify the reasons, barriers, and interventions essential to the development of mobility behavior at UM, according to status (student or staff) and gender. The final aim was to set up the new perspectives of sustainable commuting at UM and to propose actions for the TDM strategy. To the best of our knowledge, the present study is the first detailed report on travel behavior and mobility decisions at UM.
2. Materials and Methods
2.1. Study Site
Maribor is the second largest city in Slovenia and is located in the north-east region. The city has a moderate continental climate with strong sub-Pannonian characteristics [44]. On average, there are 266 sunny days per year. The relief in the urban area is flat, and from the city center to the outskirts it is not more than 5 km, which, together with the favorable climatic conditions, allows residents and visitors to walk or bike most of the year. However, car traffic has no real competition with bicycle traffic [45]. In the field of cycling, Maribor is a city where development is still in its infancy. Although the bicycle network has the length of approximately 86 km, its quality is poor. The infrastructure is subordinate to the infrastructure for motorized traffic. The bicycle network consists mainly of one-way bicycle paths on sidewalks and occasionally (on less busy roads) on the roadway together with motor vehicle traffic [46]. The paths are fragmented, disconnected, and full of danger spots. The offer of PPT in the city is modest (low frequencies, outdated fleet, and short operating hours) and user-unfriendly [45]. Conditions for pedestrians have improved in the city center, which is not the case in other areas.
UM has 17 faculties, 10 of which are located in the city of Maribor. Our study focused on the building complex of the four technical faculties (BCTF), combining the Faculty of Electrical Engineering and Computer Science, the Faculty of Mechanical Engineering, the Faculty of Civil Engineering, Transportation Engineering and Architecture, and the Faculty of Chemistry and Chemical Engineering, because the building complex is located in the center of the city (Figure 1).
In the academic year 2020/2021, 9261 students (excluding graduates) were enrolled at the faculties in the city of Maribor, while the number of staff was 1706. At BCTF, 3840 students (excluding graduates) were enrolled, while the number of employees was 688. The number of students and staff at the BCTF corresponds to 42% and 40% of the faculties in Maribor, respectively.
Service activities and housing are concentrated near the BCTF. Students live in dormitories within a 2 km radius (Figure 1), and in private accommodations scattered throughout the city, but mostly close to the BCTF (Table 1). Seven city bus lines operate near the BCTF, running from 5.00 am to 10.30 pm. Eight bus stops are up to 250 m away. The main railway and bus stations are 2 km away. The monthly pass for the city PPT costs EUR 18 for students and EUR 30 for staff. Annual transferable tickets for EUR 280 are also available for everybody.
The BCTF provides 493 parking places for its staff, located in five parking lots adjacent to the building complex. The monthly parking pass is inexpensive (EUR 18–20). Students are not allowed to park in these lots and must compete with city residents for available public parking. Parking on the street costs EUR 0.5 to 1.5 per hour and in the parking garage EUR 1–2. There are still possibilities to park for free in the parking lots of the nearby shopping centers (open from 9.00 am) and in some nonlegal parking lots. The BCTF does not provide special parking places for carpooling or car-sharing. The two closest car-sharing places are 400 m away. The bicycle parking facilities are far too small relative to the needs. They are uncovered and unattended, with the exception of one bicycle shed that has space for 30 bicycles. The BCTF is connected to the city’s bicycle network. In the 2020/2021 academic year, bike-sharing was not regulated, neither at UM nor in the city.
2.2. Questionnaire
The study on commuting behavior at UM is based on data from two questionnaire surveys conducted ten years apart. The first questionnaire is from 2010 and the second is from 2020. In both cases, the questionnaires were designed at the BCTF, Department of Traffic and Transportation, and a random sample of BCTF undergraduates, graduate students, and staff was surveyed. Participation in the surveys was voluntary and anonymous.
The first questionnaire survey from September 2010 was brief (Supplementary Materials S1). Nevertheless, it provided valuable data for comparative analyses of commuting by university students and staff. It was conducted as part of the European researchers’ night project. Student volunteers from the Transportation engineering study program distributed the questionnaire to students and staff outside all entrances to the BCTF. The content of the questions referred to general information on the commute: transport mode, travel time to the BCTF (door-to-door), and direction of the trip. Participants could choose from five transport modes: walking, biking, using PPT, driving alone in a car, and carpooling.
The characteristics of the study population in 2010 are presented in Table 1. A total of 1153 individuals participated in the survey. In total, 1057 responses were considered valid, of which 84% were from students and 16% from staff. Information on gender was not collected.
The second questionnaire survey, concluded in April 2020, was much more comprehensive (Supplementary Materials S2). It was conducted online using the 1 KA web application. The survey pre-test was conducted with 35 students and staff to verify that all questions were clear, response options were complete, and the length of the questionnaire was acceptable. Feedback was incorporated into the final version of the questionnaire, which was emailed to the BCTF students and staff. The survey had to end early (3rd week of March 2020) because the COVID-19 lockdown was declared in Slovenia on March 15. Therefore, the survey was online for only one month, but the pandemic did not affect the results. Moreover, it was not the trips made on a given day that were the subject of the survey but the description of long-term commuting habits.
The second questionnaire survey was divided into six sections. The first section identified the status and gender of the respondents. The second section asked about the origin and distance of the daily commute. The following sections contained stated preferences, some of which were multiple-choice questions. The third section, relating to private car use, was the most extensive. It asked about frequency of use, travel time to the BCTF (door-to-door), type of use (i.e., alone or shared), location of parking, time required to find a parking place, reasons for car use, and barriers and interventions to shared car use. The last three sections related to active commuting. Frequency of use, travel time to the BCTF (door-to-door), and preferred interventions for more frequent use of PPT, bicycling, and walking were identified.
The characteristics of the study population in 2020 are shown in Table 1. A total of 551 individuals participated in the survey. Up to 462 responses were considered valid. In addition to females and males, the unspecified gender could not be included in standard statistical procedures due to its statistically unrepresentative sample size.
2.3. City Transport Policy
Since neither the University of Maribor nor the BCTF have a sustainable mobility plan or a comparable transport policy document, we assume that noticeable transport modal shifts among the BCTF students and staff have so far mainly depended on the MOM and national transport policy measures implemented in the period between the two questionnaire surveys. Consequently, we selected the most important projects and measures in the study site, which can be found on the website of the MOM [47]. The obtained data were summarized in the following list and later compared with the survey results to evaluate their efficiency, to analyse the opportunities of UM mobility, and to elaborate guidelines for the TDM strategy at BCTF.
The general transport measures implemented in the MOM during the 2010–2020 period are:
Adoption of the integrated transport strategy of the city of Maribor.
The continuous extension of the 30 km/h speed zone for motorized traffic in the central areas of the city.
Establishment of a mobility center (co-financed by MOM).
Gradual introduction of a car-sharing system since 2017.
Gradual installation of charging stations for e-cars.
Other measures were more targeted. In terms of parking management, the following measures stand out:
Continuous reorganization of parking places while gradually reducing their number and increasing the number of paid parking places in the city center.
Gradual increase in parking fees (2010: EUR 0.5–0.8; 2020: EUR 0.8–1.5).
Some important changes were made in the field of PPT:
Introduction of the integrated PPT ticketing system at the national level in 2013.
Partial adaptation of city bus lines (e.g., changing routes, introducing new stops, and increasing frequencies).
Subsidizing PPT tickets for seniors, students, and pupils.
Renovation of the 160 city bus stops in the period 2016–2021 (e.g., bus shelters, equipment, timetables, and information system).
Introduction of demand responsive PPT in the city center in 2017 (e.g., minibus Maister).
Introduction of RTPI (real-time passenger information system) at the most frequented bus stops.
Key measures to promote walking and bicycling in the 2010–2020 period include the following:
Extension of the pedestrian zone in the city center in the years 2011 and 2019.
Continuous extension of the city’s network of bicycle paths (for approx. 20 km).
Introduction of tactile markings on pedestrian surfaces in the city center.
Introduction of traffic lights with acoustic warning signals.
Introduction of regional bicycle tourism routes.
Definition of bicycle corridors in the city.
Construction of a secure bicycle parking facility (Kolesodvor) at the train station in 2014.
Increase in the number/quality of bicycle racks in the city center.
Ongoing adaptations of pedestrian and bicyclist areas and intersections as part of road reconstruction or regular road maintenance.
2.4. Data Processing
Data on modal splits in the study site zones (Figure 1) were obtained by students and staff for both surveys and by gender for the second survey only. The survey on stated preferences in the second questionnaire provided data on reasons for private car use and measures for the increased use of shared cars and active commuting by student, staff, and gender. Descriptive analysis was performed using Microsoft Excel software, while the statistical processing of data was performed using IBM SPSS Statistics 22 (IBM Corporation, Armonk, NY, USA). A value of p < 0.05 was considered significant. Given that this investigation focused on the mode choice of the study population, a formula by Smith [48] was used to estimate the appropriate sample size, as in the study by Zhou [49]:
(1)
where S is standard deviation, d is absolute accuracy level expressed as a percentage, and Z is normal variate, which depends on the confidence limit. In this case, the 95% confidence limit was chosen, resulting in a value of 1.96 for Z, whereas a standard percentage, i.e., 5%, was used for d. The standard deviation was estimated according to the following formula:(2)
where p is the percentage of transit. Based on the archived data [50], the p value was estimated to be approximately 0.2. From Equations (1) and (2), it was calculated that n is equal to 246. Therefore, the sample sizes 1057 and 462 for the first and second questionnaire surveys, respectively, were large enough to investigate the mode choice of the studied population at BCTF.Participants provided information on the location from which they commute, and these data were spatially distributed using QGIS 3.22.9 (QGIS Development Team; URL:
Based on the collected data (Section 2.2), the study population of the first survey could only be aggregated by two zones with a radius of 0–2 and >2 km from the BCTF (Table 1a). The majority of students, 57%, commuted daily from zones 1 and 2, while 43% commuted from zones 3 and 4 (Figure 1), which compares well with the results of the second survey. The same is true for the staff. The majority, 56%, commuted from zones 3 and 4, while the rest, 44%, commuted from zones 1 and 2. Given these facts, it was assumed that only the detailed data on commuting distances from the second survey could be used to interpret the results presented in the following subsections.
To test the statistical representativeness of the input data on sociodemographic characteristics (gender, status, and place of residence), and to test associations between them, we created contingency tables to display the frequency distribution of the variables. The differences in the count and expected count for each of the variables indicate whether they are associated or not. The significances of the associations were calculated using the chi-square test, with the significance level set at p < 0.05. All estimated parameters were statistically significant and were consistent with evidence reported in the literature (Supplementary Materials S3). The data on stated preferences were not part of the statistical analysis because they served as an additional tool to create an efficient proposal for the interventions of the TDM strategy at the BCTF, along with the MOM transport policy measures that had the greatest impact on the observed modal shifts, and as a basis that would be consistent with the expectations and needs of the BCTF students and staff.
3. Results and Discussion
3.1. Transport Mode Choices
In 2010, the majority of students chose walking for commuting to the BCTF, 46%, while 38% used cars, both their own (30%) and shared (8%; Figure 2). The choice of other active modes of transport was lower: 8% travelled by bicycle and 7% by PPT. For staff, the modal split and origin of commute trips differed from those of students (Figure 2, Table 1). The predominant transport mode choice was car, used by 64%, of which 56% used their own car and 8% used a shared car. When choosing active modes of transport, 24% opted for waking (the second most common modality), 7% for bicycling, and 5% for PPT. Thus, staff used cars practically once more and walked half as often as students, while the use of other modes (bicycling and PPT) was similar in both groups. The large discrepancy in the proportion of car users between students and staff is likely the result of the availability of cheap employer parking for staff (Section 2.1).
The most significant change in student commuting in 2020 compared with 2010 was the 22% decrease in car use (own and shared; Figure 2). The change was reflected in a 9% increase in walking and a 13% increase in the use of PPT. The observed modal shift could be the direct result of the reduction in the number of free public parking places in the city center, the increase in parking fees, and the extension of the pedestrian zone in conjunction with the introduction of integrated PPT tickets at the national level (Section 2.3). The proportion of active commuting and car use (own and shared) by staff in 2020 was similar to that in 2010, but the relationships between the modes of the two groups were different—the use of bicycles, PPT, and shared cars increased (Figure 2).
The comparative results of the two surveys were very encouraging, as the distribution of trip origins by subzones of the study site had remained practically unchanged over ten years (Section 2.2, Table 1). This led to a detailed study of the modal split by four zones (Figure 3 and Figure 4).
Looking at the results for the combination of zones 1 and 2 (Figure 3a), we find that students within 2 km of the BCTF preferred to walk during the investigated period (74–77%), while bicycling was not as popular (12–11%). The low proportion of bicycle use related to the short distances and a scant bicycling tradition, which was repeatedly confirmed in conversations with students in our lectures. However, the proportion of car use on these short distances was 14% (own and shared) in 2010 and has decreased to 5% by 2020. This change was reflected in a 4% increase in walking and a 6% increase in the use of PTT. Walking was also the preferred transport mode for staff during the investigated period (46–39%), but they used it 1.8 times less frequently than students. For distances up to 2 km, which can be easily covered on foot or by bicycle, they used cars (own and shared) by 40–27%, which, as mentioned in the first paragraph, can be attributed to the employer’s parking policy. Over a ten-year period, the largest shift in staff commuting has involved bicycling and car use. The former has increased by 17%, and the latter has decreased by 13%.
The more detailed transport mode structure of students and staff at the BCTF in 2020 in zones 1 and 2 can be discussed with the help of Figure 4. It is noteworthy that still up to 3% of students used a car within 1 km, that the proportion of walking and bicycling decreased with distance by 19% and 3%, respectively, and that the use of the city PPT became interesting among students at distances between 1 and 2 km (16%). The high percentage of active commuters among students decreased rapidly with increasing distance from the trip origins, which can be explained in part by the fact that the bicycle and pedestrian system was the best in the city center and deteriorated rapidly with increasing distance (Section 2.1 and Section 2.3). Staff also predominantly chose active commuting within 1 km of the BCTF, though 20% of them used cars, which is 1.7 times more than a typical household in Maribor [43]. Within 1–2 km of the BCTF, staff interest in bicycling remained the same, but car use increased to 35% and became the predominant transport mode. This is the direct result of the availability of very cheap parking near the BCTF, which is also true for other subzones of the study site.
Figure 3b illustrates the transport mode structure of students and staff within a radius of more than 2 km of the BCTF. Only 15% of students commuted by PPT in 2010 and as many as 72% by car (own 58% and shared 14%). The results indicate, on the one hand, that the city PPT was unattractive and, on the other hand, that PPT from the suburbs was insufficient. The situation had changed by 2020, when PPT was the predominant transport mode among students (49%), and car use decreased to 41%. The proportion of car use and active transport among staff has not changed over a decade-long period, which is not the case for the relation between the modes of the two groups, i.e., the proportion of the use of shared cars and bicycles has increased by 7% and 5%, respectively.
The details of the commuting structure in zones 3 and 4 of the study sites in 2020 are presented in Figure 4. PPT was the predominant transport mode (49%) for the minority of students accommodated within 2 and 5 km of the BCTF (6%), while 20% of the staff living in this zone (Table 1b) travelled by car (69%; 52% by their own and 16% by a shared car) and 15% by PPT. As distance increases, the MOM’s measures to encourage the use of PPT become noticeable, namely subsidising PPT tickets in conjunction with parking restrictions near the BCTF. The proportion of students commuting to the BCTF from zone 4 and using PPT remained the same, but car use increased to 47% (own car to 34% and shared car to 13%). Cars were the primary transport mode for 89% of staff, 16% of whom shared a car. The use of PPT decreased to only 5% within distances over 5 km of the BCTF, as these commuters could not use the city PPT, but mainly the regional PTT, which operated at a much lower frequency and was therefore less suitable for daily commuting. This was not true for students, as their financial situation was different, which affected their commute modes and places of residence. On the one hand, many of them did not own a car, and on the other hand, they had the opportunity to buy cheap integrated tickets (Section 2.3). The proportion of shared cars has not changed significantly for a decade.
The largest changes in student modal split from 2010 to 2020 occurred in zones 3 and 4 of the study site, especially for distances greater that 5 km to the BCTF, from where 34% of students commuted—car use decreased by 30% (own car by 28%) and the use of PTT increased by 34% (Figure 3b and Figure 4). The results suggest that students have been very receptive to the subsidized regional PPT tickets and considered them a better option than using a car every day or accommodating close to the BCTF.
The largest changes in staff modal split between 2010 to 2020 occurred in zones 1 and 2, which accounted for 44% of trips—as mentioned earlier, car use decreased by 13% and bicycle use increased by 17% (Figure 3a). Only in zones 1 and 2 were they more likely to ride a bicycle than students. However, car use within 1 km of the BCTF increased from 20% to 35% within 1 to 2 km in 2020. Again, the explanation for this is the availability of very cheap parking places provided by the employer. In all four zones of the study site, staff were less likely than students to commute by active transport. They were not interested in using regional PPT for distances greater than 5 km to the BCTF, from where 48% of them (the majority) commuted, while this mode of transport was of interest to as many as 49% of students. At this distance from the BCTF, the percentage of staff and students sharing a car increased to 16% and 13%, respectively.
The findings are consistent with the studies of Delmelle and Delmelle [14] and Hidalgo-Gonzalez et al. [52], who reported that distance from the university is a major determinant of mode choice and that walking is the primary mode of transport for students living within 2.5 km of the campus, while cars become more popular beyond this distance. Our study also indicated that students were much more receptive to price-based interventions. The unavailability of free parking, the increase in parking fees, the subsidization of PPT, and the introduction of the integrated PTT ticket convinced many to use PPT instead of cars for longer distances and to walk or bike for shorter distances. Modal shifts were much smaller for staff. The main reason that the implemented measures did not have the same impact on staff is that most staff could park their cars in reserved lots near the BCTF almost for free (Section 2.1), and they have a different financial status.
Regarding staff gender (Figure 4), differences were negligible for distances up to 2 km to the BCTF, with the exception that female staff within 1–2 km were more likely to use the city PTT instead of walking. The same was true for student genders, except that male students were more likely to use bicycles and cars for distances up to 2 km. These results are consistent with the findings of the national cross-sectional study by Jurak et al. [53], which involved Slovenian schoolchildren aged 12 to 15 years. They revealed a somewhat different picture than most previous studies, which found that active commuting to school was more common among boys than girls. They show that the difference in active commuting between genders is negligible, but more girls than boys walk to school, while more boys than girls use so-called wheel commuting to school (by bicycle, skateboard, roller-skates or kick scooters). The similarity with our results highlights the transfer of habits to university and the importance of raising awareness of sustainable mobility in the early stages of education.
It is interesting to note in Figure 4 that male staff and male students used PPT (the city PPT) more frequently than their mates only for distances between 2 and 5 km to the BCTF, and only in this zone did female students use bicycles more frequently than male students. Male students were more likely to use their own cars than female students in all zones of the study site (3% to 16%), which represented the largest difference according to the two genders. Up to distances of 2 km from the BCTF, the proportion of staff commuting by personal car was similar for both genders. However, at distances of 2 to 5 km, and at distances greater than 5 km, female staff were about 10% more likely to use their own car than male staff. On the other hand, male staff and male students were twice as likely as female staff and female students to share a car for longer trips. The results suggest that female students and female staff are more likely to share a car than their mates only for distances up to 2 km from the BCTF. This might be related with Slovenian tradition, as explained by Jurak et al. [53]. For distances up to 2 km from the BCTF (which was typical for the mentioned study), female students and female staff were used to share a car with known persons (relatives or friends). For longer distances, the situation is different, especially for distances outside the city (more than 5 km), where a car is often shared with unknown persons. At these distances, male students and male staff felt safer sharing a car.
3.2. Reasons for Car Use
Staff emphasized comfort and time predictability (28%), speed (24%), and combining transport with other activities (15%) as the most important reasons for choosing a car to commute to the BCTF (Table 2), which is closely related to convenient and cheap reserved parking. Reasons for car use did not differ by more than 3% between staff genders, except for combining transport to the BCTF with other activities, which was more important for female staff by 7% (they were likely to have more family errands to run). Students also reported comfort, time predictability, and speed as the main reasons for commuting by car, but in reverse order to staff. In contrast to staff, sharing a car with relatives, classmates, and acquaintances ranked third (15%), and combining the trip to the BCTF with other activities ranked fourth (13%). Reasons for car use did not differ by more than 4% between genders.
In total, 8–9% of students and staff had no other alternatives to cars for commuting to the BCTF (Table 2). The largest difference in reasons for car use between staff and students, 7%, involved a shared car and parking availability—the latter was more important for students who depend on public parking, while the former was less important for staff.
Previous studies have reported that institutional parking policies have a large impact on commuting by own and shared car (Section 1). The high percentage of staff commuting to the BCTF by car can be explained by the fact that 86% of the staff have the parking option in the facility’s lots (Section 2.1, Figure 3, Table 3a). The majority of students parked in free and paid public street parking lots near the BCTF, 85%, while 7 to 10% of them parked in free parking lots in nearby shopping centers. Consequently, 98% of the staff did not take time to find a parking place, or the time was shorter than 5 min. In contrast, 86% of students took up to 10 min to find public parking, and 11% took between 10 and 15 min. The previous study evidenced that finding a parking place accounts for 11% of traffic in the city of Maribor [54]. Nevertheless, 57% of students took less than 5 min.
The main barriers for staff to share cars to get to work (Table 4) were time adjustment (32%) and inappropriate or too short distances (23%). Lack of information about shared cars, coordination of drop-off points, and lack of confidence in the accuracy of arrangements (delays and trip cancellations) hindered them by 12–14%. Time adjustment (20%) and inappropriate distances (28%) were also the main barriers for students to share cars for commuting. As expected, the former was 12% less important for young people than for staff. Only the proportion of barriers to personal safety differed slightly more from that of staff—it was 8% higher for students, suggesting that students were more likely to share a car with unknown people than staff. Most student gender responses differed up to 3%, except two. Inappropriate distances were an 11% more important barrier for female students and a 6% lack of information about shared cars for male students.
A total of 25% of the staff indicated that no action would change their habits of choosing carpooling or car-sharing more often to commute to the BCTF (Table 5), and 21% indicated that assistance in finding carpoolers would be effective. Most responses from two genders differed by up to 3%, except two. Assistance in finding carpoolers and raising awareness of the benefits of sharing a car were more important and less important, respectively, by 5% for female staff. Students highlighted the same two interventions as staff, but reserved parking would be the most important measure to increase the use of shared cars among students (12–26% more than among staff), because only public parking lots are available to students. Responds of the genders differed by only up to 3%.
3.3. Barriers and Interventions to Stimulate Active Commuting to the BCTF
Inappropriate or too long distances and poor infrastructure were the main barriers to bicycling to the BCTF for 29% and 20% of staff, respectively (Table 4). Hygiene reasons (lack of showers and changing rooms) and accident risks (conflicts between bicyclists and pedestrians and between bicyclists and cars) hindered 16% and 12% of staff, respectively. Gender responses differed by only up to 3%.
Students had different views on the barriers to commuting by bicycle than staff (Table 4). They also emphasized inappropriate distances (12% less than staff), but lack of bicycle ownership, dislike of bicycling, and risk of bicycle theft were also important barriers for 23%, 18%, and 16% of students, respectively. The reasons are related to their origin of trips. On the one hand, 42% of trips originated from zone 1, where students preferred to walk (Table 1, Figure 3a), which could be interpreted as an inappropriate distance and dislike of bicycling. On the other hand, 34% of trips were from zone 4, which could be related to inappropriate distance, lack of bicycle ownership, and risk of bicycle theft, as safe bicycle storage is rarely available in dormitories and private housing. These responses provided another explanation for why bicycle use among students did not change between 2010 and 2020 (Section 3.1). In terms of genders, 5% more female students than their peers did not own a bicycle, and 4% more female students than their peers did not like to ride a bicycle. This is consistent with previous findings. Consequently, poor infrastructure was a greater barrier for 6% of male students than for their peers.
The vast majority of staff (35%) indicated that no action would cause them to bike more (Table 5). They also indicated that changes in priorities at intersections (bicycling pockets and longer green phases), integration with PPT (bicycle parking lots and rentals), and subsidies for e-bike purchases would be effective interventions for 17%, 14%, and 12% of staff, respectively. In total, 6% more female than male staff reported that no action would increase their bicycling, and 9% more male staff indicated that subsidies for the purchase of e-bikes could increase their interest in bicycling. Students emphasized the same interventions to increase bicycling as staff but in different proportions. In addition, 18% of students (10% more than staff) pointed to bike sharing. Gender responses differed by only up to 3%.
Inappropriate distances were the main barrier to walking to the BCTF for 60% of the staff (Table 4), which can be explained by the fact that 69% of the staff commuted from zones 3 and 4 (Table 1). A total of 17% of the staff preferred other transport modes. Gender responses differed by only up to 2%. With 29% fewer students than staff commuting from zones 3 and 4 (Table 1), inappropriate distances were the main barrier to walking to the BCTF for 48% of students. On the other hand, 13% more students than staff preferred other transport modes, which can be explained by the decrease in walking in zone 2, from which 18% of students commuted (Table 1, Figure 3a). Gender responses differed by no more than 5%. Inappropriate distances were more important for female students and accident risks for male students. This demonstrates that female students shifted walking and PPT (Section 3.1).
As many as 52% of the staff believed that no intervention could persuade them to walk more (Table 5), which is related to the origin of their trips (Section 3.1). In total, 19% of them would be encouraged by greener pedestrian infrastructure and 15% by safer pedestrian infrastructure (e.g., crosswalks, wider pedestrian zones, and sidewalks). A total of 7% more female than male staff felt that no intervention could encourage them to walk more. The same interventions to encourage walking were also important to students. However, 18% fewer students felt that no intervention could persuade them to walk. Like female staff, 8% more female students than their peers felt that no measures could persuade them to walk more.
Lack of PPT connections and low frequency of trips were the main barriers to commuting to the BCTF by PPT for 26% and 25% of staff, respectively (Table 4), which was reflected in the very low use of the city PPT from zone 3, and especially the regional PPT from zone 4 (Section 3.1). Too distant bus stations, a dislike of PPT as a transport mode, and too short distances were barriers that hindered 13–19% of staff. Gender responses did not differ by more than 5%. Student responses differed slightly from staff. Since the majority of students commuted from zones 1 and 2, too short distances were emphasized by 37% of students (24% more than staff). Too distant bus stations and a dislike of PPT hindered a similar proportion of students and staff, while the lack of PPT connections and frequency of trips were less important to 11–13% of students. The low frequency of trips and a dislike of PPT were more important barriers for 6% of male students and too short distances for 9% of female students.
Faster travel (e.g., fewer stops and reserved yellow lanes) was the intervention that would encourage the most staff (24%) to use PPT more often (Table 5). Better accessibility and synchronization of PPT (e.g., transfer points and schedule) would persuade 16–17% of staff, and better ticketing (integrated tickets and subsidies) and fewer delays 11–13% of staff. Students had similar views on interventions to increase the use of PPT. Their responses did not differ by more than 6%, with fewer delays being more important to students. The gender responses of staff and students did not differ by more than 3%.
3.4. Guidelines for the TDM Strategy at BCTF
The differences in travel behavior between the BCTF students and staff during 2010–2020 were merely the result of transport policies implemented at the municipality level (Section 3.1). It is pointed out, on the one hand, that students are much more receptive to monetary-based policies and, on the other hand, that it is extremely difficult to achieve significant modal shifts among staff until they can use cheap reserved parking places near the BCTF. The results confirm the previous findings [2,30,32,34,41] that a combination of subsidizing bus use, unavailability of free parking, and monetary parking fees are effective modal shift measures, and that students are willing to use more sustainable modes of transport if they live close to the faculty. Although the differences in travel behavior between genders were less significant, some of them should not be ignored in planning.
Since we analyzed the data by distance, we cannot only find the most appropriate measures for specific user groups, but also determine where they can best be implemented (geographically). The detailed analysis of responses to the questionnaires on the barriers to the use of the chosen transport mode and on interventions to increase the use of this mode also give us a powerful tool to find the most effective measures to design TDM. It is also worth highlighting that 8–9% of students and staff had no alternatives other than using a car to get to the BCTF.
In addition to our research, we also considered the latest findings on the key elements of sustainable urban mobility plans in Slovenian towns [55] and the impact of changes in PPT tariffs on travel behavior [56].
Table 6 summarizes the proposed interventions for the TDM strategy at the BCTF that focus on reducing car use for daily trips. The interventions should be evaluated based on the difficulty of implementation and potential outcomes, and the proposal should be linked to city-level actions, as the BCTF and other UM faculties are distributed throughout the city and share infrastructure and services with other users.
4. Conclusions
The results show some significant differences between student and staff travel behavior and less significant differences between genders. Modal shifts were related to trip origins, subsidization of bus use, availability or unavailability of free parking, reduction in the number of free parking places in the city center, increase in parking fees, extension of the pedestrian zone, and introduction of integrated PPT tickets. Since 2010, active commuting has predominated among students, while car use has decreased by 22%. Active commuting and car use by staff have not changed since 2010. However, 12% of students within 2 km of the BCTF used a car, and as many as 20% and 35% of the staff within 1 km and 1 to 2 km of the BCTF, respectively, where it is convenient to walk or bike. A total of 49%, and 47% of students, commuted from distances greater than 5 km by regional PTT and by car, respectively, while 89% of the staff mostly used cars. These results represent the greatest challenge for improving sustainable mobility at the BCTF. The responses to the questions on stated preferences regarding barriers and interventions for alternative transport use provide us with a powerful tool to find the most effective interventions for TDM design. Responses did not differ significantly between students and staff or between genders, with some exceptions that highlight the need to incorporate these perspectives into sustainable mobility strategies.
The policy proposals of this study could be useful for developing TDM strategies for other UM faculties, while the methodology can be applied to other traffic generators in the city (e.g., elementary and secondary schools, hospitals, courts, shopping centers, banks, etc.), as well as to other universities with similar conditions, especially in post-communist countries in Europe. The results also suggest that transport policies can be more effective if they are planned in coordination with housing policies.
Conceptualization, B.T.; methodology, B.T. and B.M.; validation, B.T. and B.M.; formal analysis, B.M.; investigation, B.T. and B.M.; data curation, B.M.; writing—original draft preparation, B.T.; writing—review and editing, B.T. and B.M.; visualization, B.M.; supervision, B.T. and B.M. All authors have read and agreed to the published version of the manuscript.
The University of Maribor processes and protects personal data of individuals in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data, on the free movement of such data, and repealing Directive 95/46/EC (OJ L 119, 4 May 2016, pp. 1–88; hereinafter: General Data Protection Regulation (or GDPR), the Personal Data Protection Act (Official Gazette of the Republic of Slovenia, No. 94/2007–official consolidated version and 177/20)) and in accordance with other national and internal legal acts.
Both questionnaires (2010 and 2020) were non-interventional studies, in which all participants were fully informed that anonymity was guaranteed, why the research was being conducted, how their data would be used, and whether there were any risks involved. Participation was voluntary.
Data sharing is not applicable to this article.
The authors would like to thank the Department of Transportation Engineering at the Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor for providing archival data. The authors also thank Matej Mencinger for valuable advice on statistical analysis.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Study site with subdivision Zones 1 (0–1 km), 2 (1–2 km), 3 (2–5 km), and 4 (>5 km) around the BCTF.
Enlarge this image.Figure 2. Transport mode structure of students and staff at the BCTF in 2010 and 2020.
Enlarge this image.Figure 3. Transport mode structure of students and staff in subzones of the study site in 2010 and 2020: (a) in zones 1 and 2 and (b) in zones 3 and 4. Additional information can be found in Figure 1 and Table 1.
Enlarge this image.Figure 4. Transport mode structure of students and staff at the BCTF in 2020 grouped by subzones of the study site and gender; further information can be found in Figure 1 and Table 1.
Characteristics of the study population at the BCTF in (a) 2010 (sample size 1057) and (b) 2020 (sample size 462).
| Characteristic | Staff | Students | ||||
|---|---|---|---|---|---|---|
| N | Sample (%) | N | Sample (%) | |||
| Gender | ||||||
| 1 | All | 171 | 16.2 | 886 | 83.8 | |
| Location of living | ||||||
| 1 | Zone 1 (0–2 km from BCTF) | 76 | 44.4 | 508 | 57.3 | |
| 2 | Zone 2 (> 2 km from BCTF) | 95 | 55.6 | 378 | 42.7 | |
| (a) | ||||||
| Gender | ||||||
| 1 | Female | 115 | 50.7 | 145 | 61.7 | |
| 2 | Male | 112 | 49.3 | 90 | 38.3 | |
| Location of living | ||||||
| 1 | Zone 1 (0–1 km from BCTF) | 41 | 18.1 | 99 | 42.1 | |
| 2 | Zone 2 (1–2 km from BCTF) | 29 | 12.8 | 42 | 17.9 | |
| 3 | Zone 3 (2–5 km from BCTF) | 45 | 19.8 | 15 | 6.4 | |
| 4 | Zone 4 (5+ km from BCTF) | 112 | 49.3 | 79 | 33.6 | |
| (b) | ||||||
Reasons for commuting by car to the BCTF (in %).
| Using the Ride with Somebody | No Alternative | Speed | Price | Safety | Comfort and Time Predictability | Easy to Park | Combining Other Activities | |
|---|---|---|---|---|---|---|---|---|
| Staff-Female | 7.0 | 10.3 | 22.7 | 2.2 | 0.0 | 27.0 | 11.9 | 18.9 |
| Students-Female | 15.1 | 6.7 | 30.3 | 2.5 | 1.7 | 28.6 | 1.7 | 13.4 |
| Staff-Male | 8.4 | 7.9 | 26.0 | 4.7 | 1.9 | 29.3 | 10.2 | 11.6 |
| Students-Male | 15.9 | 9.3 | 29.9 | 0.0 | 0.9 | 25.2 | 5.6 | 13.1 |
| Staff-All | 7.9 | 9.1 | 24.4 | 3.5 | 1.0 | 28.1 | 10.9 | 15.1 |
| Students-All | 15.3 | 8.3 | 29.7 | 1.3 | 1.3 | 27.1 | 3.5 | 13.5 |
Parking options (a) and time to find parking place (b) for students and staff of the BCTF (in %).
| Free Street Parking | Paid Street Parking | Parking Garage | Paid BCTF Parking for Staff | Free BCTF Parking for Staff | |
|---|---|---|---|---|---|
| Staff—Female | 4.8 | 4.8 | 4.8 | 41.0 | 44.6 |
| Students—Female | 53.7 | 36.6 | 7.3 | 0.0 | 2.4 |
| Staff—Male | 3.3 | 3.3 | 7.8 | 52.2 | 33.3 |
| Students—Male | 47.5 | 32.5 | 10.0 | 5.0 | 5.0 |
| Staff—All | 4.0 | 4.0 | 6.2 | 48.0 | 37.9 |
| Students—All | 51.2 | 34.1 | 8.5 | 2.4 | 3.7 |
| (a) | |||||
| No Need to Search | Less than 5 min | 5–10 min | 10–15 min | More than 15 min | |
| Staff—Female | 78.6 | 20.2 | 1.2 | 0.0 | 0.0 |
| Students—Female | 18.8 | 31.3 | 31.3 | 12.5 | 6.3 |
| Staff—Male | 83.5 | 15.4 | 1.1 | 0.0 | 0.0 |
| Students—Male | 24.4 | 42.2 | 24.4 | 8.9 | 0.0 |
| Staff—All | 80.4 | 17.9 | 1.7 | 0.0 | 0.0 |
| Students—All | 21.3 | 36.2 | 28.7 | 10.6 | 3.2 |
| (b) | |||||
Barriers to using active modes of transport and shared cars for commuting to the BCTF (in %).
| Mode | Main barriers | ||||
| Walk | 1. Too long distance | 2. Preferring other modes | 3. Hygiene | 4. Unattractive path | 5. Health issues |
| All (i + ii) | 56.3 | 21.4 | 10.0 | 8.9 | 2.0 |
| i. Students | 47.9 | 29.8 | 8.3 | 11.6 | 0.0 |
| Female | 50.0 | 29.0 | 8.1 | 12.9 | 0.0 |
| Male | 44.8 | 31.0 | 8.6 | 10.3 | 0.0 |
| ii. Staff | 60.7 | 17.0 | 10.9 | 7.4 | 3.1 |
| Female | 59.7 | 18.5 | 11.8 | 6.7 | 3.4 |
| Male | 60.7 | 15.9 | 10.3 | 8.4 | 2.8 |
| Bicycle | 1. Too long distance | 2. Bad infrastructure | 3. Ownership | 4. Preferring other modes | 5. Thievery |
| All (i + ii) | 22.5 | 16.3 | 14.8 | 13.8 | 12.1 |
| i. Students | 17.1 | 12.9 | 23.4 | 17.9 | 16.0 |
| Female | 16.2 | 10.8 | 25.2 | 19.4 | 14.9 |
| Male | 18.4 | 16.9 | 19.9 | 15.4 | 18.4 |
| ii. Staff | 28.6 | 20.0 | 5.2 | 9.2 | 7.7 |
| Female | 27.5 | 20.0 | 4.4 | 8.8 | 8.1 |
| Male | 29.4 | 20.0 | 6.3 | 10.0 | 6.9 |
| Public Transport | 1. Too short distance | 2. Lack of connections | 3. Frequency | 4. Bus stop too far | 5. Preferring other modes |
| All (i + ii) | 23.9 | 20.2 | 20.0 | 16.1 | 15.9 |
| i. Students | 37.0 | 13.3 | 14.3 | 12.0 | 17.7 |
| Female | 40.1 | 14.3 | 12.1 | 12.1 | 15.4 |
| Male | 31.3 | 12.2 | 18.3 | 12.2 | 20.9 |
| ii. Staff | 13.2 | 25.8 | 24.7 | 19.5 | 14.5 |
| Female | 15.9 | 24.2 | 24.2 | 19.2 | 15.4 |
| Male | 10.7 | 27.1 | 25.4 | 19.2 | 14.1 |
| Car Sharing | 1. Time adapting | 2. Too short distance | 3. Punctuality | 4. Lack of information | 5. Exit locations |
| All (i + ii) | 26.3 | 25.8 | 12.1 | 11.9 | 11.9 |
| i. Students | 20.5 | 28.4 | 11.9 | 10.2 | 12.2 |
| Female | 18.7 | 32.8 | 11.1 | 7.6 | 12.1 |
| Male | 22.3 | 22.3 | 13.5 | 13.5 | 12.2 |
| ii. Staff | 32.2 | 23.2 | 12.2 | 13.6 | 11.6 |
| Female | 33.5 | 25.3 | 10.6 | 12.9 | 13.5 |
| Male | 31.4 | 21.9 | 13.0 | 14.2 | 10.1 |
Interventions to using active modes of transport and shared cars for commuting to the BCTF (in %).
| Mode | Interventions | ||||
| Walk | 1. Nothing | 2. Green infrastructure | 3. Safer infrastructure | 4. Priority rules | 5. Awareness |
| All (i + ii) | 42.4 | 21.9 | 17.3 | 9.9 | 5.7 |
| i. Students | 34.5 | 24.6 | 19.3 | 11.4 | 7.0 |
| Female | 36.8 | 23.4 | 19.9 | 10.9 | 5.5 |
| Male | 29.2 | 27.0 | 19.0 | 12.4 | 9.5 |
| ii. Staff | 51.9 | 18.8 | 15.0 | 8.0 | 4.2 |
| Female | 54.7 | 17.3 | 14.4 | 8.6 | 2.9 |
| Male | 47.9 | 20.8 | 16.0 | 7.6 | 5.6 |
| Bicycle | 1. Nothing | 2. Priority rules | 3. Integration with PT | 4. Bicycle rent system | 5. E-bikes subventions |
| All (i + ii) | 30.0 | 14.4 | 14.3 | 13.2 | 13.0 |
| i. Students | 25.7 | 12.3 | 14.4 | 17.8 | 13.6 |
| Female | 25.7 | 12.2 | 15.3 | 18.0 | 13.5 |
| Male | 23.9 | 12.9 | 13.5 | 18.1 | 14.2 |
| ii. Staff | 35.0 | 16.9 | 14.1 | 7.7 | 12.3 |
| Female | 38.0 | 17.1 | 13.3 | 6.3 | 8.2 |
| Male | 31.9 | 16.6 | 15.3 | 8.0 | 16.6 |
| Public Transport | 1. Faster travelling | 2. Synchronisation | 3. Accessibility | 4. Less delays | 5. Ticketing |
| All (i + ii) | 22.1 | 15.5 | 15.0 | 13.9 | 13.8 |
| i. Students | 20.3 | 14.8 | 13.5 | 16.9 | 14.8 |
| Female | 20.9 | 16.0 | 12.8 | 16.3 | 16.3 |
| Male | 18.9 | 13.5 | 15.1 | 17.3 | 13.0 |
| ii. Staff | 24.2 | 16.3 | 16.6 | 10.7 | 12.8 |
| Female | 24.3 | 15.9 | 15.0 | 12.1 | 11.7 |
| Male | 24.7 | 16.7 | 18.1 | 8.8 | 14.1 |
| Car Sharing | 1. Nothing | 2. Reserved parking lots | 3. Help to find co-users | 4. Awareness | 5. Stops |
| All (i + ii) | 23.9 | 20.4 | 18.6 | 13.6 | 10.2 |
| i. Students | 23.2 | 26.3 | 16.7 | 12.2 | 8.6 |
| Female | 22.9 | 26.4 | 16.7 | 13.2 | 9.7 |
| Male | 23.3 | 26.7 | 16.0 | 10.7 | 7.3 |
| ii. Staff | 24.6 | 14.4 | 20.6 | 15.0 | 11.8 |
| Female | 23.1 | 15.4 | 22.5 | 12.6 | 12.6 |
| Male | 26.5 | 13.5 | 17.8 | 17.8 | 10.8 |
Proposed interventions for the TDM strategy at the BCTF.
| Field | Intervention | Description |
|---|---|---|
| General Measures | Sustainable mobility plan for BCTF | preparation of sustainable mobility plan for the BCTF introducing a mobility coordinator for the BCTF introducing the BCTF Mobility Fund national initiative on sustainable mobility plans for commuters to institutions of higher education |
| Charging stations | introducing new charging stations (preferably at new sustainable mobility hub) | |
| Teleworking | supporting working from home (when possible) | |
| Sustainable mobility hub | introducing a new sustainable mobility hub (charging stations for e-cars, parking places for carpooling, bicycle station, …) | |
| Walking | Promotional activities | providing promo equipment |
| providing hygiene facilities | ||
| Pedestrian paths | improving quality of walk paths | |
| greener/safer walk paths | ||
| Cycling | Cycling infrastructure | improving cycling infrastructure providing safe parking facilities (cycling station) providing new racks at main entrances |
| New services | introducing a rent-a-bike system (2022–future expansion) and e-bike system | |
| introducing company e-bikes | ||
| Promotional activities | providing promo equipment | |
| integration with PT (suburban PT, railroad) | ||
| Public Transport | Reorganization of PT lines | reorganization of PT lines |
| Improved information | displays for arrival/departure times of buses on neighboring bus stops on main entrances to the BCTF (RTPI), other PPT-related information | |
| Bus stops | introducing smart bus stops | |
| Parking Policy | Reorganization of parking policy | lowering number of “classic” parking lots for staff (selection of users by sustainability index) |
| increasing the price of parking |
Supplementary Materials
The following supporting information can be downloaded at:
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