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Article

Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye

by
Raziye Peker
1,
Mustafa Sinan Yardim
1 and
Kadir Berkhan Akalin
2,*
1
Department of Civil Engineering, Yildiz Technical University, Istanbul 34220, Turkey
2
Department of Civil Engineering, Eskisehir Osmangazi University, Eskisehir 26480, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9660; https://doi.org/10.3390/su16229660
Submission received: 21 September 2024 / Revised: 18 October 2024 / Accepted: 1 November 2024 / Published: 6 November 2024
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Abstract

:
This study explores the transportation behaviors of university students residing in dormitories in Kütahya, Türkiye, emphasizing their preferred modes for educational trips. Utilizing a Multinomial Logit model, the research analyzes the influence of socio-demographic factors, trip characteristics, and environmental perceptions on mode choice. The results indicate that public transport and walking are the predominant modes, with significant negative associations being observed between car ownership and the likelihood of choosing these sustainable options. Key findings reveal that, as trip distances increase, students are more likely to use public transport, while higher income levels decrease reliance on both public transport and walking. Male students demonstrate a higher preference for these modes compared to female students. Environmental perceptions, including feelings of safety and satisfaction with infrastructure, play a critical role in shaping transportation choices, highlighting the need for improved lighting, walkability, and public transport quality. These insights have important implications for transportation policy, suggesting that reducing private vehicle reliance and enhancing public transport services can significantly promote sustainable travel behaviors. Overall, the study underscores the importance of comprehensive transportation policies that not only enhance infrastructure and service quality but also consider environmental perceptions and safety to promote sustainable travel behaviors among university students.

1. Introduction

Urban transportation planning must address the diverse needs of all population subgroups to create comprehensive regional demand models. Among these subgroups, university students are often under-represented in transportation surveys despite their significant impact on urban traffic. In 2021, the number of university students per 1000 inhabitants was 21.90 in EU countries, 57.39 in the U.S., and 93.03 in Türkiye. By 2024, these figures increased to 57.76 in the U.S. and 81.78 in Türkiye [1,2,3]. These substantial figures emphasize the importance of understanding university students’ transportation habits for predicting demand and devising effective urban mobility solutions.
University students typically reside near campuses, favoring areas that support alternative transportation modes while fulfilling their daily needs. Many students, especially those living in dormitories, rely heavily on dorm-to-campus commutes. Various factors influence their mode choice, including the quality of transportation infrastructure, socio-economic conditions, and environmental aspects such as safety and lighting. As a result, urban transportation planners must account for the specific needs of this demographic when designing regional systems.
Several challenges arise in analyzing the transportation mode choices of university students:
  • Geographic concentration of campuses: University campuses are often clustered in city centers, intensifying transportation demand. Numerous studies examining the influence of land-use patterns on transportation preferences highlight how these factors impact students’ decisions. Socio-demographic factors, daily activities, and travel behavior are crucial in shaping students’ mode choices. Low-income students often prefer public transportation due to a lack of alternatives, while wealthier students favor private vehicles for safety and convenience [4,5,6]. Off-campus students’ mode choices are also influenced by socio-economic factors, transportation availability, and housing location [7]. Key criteria affecting mode choice include vehicle availability, trip origin, and accessibility [8].
  • Urban transportation options: The variety and quality of public transportation can significantly influence student preferences. Shifting to more environmentally friendly modes, such as public transit and micro-mobility, could alleviate urban traffic congestion [9,10,11]. Studies have shown that integrating micro-mobility options can improve public transportation systems [12].
  • Accessibility and availability of public transportation: The infrastructure connecting dormitories and campuses significantly influences students’ decisions. Research indicates that students living in suburban areas with limited transport access tend to make fewer, longer trips, affecting their educational opportunities [13]. Improving sustainable transportation could benefit students while promoting social equity [8,14,15].
University students represent a unique subgroup with diverse demographic characteristics [16] and exhibit complex travel behaviors as they balance work, study, and social life [17]. For instance, students without access to private vehicles often depend on carpooling with friends or using public buses, highlighting a significant level of social dependency [18]. As public transportation becomes more critical, factors such as travel distance, time, cost, and safety increasingly shape students’ mode choices [19]. Key determinants of transportation preferences also include accessibility and comfort [20].
Research has categorized students based on their transportation habits, underscoring how socio-demographic characteristics and attitudes shape these preferences [21]. In central business districts, walking and public transport are the dominant modes, while off-campus students tend to exhibit more varied travel behaviors. The availability and quality of urban transportation services play a crucial role in students’ daily transportation decisions [10,14,22,23].
Transportation demand among students is also shaped by policy and infrastructure. For instance, improving micro-mobility or shared transport services, such as bike- and scooter-sharing systems, could enhance students’ daily commutes and reduce reliance on private transport. Public transportation services are especially vital for low-income students; improving service quality, safety, and accessibility could promote social equity and sustainability on university campuses [10,14,20,22,23].
For students residing in dormitories, transportation preferences are influenced by socio-economic conditions, transportation availability, and proximity to campus [7,8]. Additionally, age, gender, and campus infrastructure quality affect their choices [19,24]. Proximity between dormitories and campuses often encourages walking or cycling, while students with more distant commutes tend to rely on motorized modes [25]. Clearly, individual, environmental, and policy factors shape the transportation behaviors of dormitory residents.
When addressing transportation mode choice problems, Multinomial Logit [26,27,28], Nested Logit [26], Heteroscedastic Extreme Value [28], Negative Binomial [29], and Analysis of Variance [30] have been widely used [31,32,33]. While machine learning (ML) techniques such as Support Vector Machines [34], Extreme Gradient Boosting [35], and Naive Bayes [36], have shown potential in transportation studies [37,38], traditional choice models offer advantages in terms of variable estimation and scenario analyses [39,40]. The ability of these models to interpret the magnitude and sign of key variables makes them well suited for policy-driven analyses.
This study aims to investigate the factors influencing transportation mode preferences for educational trips among university students living in dormitories in Kütahya, Türkiye. While previous studies have explored student travel behaviors, this research offers a novel contribution by incorporating environmental perceptions and infrastructure quality, in addition to socio-demographic and economic factors, into the analysis of transportation mode choices.
This study is structured around two main research questions:
  • What factors influence the transportation mode choices of dormitory resident university students in Kütahya, Türkiye?
  • How do perceptions of the environment and infrastructure quality affect these transportation behaviors?
The remainder of this paper is structured as follows. Section 2 describes the data collection process and study area, providing an overview of the key variables used in the analysis. Section 3 outlines the methodology, including the Multinomial Logit model applied to analyze transportation mode preferences. In Section 4, the results of the model are presented, focusing on the key factors influencing students’ transportation choices. Section 5 discusses the key findings in relation to previous studies. Finally, Section 6 concludes the paper by addressing policy implications, study limitations, and suggestions for future research.

2. Data Description

The primary data source for this study is a dormitory survey conducted with university students residing in state dormitories as part of the Transportation and Parking Master Plan for Kütahya, Türkiye (KTMP) [41]. A total of 1027 students participated in the survey, which was conducted face-to-face, based on a sample ratio determined for the KTMP. The campus areas and dormitories within the study region are depicted in Figure 1. While most dormitories are located approximately one kilometer from various campus areas, many students live in more distant dormitories relative to their respective campuses. This makes walking less feasible for their daily commutes, highlighting the need to investigate the factors influencing their transportation mode preferences for their education-related trips.
The survey was conducted during the 2022–2023 academic year and was structured similarly to household surveys. The dataset includes revealed preference travel data at the individual level, along with socio-economic characteristics, infrastructure perception, and environmental satisfaction assessments.
Table 1 presents descriptive statistics for key variables in the study. The average trip distance is 1.92 km, and the average annual income per person is USD 2094.35. The mean age of respondents is 20.31 years. In terms of gender distribution, 64.17% of the participants are female and 35.83% are male. The majority, 64.07%, are enrolled in daytime education, while 35.93% attend evening classes. Only 5.60% of the respondents are employed, and 5.49% have access to a private vehicle. Regarding transportation modes, 33.20% of the participants use public transport, which is exclusively serviced by buses, 64.56% walk, and 2.24% use other modes of transport, including private vehicles, carpooling, or other shared options.
In addition to travel and socio-economic data, the survey collected responses on environmental perceptions and infrastructure satisfaction using a five-point Likert scale. Figure 2 provides a summary of these responses, which cover issues such as the presence of stray animals, overall safety in the surrounding environment, adequacy of street lighting, condition of sidewalks and pedestrian crossings, and overall satisfaction with the public transportation system along their routine travel routes.

3. Methodology

This study aims to examine the transportation behavior of university students residing in dormitories and to identify their most frequently preferred modes of transport for educational trips. To achieve this, a Multinomial Logit (MNL) model was employed. As illustrated in the flowchart in Figure 3, utility functions were constructed using individual travel characteristics, socio-demographic data, and responses related to infrastructure perception and environmental satisfaction. Prediction parameters were calculated, and the best-fitting model was selected based on performance.
Previous studies have shown that transportation mode choice is a complex decision-making process influenced by individuals’ travel behaviors, preferences, and environmental factors [39,42,43,44,45,46,47]. Mathematical models are frequently used to analyze the relationship between these variables and transportation choices [45,48,49]. The MNL model is particularly useful for examining situations where individuals must choose between discrete alternatives, with the probability of choosing a specific mode being determined by the characteristics of both the options and the decision-maker [42,43,48,50]. Based on its proven ability to handle multiple alternatives and its capacity to predict future travel behavior, the MNL model was selected for this study.

3.1. Multinomial Logit Model

The MNL model was used to analyze the factors influencing students’ transportation mode choices. This model is grounded in utility maximization, where each individual selects the option that provides them the highest utility. The utility function is typically formulated as a linear combination of explanatory variables, and the probability of choosing a specific mode depends on the attributes of the alternatives and the decision-maker. For an individual i choosing among J transport modes, the systematic component of the utility function V i j for mode j is expressed as [39,43,51]:
V i j = β j 0 + ∑ k β j k x i j k
where
β j 0 , β j k are the alternative specific parameters of mode j in the dataset with k explanatory variables and
x i j k is the vector of explanatory variables.
The probability, P i j , that individual, i , chooses mode, j , is given by [39,43,51]:
P i j = e V i j ∑ J e V i J
The parameters of the MNL model are estimated using the maximum likelihood method. To facilitate estimation, the log-likelihood (LL) function is used, as it simplifies computations by converting the products of probabilities into sums [39,43,51]:
L L = l n L β = ∑ ∀ i ∈ I ∑ ∀ j ∈ J δ i j × l n ( P i j β )
where
δ i j is the selection indicator, which takes the value 1 if individual i chooses mode j ; it takes the value of 0 otherwise.

3.2. Performance Evaluation

The model’s performance was evaluated using several goodness-of-fit measures, including the likelihood ratio ( L R ) and McFadden’s pseudo- R 2 . The significance of the estimated coefficients was assessed using z-statistics.
The L R test compares a model to a reference model by evaluating the difference between their log-likelihood values [39,43,52]:
L R = − 2 L L r e f e r e n c e   m o d e l − L L f i n a l   m o d e l
This difference is tested against a critical chi-square value. If the calculated L R value exceeds the critical value, the estimated model is deemed to be significantly better than the reference model. We exclusively used constant terms in the utility functions as the baseline reference model within this study.
McFadden’s pseudo-R2 is a measure used to evaluate the goodness-of-fit of models [39,42,53]:
M c F a d d e n ′ s   p s e u d o   R 2 = 1 − L L f i n a l   m o d e l L L r e f e r e n c e   m o d e l
Unlike the traditional R2, which explains variance, McFadden’s pseudo-R2 is based on the likelihood of the model, with values between 0.2 and 0.4, indicating a good fit for discrete choice models [39,42,53].

4. Model Analysis Results and Discussion

The results of the Multinomial Logit (MNL) model highlight key factors influencing university students’ transportation mode choices, particularly between public transport and walking, compared to other modes. The estimated coefficients (Betas) and odds ratios (ORs) provide insights into how socio-demographic factors, trip characteristics, and environmental perceptions shape these decisions (Table 2). Out of the 36 estimated parameters from the utility functions, which included 17 variables and constant terms, 22 were statistically significant at the 90% confidence level. The McFadden’s pseudo-R2 of 0.24 and adjusted pseudo-R2 of 0.21 indicate that the model accounts for a reasonable portion of the variability in transport mode choice. Moreover, the chi-square statistic (44.90), with 34 degrees of freedom at a 0.10 significance level, suggests that the model fits the data significantly better than the reference model, which only included constant terms.
The model results are interpreted in two subsections: Trip Characteristics and Individual Attributes and Environmental Perceptions and Satisfaction with Infrastructure, as detailed below:
  • Trip Characteristics and Individual Attributes:
The coefficient for trip distance shows that individuals are more likely to choose public transport as trip distances increase, while the likelihood of walking decreases substantially. As distance increases by one kilometer, the likelihood of choosing public transport rises by 16.1%, while the odds of walking decrease by 55.8%. This suggests that public transport becomes more attractive for longer distances, while walking is preferred for shorter trips, which is consistent with previous studies [54,55]. The contrast with findings where public transport use decreases with distance [7] may arise from differences in choice set, as we include private car, shared transport, and carpooling in the same reference category, where effects are measured relative to this category.
A higher income reduces the odds of selecting both public transport and walking, indicating that wealthier individuals are more likely to use private vehicles or motorized alternatives [54,56]. Our results reveal that, for every USD 1000 increase in annual income, the odds of choosing public transport decrease by 9.8% and the odds of walking decrease by 20.2%. However, it is important to clarify that this refers specifically to students’ personal annual income or allowances.
Age appears to have minimal impact on the choice of public transport (OR = 1.03) but slightly reduces the likelihood of walking (OR = 0.90) as individuals age. This modest decline in walking could be attributed to physical mobility issues or a growing preference for more comfortable modes of travel with age. Interestingly, gender plays a significant role, with males being much more likely to choose both public transport and walking compared to other modes. Males are 5.14 times more likely to choose public transport and 3.03 times more likely to choose walking. These findings may reflect gender differences in attitudes toward sustainable modes of transport or distinct occupational and travel patterns. While similar results are reported in the literature [54,57], some studies show opposite trends [7,18,55,58]. This divergence could indicate that, in environments where sustainable transportation modes and infrastructure are promoted, environmental impacts take precedence over socio-demographic factors influencing transportation choices.
Daytime students are 81% more likely to choose public transport and 62.9% more likely to walk, reflecting accessibility benefits in urban settings. Employed individuals, however, are 48.8% less likely to use public transport and 63% less likely to walk, likely due to time constraints.
Car ownership strongly deters public transport and walking, similar to previous findings [54,56,59]. Having access to private or shared transport decreases the likelihood of choosing public transport by 64.4% and walking by 84.1%. This strong deterrent effect underscores the reliance on private vehicles when available, suggesting that policies aimed at encouraging sustainable modes should prioritize strategies that address the convenience and affordability of car ownership. The very low odds ratios for walking in the presence of car ownership suggest that, without targeted interventions, walking will remain a less preferred option when private vehicles are accessible.
  • Environmental Perceptions and Satisfaction with Infrastructure:
Perceptions of the environment and satisfaction with local infrastructure also play a significant role in mode choice. Concerns about stray animals decrease the odds of using public transport (OR = 0.89 to 0.44) and walking (OR = 0.78 to 0.58). This finding could imply that concerns over safety or discomfort with outdoor conditions may push individuals toward more enclosed modes of transport. On the other hand, individuals who feel safe in their surroundings are more likely to choose public transport (OR = 1.23 to 1.27) and walking (OR = 1.11 to 1.23), suggesting that improving public safety could encourage greater use of both sustainable modes. These findings are consistent with previous studies [18,55,60].
Adequate lighting improves the likelihood of choosing public transport (OR = 1.02 to 1.40) and walking (OR = 1.17 to 1.71). Well-lit streets and pedestrian pathways can increase perceptions of safety, particularly for walking, making these modes more attractive, especially during the evening or at nighttime. Satisfaction with sidewalks and pedestrian crossings also has a positive effect on both public transport (OR = 1.15 to 1.16) and walking (OR = 1.01 to 1.53), reinforcing the idea that better walking conditions support not only walking but also transit use, as good pedestrian infrastructures often facilitate access to public transport stops.
Satisfaction with the public transport system shows one of the strongest effects, significantly increasing the odds of choosing public transport (OR = 1.52 to 5.72) and walking (OR = 1.40 to 4.26). Improvements in public transport service quality can thus lead to substantial shifts toward sustainable modes [18,55,61].

5. Discussion

In the context of Kütahya, a small and horizontally expanded city, environmental factors such as infrastructure quality and safety play a more significant role in the transportation mode choice compared to larger metropolitan areas, where factors like distance and income tend to dominate. The integration of environmental perceptions into the analysis provides a unique perspective, suggesting that improving local infrastructure and addressing safety concerns can encourage more sustainable transportation behaviors. Consistent with previous studies, public transport becomes more attractive for longer distances, while walking is preferred for shorter trips. However, this contrasts with findings where public transport use decreases with distance, which may be attributed to differences in the choice set, as this study groups rideshare, private, and shared transport in the same reference category due to the small number of observations for these modes. A higher income reduces the odds of selecting both public transport and walking, indicating a preference for private vehicles or motorized alternatives, similar to previous findings. Gender differences in attitudes toward sustainable transport and distinct occupational patterns also influence mode choice, with some studies reporting opposite trends, particularly in regions with well-developed sustainable transportation infrastructures. Car ownership remains a strong deterrent to using public transport and walking, consistent with prior research, further emphasizing the need for targeted interventions that improve public transport service quality and safety in order to encourage sustainable travel behaviors.
The transportation preferences of university students in Kütahya, as revealed in this study, indicate a strong reliance on walking and public transportation for daily trips between dormitories and campuses. This suggests that students continue to use public transportation out of necessity, even if they are not fully satisfied with the current system. This observation highlights the following a critical insight: the introduction of a viable alternative to public transportation could lead to a shift in mode choice without necessarily improving the quality of the existing public transportation system. This potential change should be carefully considered by decision-makers who seek to enhance overall student satisfaction with transportation options.
However, Kütahya has the opportunity to remain a sustainable city by reducing its carbon footprint and improving traffic congestion through enhancements to its public transportation system and infrastructure. Therefore, it is recommended that Kütahya implements policies that focus on improving public transportation services, increasing the safety and accessibility of pedestrian paths and addressing concerns related to stray animals and environmental safety. These measures would encourage more sustainable transportation behaviors. The findings suggest that targeted interventions aimed at improving infrastructures and addressing safety concerns can shift student behavior toward more sustainable travel patterns, even if additional alternatives are introduced. These priorities should be at the forefront of future urban transportation planning.

6. Conclusions

This study explored the transportation mode preferences of university students residing in dormitories, focusing on the factors influencing their decisions to choose public transport and walking over other modes. Using a Multinomial Logit model, we identified key trip characteristics, socio-demographic attributes, and perceptions of the environment and infrastructure that significantly impact students’ choices. The findings offer several insights with important implications for transportation policy and urban planning.
Trip distance emerged as a decisive factor, with students being more likely to choose public transport for longer distances and walking for shorter trips. Income, gender, employment status, and car ownership further shaped these decisions, with higher-income individuals, employed students, and those with access to cars showing a clear preference for private and shared transport use. Notably, male students demonstrated a higher likelihood of choosing both public transport and walking, suggesting potential gender-based differences in attitudes or accessibility to sustainable modes.
Environmental perceptions and satisfaction with local infrastructure also played a crucial role in determining transportation choices. Students who felt safer and more satisfied with their surroundings, including the quality of sidewalks, lighting, and the public transport system, were more likely to opt for sustainable modes. In particular, satisfaction with public transport services showed one of the strongest effects, highlighting the importance of service quality in encouraging a shift away from motorized vehicles.
Overall, the results underscore the need for integrated transportation policies that improve the quality, accessibility, and safety of sustainable modes. The relevance of this study to sustainable development lies in its focus on encouraging the use of sustainable transportation modes, such as walking and public transport, for educational trips between dormitories and university campuses. By understanding the factors that influence students’ daily commuting choices, this research provides insights into how policies and infrastructure improvements can reduce reliance on private vehicles, thereby promoting lower carbon emissions, improved air quality, increased mobility, and healthier living. Enhancing public transport services, improving pedestrian infrastructure, and addressing the affordability and convenience of private car ownership could lead to a more balanced and sustainable transportation system. For university campuses and urban areas alike, these strategies could promote healthier, more environmentally friendly transportation behaviors among students and the broader population.
Future research should examine the influence of emerging mobility options, such as electric vehicles, shared transport, and micro-mobility services, on mode choice. Additionally, extending the analysis to include larger datasets from cities with diverse characteristics and incorporating a broader range of variables will provide a more nuanced understanding of how various factors—such as urban-level, travel-level, and individual-level influences—affect transportation choices. Large and varied datasets enable researchers to capture a wider range of behaviors and conditions, enhancing the generalizability and applicability of the findings to different urban contexts. To achieve this, it would be beneficial to integrate logit models with different machine learning and regularization techniques, aiming to address issues related to uneven class distribution or overfitting when working with large datasets derived from diverse variables and cities with varying characteristics.

Author Contributions

Conceptualization, R.P., M.S.Y. and K.B.A.; Methodology and Supervision, K.B.A. and M.S.Y.; Software, Formal Analysis and Visualization, K.B.A.; Recourses, Validation, Investigation, Data Curation and Writing—Original Draft Preparation, K.B.A. and R.P.; Writing—Review and Editing, K.B.A. and M.S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This study was not funded by any external sources.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are not publicly available due to privacy restrictions.

Acknowledgments

The authors would like to acknowledge that this paper is submitted in partial fulfillment of the requirements for a Ph.D. degree at Yildiz Technical University.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 09660 g001
Figure 1. Campus areas and dormitories in the study region.
Figure 1. Campus areas and dormitories in the study region.
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Figure 2. Summary distributions of environmental perception and infrastructure satisfaction survey responses.
Figure 2. Summary distributions of environmental perception and infrastructure satisfaction survey responses.
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Figure 3. Flowchart of the study.
Figure 3. Flowchart of the study.
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Table 1. Descriptive statistics of key variables.
Table 1. Descriptive statistics of key variables.
VariablesSortsAverage
Trip distance (km)Numeric1.92
Annual income (per person in USD)Numeric2094.35
AgeNumeric20.31
GenderFemale64.17%
Male35.83%
Education timeDaytime64.07%
Evening35.93%
OccupationOccupied5.60%
Else94.40%
Car ownershipAvailable5.49%
Else94.51%
ModePublic Transport33.20%
Walk64.56%
Other2.24%
Table 2. MNL model analysis results.
Table 2. MNL model analysis results.
VariablesEstimate (Betas)Odds Ratio (OR)
Public TransportWalkPublic TransportWalk
    Constant0.8916.575 **
Trip Characteristics and Individual Attributes
    Trip distance (km)0.149 *−0.816 **1.1610.442
    Annual income (×1000 USD)−0.103−0.225 *0.9020.798
    Age0.026−0.1081.0270.898
    Gender: Male1.638 ***1.108 *5.1433.028
    Education Time: Daytime0.594 *0.488 *1.8101.629
    Occupation: Occupied−0.670 **−0.995 ***0.5120.370
    Car ownership: Available−1.034−1.842 *0.3560.159
Environmental Perceptions and Satisfaction with Infrastructure
    I feel disturbed by stray animals:
            Agree−0.108−0.2550.8980.775
            Strongly agree−0.823 **−0.537 **0.4390.584
    I feel safe in my surroundings:
            Agree0.2070.1051.2301.111
            Strongly agree0.2380.2041.2681.226
    The lighting is adequate:
            Agree0.0200.1101.0201.116
            Strongly agree0.336 *0.534 *1.4001.705
    I am satisfied with the condition of sidewalks and pedestrian crossings:
            Agree0.139 *0.0131.1491.013
            Strongly agree0.145 *0.423 **1.1531.527
    I am satisfied with the public transport system:
            Agree0.421 **0.335 *1.5231.397
            Strongly agree1.743 ***1.450 ***5.7174.263
Summary statistics
    Final model log-likelihood −634.52
    Reference model log-likelihood−829.55
    McFadden’s pseudo-R20.24
    Adjusted pseudo-R20.21
    Likelihood ratio390.06
    Chi-square critical value44.90 (34, 0.10)
Note: “Other” modes are used as the reference category in this analysis. Confidence levels: * 90%, ** 95%, *** 99%.
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Peker, R.; Yardim, M.S.; Akalin, K.B. Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye. Sustainability 2024, 16, 9660. https://doi.org/10.3390/su16229660

AMA Style

Peker R, Yardim MS, Akalin KB. Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye. Sustainability. 2024; 16(22):9660. https://doi.org/10.3390/su16229660

Chicago/Turabian Style

Peker, Raziye, Mustafa Sinan Yardim, and Kadir Berkhan Akalin. 2024. "Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye" Sustainability 16, no. 22: 9660. https://doi.org/10.3390/su16229660

APA Style

Peker, R., Yardim, M. S., & Akalin, K. B. (2024). Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye. Sustainability, 16(22), 9660. https://doi.org/10.3390/su16229660

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