Skip to Content
MDPIMDPI

Advanced

SustainabilitySustainability
PDF
  • Article
  • Open Access

31 July 2025

22 Pages

Methodology and Innovation in the Design of Shared Transportation Systems for Academic Environments

,
,
and
GITICEA AND GMOVINT Research Groups, Automotive Engineering Career, Politecnica Salesiana University, Guayaquil 090109, Ecuador
*
Author to whom correspondence should be addressed.
Sustainability2025, 17(15), 6946;https://doi.org/10.3390/su17156946
Version Notes
Review Reports
Order Reprints

Abstract

At the Politecnica Salesiana University (UPS) in Guayaquil, Ecuador, urban mobility challenges were addressed with the aim of improving students’ quality of life and promoting sustainability. This study evaluated the technical, economic, and social feasibility of implementing a shared transportation (carpooling) system using a quantitative-descriptive approach. Surveys were applied to a stratified sample of 256 students to analyze transportation habits. Route planning was performed using ArcGIS software, and costs were calculated with Microsoft Excel. Social impact assessment involved focus groups and analysis of variables such as changes in mobility patterns, system acceptance, and perceived safety, comfort, and accessibility. Key indicators included the percentage of students willing to participate in the pilot (82.7%), satisfaction with travel time savings (85.7% fully satisfied), and positive perceptions of safety and comfort. The results suggest that the proposed system is not only economically viable but also widely accepted by students, contributing to reduced stress, travel time, and single-occupancy vehicle use. This study demonstrates the feasibility of shared transport in urban universities and provides a replicable model to guide sustainable mobility policies that improve safety, comfort, and efficiency in student commuting.

1. Introduction

Traffic congestion in Guayaquil represents a significant problem that affects both residents and visitors to the city [1]. The increasing number of vehicles in circulation has caused the main arteries and secondary streets to experience heavy traffic throughout the day, especially during rush hour [2]. This congestion not only delays travel time and increases stress levels among drivers and passengers but also contributes to environmental pollution and the degradation of air quality [3]. In addition, heavy traffic negatively impacts the efficiency of public and private transportation, increasing operating costs and reducing the overall productivity of the city. As Guayaquil continues to grow, the need for effective solutions to mitigate traffic congestion becomes increasingly urgent, requiring a combination of infrastructure improvements, sustainable transport policies, and the promotion of alternatives such as ride-sharing.
In addition to shared transportation systems, short-distance cycling within urban areas and university campuses plays an important role in promoting sustainable mobility. Previous research has emphasized the importance of integrating bicycle traffic solutions into urban mobility strategies, especially in academic environments where infrastructure and trip length make cycling a viable alternative [4].
E-Commerce has been evolving more and more, because the business world moves so fast that companies must always be up to date with new technologies and trends to stay competitive [5]. In this circumstance, mobility solutions and the reduction of traffic congestion are important to not only improve the quality of life of students but also facilitate economic development and the growth of electronic commerce in the city, since efficient transportation can reduce travel time and optimize the supply chain. The Politecnica Salesiana University (UPS) in Guayaquil faces a significant challenge in relation to the mobility of its students [6]. The congestion of vehicles in the city, especially during rush hour, makes it difficult for students to get to campus in a timely manner, affecting their punctuality and participation in academic activities [7]. This situation not only generates stress and loss of time but can also negatively impact academic performance [8].
Understanding vehicular behavior and improving traffic flow prediction are essential foundations for designing efficient shared transportation systems. Chen et al. [9] investigated lane-changing behavior on highways using high-resolution vehicle trajectory data, revealing significant behavioral patterns that influence traffic dynamics and system efficiency. These insights are particularly relevant for planning shared mobility routes in congested environments, as they inform lane usage, merging points, and flow optimization. Additionally, Chen et al. [10] compared several denoising schemes combined with artificial neural networks (ANNs) for traffic flow prediction based on real-time sensor data. Their findings demonstrate that accurate short-term predictions can substantially support dynamic route planning and adaptive ridesharing decisions. Together, these studies emphasize the importance of integrating behavioral and predictive analytics into the design of smart transportation solutions in academic settings.
The implementation of shared transportation systems in academic environments has received increasing attention in recent years due to its potential to improve student mobility and reduce environmental impacts. Etminani-Ghasrodashti et al. [11] examined the shared travel behavior of university students and identified key factors influencing their adoption of carpooling, such as residence location and private vehicle availability. Similarly, AlQuhtani et al. [12] analyzed the viability of ridesharing in a suburban university context, finding that economic and ecological benefits could enhance acceptance when paired with incentive policies. In addition, Fehler et al. [13] proposed a framework to evaluate and compare different mobility modes on campus, revealing that ridesharing schemes can significantly reduce emissions and improve overall system efficiency when supported by multimodal planning. Moreover, the integration of shared autonomous vehicles (SAVs) was studied by Etminani-Ghasrodashti et al. [14], who found that their success depends heavily on seamless coordination with existing infrastructure and user expectations. These findings collectively suggest that shared mobility solutions, especially when tailored to university-specific needs, represent a technically viable and socially accepted alternative to conventional student transportation.
This study focuses on answering the following question: Is it feasible, from a technical, economic, and social perspective, to implement a shared transportation system for university students in congested urban environments such as Guayaquil? Evaluating the feasibility of shared transportation using a quantitative and descriptive approach, we applied surveys to a representative sample of 256 students to analyze their transportation habits. The sample was selected from a total population of 4123 students from morning and evening shifts, using stratified sampling. Routes were designed using ArcGIS software, while initial and recurring costs were calculated using Microsoft Excel. Social impact was also assessed through focus groups and analysis of demographic and socioeconomic data. Furthermore, travel time on public transport and the lack of accessible alternatives exacerbate the problem, limiting mobility options for many students [15]. Those who rely on urban transport face long and unsafe routes, which increases their vulnerability and reduces their quality of life [16]. The lack of an efficient and safe transport system creates additional barriers to accessing higher education, especially for low-income students [17].
Based on the mobility challenges faced by students at the Politecnica Salesiana University and the need for sustainable transportation alternatives, this study aims to address the following research questions:
  • What are the current mobility habits and challenges faced by students at Politecnica Salesiana University?
  • Is it technically and economically feasible to implement a shared transportation (carpooling) system in an academic environment?
  • What is the social acceptance and impact of a pilot carpooling system among university students?
  • Which mobile development frameworks offer the best performance and scalability for a university-oriented carpooling application?
To address these questions, the study begins by outlining the methodological approach used to assess the technical, economic, and social feasibility of a shared transportation system. It then presents the main findings, including survey results, pilot implementation outcomes, and cost analysis. This is followed by a discussion of the implications of these findings in light of existing literature on urban and university mobility. The article concludes with a summary of the key insights and recommendations for future research and practical application.

2. Materials and Methods

The shared transportation system proposal was defined as a solution to improve student mobility at the Politecnica Salesiana University (UPS), Guayaquil campus. The shared transportation system, also known as “carpooling”, aims to optimize student travel through the joint use of vehicles, thus reducing the number of cars in circulation, minimizing transfer times and decreasing traffic congestion. Therefore, in this type of research, quantitative research is included, used to collect numerical and statistical data on students’ transportation habits, associated costs and their willingness to participate in a shared transportation system. Likewise, descriptive research provides a detailed description of the current state of transportation at the university and the city of Guayaquil, identifying problems and opportunities to improve the transportation system through the implementation of shared transportation or also called carpooling [18].
Regarding the research methods, the deductive method is used to analyze the problem from the general to the specific, providing a complete view of mobility to the Politecnica Salesiana University (UPS). The analytical method focuses on breaking down the problem into its constituent parts to identify underlying causes and effects. In addition, a thorough theoretical review of relevant literature is carried out to collect information. Finally, the descriptive method is used to systematically collect and process data, guaranteeing a clear and precise synthesis of the findings. In Figure 1, the systematization of processes is presented to fulfill the research topic, detailing each stage necessary to achieve an effective implementation of the shared transportation system at the Politecnica Salesiana University, Guayaquil campus. This comprehensive approach ensures that all relevant aspects are considered and addressed efficiently.
Figure 1. Systematization of processes.

2.1. Study Area

The Politecnica Salesiana University, Guayaquil campus, Centenario campus, is selected as the specific site for conducting surveys and implementing the pilot plan within the framework of this research.

2.2. Population

To assess the demand for transportation on the Centenario campus of Politecnica Salesiana University, a representative sample of 4123 individuals from the morning and evening academic sections was selected, including exclusively students. This approach ensured a comprehensive and varied analysis of university transportation needs, facilitating a comprehensive understanding of the magnitude and scope of the challenge.

2.3. Sample Size

Determining the sample size involves providing data specific to the population under study. To achieve this, a specialized formula is used to calculate the appropriate sample size, as described in the research on sample calculation in education [19].
The parameters used for the sample size calculation in Equation (1) were established as follows: a 90% confidence level (Z = 1.65), a 5% margin of error (D = 0.05), and maximum variability (P = 0.5, Q = 0.5), which is a conservative assumption commonly used in social science research.
The sample size was determined using the calculation method that considers the following equation:
n   =   N · Z a 2 · P · Q D 2 · ( N − 1 ) + Z a 2 · P · Q
n:Sample size;
N: 4123Population size;
Za: 1.65Confidence (90%) Coefficient (1.65);
P: 0.5Probability of occurrence (50%);
Q: 0.5Probability of failure (1 − p);
D: 0.05Precision, maximum admissible error values between 1% and 9%.
Once the application of the specified equation was carried out, the following result was obtained:
n   =   4123 · 1.65 2 · 0.5 · 0.5 0.05 2 ( 4123 − 1 )   +   ( 1.65 2 · 0.5 · 0.5 )   =   256
It was established that the population to be surveyed corresponds to 256 people.

2.4. Stratified Sampling

As mentioned by Otzen [20], the stratum composes the target population to select and extract representative samples from each of them. Therefore, this type of sampling was used to determine the number of people to be surveyed in each section, as detailed in Table 1. To achieve this, the following calculation was performed:
n i   =   N i n N
where
  • ni = Sample size of each stratum;
  • Ni = Population of each stratum;
  • n = Sample size
  • N = Total sample size.
n M   =   1571 256 4123   =   98
n N   =   2552 256 4123   =   158
Table 1. Segmentation table by stratum.
The total sample of 256 students was selected using a simple random sampling method stratified by academic shift (morning and evening), based on the university’s official enrollment database. The only inclusion criterion was that participants were officially enrolled students at the Centenario campus during the semester in which the study was conducted. No filtering was applied based on academic level, faculty, or demographic characteristics in order to ensure an unbiased and representative sample of the general student population.
The calculation was performed by stratum to determine the number of students to be surveyed on each day of the class. This allows for an accurate and equitable representation of each group, ensuring that the results adequately reflect the characteristics and opinions of the students.
According to Bauce [21], the operationalization of variables is the process of converting abstract concepts into concrete and observable measures. It serves to clearly define what is being measured and how it is being measured, which facilitates the reproducibility of studies, increases objectivity in the interpretation of the results, and facilitates statistical analysis. Therefore, Table 2 details the variables that were identified according to each of the objectives.
Table 2. Operationalization of Variables.

2.5. Data Collection

2.5.1. Software and Tools Specification

In the execution of this study, several software tools were employed:
  • ArcGIS Desktop, version 10.8, developed by Esri Inc., Redlands, CA, USA, was used for route mapping and spatial analysis.
  • Microsoft Excel 2019, developed by Microsoft Corporation, Redmond, WA, USA, was used for cost estimation and data organization.
  • Minitab 21.3, developed by Minitab LLC, State College, PA, USA, was used for statistical analysis, including reliability testing (Cronbach’s alpha).
  • The pilot test utilized the web-based platform CarpoolWorld, developed by CarpoolWorld, Toronto, ON, Canada (https://www.carpoolworld.com).
  • For mobile application development, the evaluated framework was Flutter, version 3.16.4, developed by Google LLC, Mountain View, CA, USA.
No specialized physical equipment or measurement devices were used. All data were collected digitally using software tools and structured surveys, without the need for external hardware such as sensors or GPS devices.

2.5.2. Evaluation of the Technological Needs for the Implementation of the Shared Transportation System

For the development of a mobile ride-sharing application, it is crucial to choose frameworks that are robust, easy to use, and offer the necessary functionalities for an academic environment. The following in Table 3 are the three main frameworks to be evaluated:
Table 3. Frameworks to Be Developed.
Defining Evaluation Criteria
Defining evaluation criteria is conclusive because it provides an objective and structured basis for making informed decisions. These criteria allow identifying and prioritizing the specific needs of the project, ensuring that the selected framework is aligned with the technical, operational, and strategic requirements [25]. In addition, an evaluation based on clear criteria helps anticipate potential challenges, optimize resources, and maximize development performance and efficiency [26], the following criteria should be followed:
  • Compatibility: This criterion refers to the framework’s ability to develop apps that run efficiently on both iOS and Android from a single codebase. Cross-platform support ensures reaching the widest possible audience without duplicating development and maintenance efforts.
  • Performance and efficiency: This criterion evaluates how well the framework can create applications that run quickly and efficiently, optimally using device resources (memory, central process unit (CPU), battery). Good performance is crucial for a smooth and satisfying user experience, especially in applications that require real-time access to data and geolocation.
  • Ease of use and learning: This criterion analyzes how easy it is for developers to learn and use the framework. This includes the availability of documentation, learning resources, and the learning curve associated with the framework. A framework that is easy to learn and use can significantly reduce development time and facilitate long-term maintenance.
  • Access to native functionalities: This criterion focuses on the framework’s ability to access and use native mobile device functionalities such as Global Positioning System (GPS), camera, push notifications, sensors, and more. Efficient access to these functionalities is essential for ridesharing applications that rely heavily on geolocation and real-time interaction with the user’s device.
  • Stability and maintenance: This criterion evaluates how well the framework supports the growth of the application in terms of functionality and user base, and the ease with which the application can be maintained and updated over time. A scalable and maintainable framework ensures that the application can continually evolve and improve without facing significant performance or compatibility issues.

2.5.3. Cost Estimate for the Implementation of the Carpooling System

Identification of Project Modules
  • Developing a ride-sharing app requires a modular architecture to deliver a seamless, efficient, and secure user experience. Each module plays a crucial role in providing end-to-end services to users and administrators. The key modules are summarized below.
  • User profile: Facilitates the management of personal information, travel history and preferences, improving personalization and data management.
  • Trip booking: Allows selection of pickup points, scheduling of trips, choosing vehicles, and obtaining trip details, offering a convenient booking experience.
  • Real-time tracking: reports the location of the vehicle on a map in real time and allows communication between user and driver.
  • Payment and billing: Supports multiple payment methods, automatically calculates fees, and generates detailed invoices, ensuring transparent transactions.
  • Notifications: Keep users informed with real-time notifications about bookings, trip status, and promotions.
  • Rating and feedback: Allow users to rate their experience and leave comments to improve the quality of service.
  • Administration and support: offer a control panel to manage users, drivers, vehicles and reservations, as well as customer support.
  • Analytics and reporting: Provide tools to analyze usage data and generate reports for strategic decisions and efficient management.
  • Integrations: Support integration with external application programming interfaces (APIs) and services, facilitating expansion and adaptation to new technological functionalities.

2.5.4. Social Impact of the Implementation of the Transport System Shared

To obtain real data, a survey was applied, as shown in Appendix A.1, directed at 256 students from the Centenario campus of the Politecnica Salesiana University, Guayaquil headquarters.

2.5.5. Evaluation of the Convenience of Implementing a Pilot Plan

The assessment of the technical, economic, and social feasibility of implementing a shared transport pilot scheme was essential to ensure the success and sustainability of the project in the study area. In terms of technical feasibility, a detailed analysis of the existing road infrastructure, the availability of vehicles and drivers, as well as the operational capacity to efficiently manage shared transport was carried out. This analysis made it possible to ensure that the pilot scheme could be implemented in a practical and effective manner, minimizing potential obstacles and optimizing its operation.

3. Results

3.1. Technological Needs

To make a comparison between React Native, Flutter, and Ionic based on the five previously defined criteria, a score of one to five is used for each criterion. A score of one indicates that the framework minimally meets the criterion, while a score of five indicates that it meets it excellently, as shown in Table 4.
Table 4. Comparison of Development Frameworks.

Criterion Comparison Summary Between React Native Flutter and Ionic

The benchmarking conducted between React Native, Flutter, and Ionic frameworks revealed significant differences in terms of compatibility, performance, ease of use, access to native features, and scalability. Flutter emerged as the most robust framework, scoring the highest overall score of 24 points, particularly in performance and scalability, suggesting it was best suited for developing efficient and scalable applications across multiple platforms. React Native, with an overall score of 22, demonstrated solid performance across all categories, presenting itself as a competitive option for its access to native features and ease of use. Ionic, although scoring a lower overall score of 20, was considered viable for projects requiring rapid prototyping and a less steep learning curve, especially for applications that did not demand high performance, as shown in Table 5.
Table 5. Summary of the Comparison.

3.2. Project Cost

This process involves a detailed analysis of the resources required, the estimated development time, and the costs associated with the use of specific technologies and tools. Below is the cost of each module of the system, using the Flutter framework, which covers software development and infrastructure, for which the following items are considered, as shown in Table 6.
Table 6. Details of Mobile App Development Costs.

Operating Costs for the Operation of the Transport System Shared

To ensure that the mobile ride-hailing app works for 4123 users, it is crucial to consider the expenditures on technologies that ensure the performance, security, and scalability of the system. The following are the technological expenditures.
Cloud Infrastructure
  • Servers and Storage: USD 500/month;
  • Network Services (Content Delivery Network—CDN, Domain Name System—DNS): USD 200/month;
  • APIs and External Services: USD 4585/month;
  • Total Cloud Infrastructure: USD 5285/month.
Security and Backup
  • Security Services: USD 250/month;
  • Backup and Data Recovery Solutions: USD 200/month;
  • Total Security and Backup: USD 450/month.
Technological Monitoring and Maintenance
  • Performance Monitoring Tools: USD 250/month;
  • Maintenance and Technical Support Services: USD 400/month;
  • Total, Monitoring and Maintenance: USD 650/month.

3.3. Analysis of the Social Impact of the Implementation of the Shared Transport System

To determine the reliability of the survey, the Minitab program was used to calculate the reliability coefficient, also known as Cronbach’s alpha. The result obtained was a value of 0.9235 as observed in Equation (2), and in the same way, Table 7 details the statistics of elements that must be omitted to increase the value of Cronbach’s alpha. According to what Toro [27] mentioned, for the survey to be reliable, Cronbach’s alpha must have a value equal to or greater than 0.7.
Table 7. Statistics of Omitted Items.
Subsequently, a test route was established from the Politecnica Salesiana University to Durán canton and subsequently to Samborondón and different points within the canton. The travel time between these points was measured and the students’ satisfaction with the service was evaluated with a survey, as detailed in Appendix A.3.

Survey Results

The results of the surveys applied are detailed below.
1.
Indicate the university campus in Guayaquil that you attend.
A total of 100% of the students surveyed came from the Centenario campus. This sample exclusively reflects the opinion and situation of the students of this campus, ensuring that the results are representative of this specific population.
2.
Indicate your gender.
A total of 20% of respondents identified as women and 80% as men. These data highlight the gender distribution among survey participants, providing a clear perspective on the representation of each gender in the study and allowing for a more accurate analysis of the needs and opinions of each group.
3.
Have you heard about car sharing?
A total of 50.8% of respondents indicated that they do have knowledge or experience with car sharing, while 49.2% indicated that they do not.
4.
Would you prefer to share a vehicle with men, women, or both?
A total of 74.6% of the people surveyed expressed their willingness to share a vehicle with people of both genders, that is, with both men and women. In turn, 20.4% indicated that they would prefer to share a vehicle with men, women, or both. Some indicated that would only share with women, while a minority of 5% chose to share it only with men.
5.
Please indicate your UPS entry times.
The data collected in the survey showed that 39.45% of students enter classes at 6:00 P.M., which is the busiest time. On the other hand, 21.09% of students enter at 7:00 A.M., while only 3.91% do so at 2:00 P.M. These results reflect a clear preference for evening schedules, which could have significant implications for the organization and demand of services such as shared transportation, especially during the afternoon rush hours, as evidenced in Figure 2.
Figure 2. Check-in time.
6.
Please indicate your current academic level (please indicate the lowest level you are currently studying).
The highest student demand are the first, fifth, sixth and seventh, respectively. In contrast, the other semesters show significantly lower participation, with the ninth semester registering one of the lowest percentages, not exceeding 2%.
7.
Please indicate your academic schedule.
In the survey, 61.7% of the students surveyed belong to the evening section, while 38.3% belong to the morning section.
8.
Please indicate what range of your cell phone you have. A total of 34.2% of students reported owning a mid-range phone, while 30.8% used a mid-high range phone. In addition, 15.4% owned a high-end phone, 13.8% used a low–mid range device, and 5.8% owned a lowend phone. These data reflect a variety in the types of devices students use, which could influence their access to digital apps and services.
9.
What is the main type of internet access you use?
The main method of accessing the Internet used by students at the Salesian Polytechnic University is Wireless Fidelity (Wi-Fi). A total of 60.4% of students connect to the Internet through this network, while 39.6% use mobile data.
10.
During the study week, what type of transportation do you mainly use to get to your destination?
During the study week, 31.4% of students use the Metrovía means of transport, 30.3% use private vehicles, 18.4% travel by city bus, 10.3% use taxis, and 9.7% opt for other types of transport.
11.
What would be your main reason for choosing ridesharing over other forms of transportation?
Students indicated that 25.2% would be willing to use the shared transport system mainly for cost reasons, 25% would do so for convenience, 18.5% would value the reduction in travel time, 6.4% would favor the environmental impact, and 24.9% for safety reasons.
12.
Would you be willing to participate in a pilot plan for a shared transportation system?
Regarding willingness to participate in a pilot plan for a shared transportation system, 82.7% of students said they were willing to participate, while 17.3% indicated they would not be interested.

3.4. Survey with Students to Assess the Convenience of Applying the Pilot Plan

An survey was conducted with 11 randomly selected students using convenience sampling [20] considering their availability, interest in participating expressed in previous phases of the study, and geographical representation of the areas of influence (Guayaquil, Durán and Samborondón). This sample made it possible to explore the operational feasibility of the system under real conditions. This survey was conducted with the aim of designing the path to implement the pilot plan. The survey questions are found in Appendix A.2.
1.
Indicate your gender
Of those surveyed, 18.2% said they were female, while 81.8% said they were male.
2.
Have you had experience or knowledge about shared vehicles as sustainable mobility?
Of the people surveys, four mentioned that they have had some kind of experience with shared mobility, while seven said they had not, but that they are willing to participate in the pilot plan as a new experience.
3.
Could you tell me in which canton your home is located?
Of those surveys, three mentioned that they belong to Durán and Samborondón, while five indicated that they belong to the Guayaquil canton.

3.4.1. Proposed Route for the Shared Transport System

The project consisted of establishing a shared transportation route that connects the cantons of Guayaquil, Durán, and Samborondón. This route was designed with the aim of meeting the mobility demand of the students of the Polytechnica Salesiana University through the ArcGIS software, guaranteeing their safety and optimizing their travel time between these cantons.
For the implementation of the project, the Carpoolworld application was used as shown in Figure A1, through which students had to register. Once registered, they were assigned a vehicle that covered the aforementioned route. This shared mobility system not only facilitated the students’ travel, but also offered them a safe and efficient service, allowing them to save time on their daily trips, as shown in Figure A2.

3.4.2. Safety of Journey

Travel safety using a shared transportation system between students from the same university offers several significant advantages. First, familiarity between students contributes to a safer and more trustworthy environment, as travelers are likely to know each other or have mutual friends, fostering an environment of trust and collaboration. These measures not only increase safety, but also offer peace of mind to both students and their families [28].

3.4.3. Travel Time

Travel time for students using a shared transportation system has been identified to be significantly reduced when they reside near established routes [29]. According to the survey results, 14.3% of participants said they were satisfied with the reduction in travel time, while 85.7% said they were completely satisfied, the travel time show in Table 8.
Table 8. Travel Time.

3.4.4. Comfort

The people who participated in the pilot plan expressed their satisfaction with the new shared transport system, highlighting improvements compared to conventional means of transport (urban bus, metrovia). In contrast, the students expressed their dissatisfaction with traditional means of transport due to the uncomfortable seats and the poor condition of the vehicles, such as urban buses and metrovias.
According to a study by the World Health Organization (WHO), the quality of public transport can significantly affect users’ satisfaction and their perception of comfort [30]. The study highlights that poor vehicle condition and lack of comfort are key factors influencing users’ preference for more modern and efficient alternatives. This supports the need to improve the conditions of conventional public transport to better meet user expectations and reduce environmental impact.

3.4.5. Costs

Although the cost of shared transport is higher than that of conventional transport (urban bus, metrovia), they are considered more favorable due to improvements in safety and comfort. According to a study by Prieto [31], users of shared transport systems often value the greater safety and comfort they offer. The culture of online payments in Ecuador is evolving, driving the growth and development of shared transport, even if the cost is higher [32]. The study points out that the perception of safety and comfort in shared transport is a determining factor in the choice of means of transport, especially in academic communities where trust and knowledge among passengers is greater. This academic support underlines the importance of the quality of the service in the user’s preference, justifying the additional cost based on the perceived benefits.
Tariff Calculation
The base price, set by the driver, is static for each trip and is usually USD 0.75. In addition, for each kilometer of detour the driver makes to pick up or drop off a passenger, the fare increases by USD 0.40 per kilometer. Also, the distance traveled by the passenger with the driver generates an additional increase of USD 0.10 per kilometer. To calculate the rate to be charged, the following expenses were considered:
Base priceUSD 0.75
Driver detour for drop users off
per km		USD 0.40
Cost per kilometer routeUSD 0.10
Pricing is calculated on a personalized basis for each user, ensuring that everyone receives a fair rate. All these values were previously established, and the pilot plan was developed based on them. Therefore, a rate had to be calculated that would take into account all vehicle maintenance costs, as well as other additional costs. Below are the variables for the cost of travel for each of the users.
Table 9 reflects the estimated costs and profits for a trip involving different stops. The cost per detour, the distance traveled, the base price per passenger, and the total calculated for two passengers at each stop are considered. The stops include places such as the Naval Base, CC Riocentro, Duran-El Recreo, and Duran-Ciudadela Panorama, with distances ranging from 7.4 km to 17.1 km. The total cost per passenger varies by stop, with prices ranging from USD 2.10 to USD 2.86, and an estimated total profit of USD 12.81 for the entire trip. This information is useful for calculating the profitability of the trip by including variable costs based on distance and number of passengers.
Table 9. Variable for travel cost.
Table 10 shows a detailed analysis of the monthly costs associated with the use and maintenance of a vehicle, considering both fuel consumption and other operating factors. Fixed expenses such as cleaning and oil changes are highlighted, which together with air conditioning maintenance represent a cost of USD 60 per month. In addition, the monthly fuel cost, calculated based on a consumption of 10.7 L/100 km and an estimated monthly travel distance of 440 km, amounts to USD 35.2. These expenses add up to a monthly total of USD 65.2, which excludes costs such as insurance, licenses, fines or parking, which in this case are assumed to be nil or nonexistent.
Table 10. Usage and Maintenance Expenses.
Cost—Income Ratio
Net Profit =   Revenue   −   Cos ts =   U S D 2.81   −   U S D 3.26 = U S D 9.55 per day
The driver’s main objective is to reach their home, i.e., their final destination. However, during the journey from the university to other points, each additional stop represents extra income for the driver.

4. Discussion

The high Cronbach’s alpha value obtained (0.9235) indicates a high internal consistency of the questionnaire used in the study [33]. This suggests that the items included reliably measure the same underlying construct related to student perceptions about shared transportation systems.The results obtained in this study through the surveys applied to 256 students at the Centenario campus show high satisfaction with the shared transportation system compared to conventional methods. User satisfaction with public transport is closely related to the perception of comfort and safety [34]. Students highlighted the greater safety and comfort of shared transportation as they felt safer traveling with peers from the same university. This element increases confidence and may reduce anxiety, especially in areas with high perceived insecurity, which supports the hypothesis that these factors are determinants of their preference for this system.
The perception of safety is essential in the choice of means of transport, especially in community settings such as universities [35]. This aspect is fundamental in understanding why students prefer shared transportation, despite a possible increase in cost. Although the cost of shared transport is higher than that of conventional transport (urban bus, metrovias), respondents consider that the additional benefits justify the extra expense. Transport system users are willing to pay more for services that offer greater safety and comfort [35]. This finding is reflected in the responses of the students, who value the improved quality of travel and the reduction of stress associated with traditional public transport and the reduction in travel time.
Although one of the central objectives of this study is to contribute to the reduction in traffic congestion in urban university areas, it is important to clarify that the analysis involves different user comparisons depending on the evaluated dimension. Regarding perceptions of safety and comfort, the comparison was primarily made with regular public transport users who constituted a significant portion of the surveyed population and showed interest in alternatives that offer a greater sense of safety and comfort.
In contrast, to analyze the potential impact of the system on traffic congestion, the reference group was particular vehicle users, as they directly contribute to increased vehicle flow, especially when traveling with low occupancy. In this sense, the proposed carpooling system aims to engage both user profiles: those seeking an improved travel experience and those whose shift to shared mobility schemes could result in a reduction in the number of vehicles circulating in the urban environment.
The results obtained in this study through the surveys applied to 256 students on campus in Centenario demonstrate high satisfaction with the shared transportation system compared to traditional methods. Specifically, 85.7% of students who participated in the pilot plan reported being completely satisfied, and 14.3% indicated being satisfied with the reduction in travel time, highlighting a significant improvement in commuting efficiency. In addition, students emphasized greater comfort in shared transportation compared to conventional options such as Metrovía and city buses, which were frequently described as uncomfortable and poorly maintained. This perception is further supported by the fact that 31.4% of surveyed students primarily use Metrovía and 18.4% use city buses during the academic week. In this context, the shared transport system emerges as a viable and well-regarded alternative, offering improved travel conditions, enhanced safety and a more satisfying user experience.
In a previous research, the implementation of a transportation system was proposed, adjusted to the schedules of higher demand and with routes designed to benefit the community, especially those residents outside the city. This measure sought to improve the movement of the university community both inside and outside of Guayaquil [36]. Robert S. Kaplan [37] stresses the importance of understanding the relationship between costs and revenues to optimize profitability and improve organizational performance. In his work, Kaplan stresses that traditional cost systems are insufficient to reflect the complexity of modern operations. They propose the use of integrated costing systems, such as activity-based costing (ABC), which allow a more accurate allocation of costs to products or services. These findings align with the fact that 31.4% of surveyed students use the Metrovía and 18.4% rely on city buses as their primary means of transportation during the academic week, underscoring a clear demand for more efficient, secure, and comfortable alternatives.
These findings are consistent with the fact that 31.4% of surveyed students use Metrovía and 18.4% use city buses as their main means of transportation during the academic week, revealing a clear need for safer, more comfortable, and efficient alternatives within the university community.
However, despite the high levels of satisfaction reported by students participating in the pilot plan, several limitations of the shared transportation system must be acknowledged. One key challenge is the need for synchronization among users, as ride coordination depends on punctuality and mutual commitment, which may not always be feasible due to varying academic schedules and unforeseen delays. Furthermore, fixed routes although optimized for efficiency may not serve all geographic areas equitably, particularly for students residing in peripheral neighborhoods or in cantons with lower student density. Additionally, sustaining the system over time requires a stable pool of student drivers and continued interest in participation, which may fluctuate across semesters. Finally, the technical ease of use of the mobile platform, as noted by some pilot participants, remains an area for improvement to ensure broader adoption. These limitations highlight the need for iterative development, user feedback integration, and institutional support to enhance the system’s long-term viability.

5. Conclusions

According to the results obtained on the possible creation of the application, the React Native, Flutter, and Ionic frameworks were evaluated using a comparative scale. These frameworks were analyzed according to evaluation criteria that included compatibility, performance, efficiency, ease of use and learning, access to native functionalities, stability, and maintenance. Through this comparative scale, the features and advantages of each framework were verified, determining that Flutter obtained the highest score compared to its competitors, Ionic and React Native. For demographic analysis, 256 surveys were conducted, distributed between 98 in the morning and 158 in the evening. These surveys allowed us to know the degree of satisfaction of students with conventional transportation services and their perception of the implementation of a shared transportation system among UPS students. The results showed that students were willing to support the creation of a shared transportation application, especially valuing safety, comfort, and efficiency in travel times.
Based on the results of surveys to implement the pilot plan, a route was proposed with a starting point at UPS and a destination in Durán, Guayas, with three stops. Base Naval Norte, C.C. Riocentro, Durán—El Recreo, and Durán—Panorama. With the help of the carpooling application, user and driver profiles were created. The six participants showed satisfaction with the decrease in average travel time, improved comfort, and safety compared to the use of conventional transportation (express, buses, and subway). However, the students observed that the application used presented low ease of use and profile management. For participants whose stop was the Naval Base, the travel time ranged from 10 to 15 min. For those who arrived at the Rio Centro Shopping Center, the estimated time was 20 to 25 min. Those who went to El Recreo took between 30 and 35 min, while those who arrived at Panorama needed between 40 and 45 min to complete the trip.
Among the characteristics mentioned by the participants, the waiting time for transportation, the number of stops made, and the detours to drop off other students, especially on UPS-regulated express trains, were highlighted. These factors contribute to increased travel time, highlighting the feasibility of ridesharing as an effective solution. In order to optimize transportation routes, it was essential for students to adapt to the destinations set by the drivers. This adaptation helped to increase efficiency and reduce costs, as it was more economical for drivers to avoid significant detours or, ideally, not to make detours at all. These factors depend on the individual needs of each driver and the business model each driver wishes to implement through ridesharing. In our research, it was observed that, although detours of a few kilometers were made, they decreased the arrival time.
Despite the high acceptance by students, the administrative and maintenance costs are not significant, since the maintenance of the application during the first semester is minimal. Once the mobile application (APP) is developed, the only recurring costs will be monthly maintenance, necessary to familiarize users with the ridesharing system. It is recommended to create groups on platforms such as WhatsApp or Telegram to disseminate the application and capture the interest of students with vehicles, promoting the use of shared transportation and the planning of coordinated routes. These tools will facilitate organization and communication between users, optimize resources, reduce costs, and improve efficiency in travel management.
An exclusive parking lot could be designated for high occupancy vehicles (HOVs), which would promote more efficient and sustainable mobility by encouraging students to share their vehicles instead of traveling alone. This measure would not only reduce vehicle congestion but also, by offering preferential parking for HOVs or priority access in certain areas, it would encourage participation in these initiatives, promoting a culture of carpooling among students.
As a long-term sustainability strategy, it is proposed to establish joint partnerships between university authorities and city’s transit authorities to coordinate actions aimed at improving student mobility. This collaboration would allow route optimization, access to institutional and municipal benefits, and the promotion of carpooling policies to reduce traffic and environmental impact. A strategy would be the joint development of a dynamic mobility monitoring system using real-time traffic data provided by the transit authority, integrated with the university’s carpooling platform to adjust routes and schedules according to current conditions.
Future research should explore the scalability of the shared transportation system across other university campuses and urban areas with different demographic and geographic conditions. It would be particularly valuable to conduct longitudinal studies assessing the long-term adoption, behavioral changes, and environmental impact of carpooling solutions in academic environments. Additionally, the development and testing of a custom mobile application specifically designed for university use could improve user experience and engagement, addressing the limitations observed with third-party platforms. Further studies could also examine the role of incentives, gamification, or institutional policies in promoting sustained participation among students and drivers. Lastly, qualitative research focused on user perceptions and barriers to adoption would complement quantitative data, offering deeper insights into factors that influence mobility decisions in academic settings.

Author Contributions

Methodology, R.L.-C.; Formal analysis, G.M.-F.; Investigation, M.D.-M.; Writing—original draft, G.M.-F.; Supervision, M.E.-G. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Politecnica Salesiana university.

Institutional Review Board Statement

This study is waived for ethical review by the Institutional Review Ethics Committee of Politecnica Salesiana University, as the study poses no physical or psychological risks to participants, no sensitive issues were addressed, and no procedures were carried out that compromised the integrity of the participants.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Appendix A.1. Social Impact Survey of the Implementation of the Ride-Sharing System

  • Indicate the university campus in Guayaquil you attend:
    □
    María Auxiliadora
    □
    Centenario
  • Indicate your gender:
    □
    Male
    □
    Female
  • Are you aware of shared vehicle services?
    □
    Yes
    □
    No
  • Would you prefer to share a vehicle with men, women, or both?
    □
    Men
    □
    Women
    □
    Both
  • Please indicate your arrival time at UPS:
    □
    7:00 A.M.
    □
    9:00 A.M.
    □
    11:00 A.M.
    □
    2:00 P.M.
    □
    4:00 P.M.
    □
    6:00 P.M.
    □
    8:00 P.M.
  • Indicate your current academic level (please mark the lowest level you are currently taking):
    □
    1
    □
    2
    □
    3
    □
    4
    □
    5
    □
    6
    □
    7
    □
    8
    □
    9
    □
    10
  • Please indicate your academic shift:
    □
    Morning
    □
    Evening
  • Please indicate the range of your mobile phone:
    □
    High-end
    □
    Upper mid-range
    □
    Mid-range
    □
    Lower mid-range
    □
    Low-end
  • What is your primary type of internet access?
    □
    Wi-Fi
    □
    Mobile data
  • During the school week, what type of transportation do you mainly use to get to your destination?
    □
    Metrovía
    □
    Public bus
    □
    Taxi
    □
    Private vehicle
    □
    Others
  • What would be your main reason for choosing shared transportation over other modes?
    □
    Cost
    □
    Comfort
    □
    Reduced travel time
    □
    Environmental impact
    □
    Safety
  • Would you be willing to participate in a pilot plan for a shared transportation system?
    □
    Yes
    □
    No

Appendix A.2. The Survey

For students willing to participate in the pilot plan - What is your gender?
- Have you had experience or knowledge about car sharing as sustainable mobility?
- Could you tell me in which canton you live?

Appendix A.3. Satisfaction Survey to Pilot Test Participants

  • Indicate your level of satisfaction (with 5 being the highest and 1 the lowest):
    □
    1
    □
    2
    □
    3
    □
    4
    □
    5
  • Indicate the time saved compared to traditional transportation:
    □
    10–15 min
    □
    20–25 min
    □
    30–35 min
    □
    40–45 min
    □
    More than 1 h
  • Based on your experience, what do you think would be the most important improvement for an app designed for university students?
    □
    Ease of use
    □
    Safety
    □
    GPS-based vehicle location tracking
    □
    Display of occupied seats
    □
    Profile of fellow ride sharers

Appendix B. Photographic Records

Figure A1. APP demonstration to pilot test participants.
Figure A2. App designed.

References

  1. Ziad, T.; Verdezoto, A.; Felix, F.; Montes, C.; Beatriz, O.; Medina, R. Análisis del congestionamiento vehicular para el mejoramiento de vía principal en Guayaquil-Ecuador. Artículo de Investigación 2020, 21, 4–23. [Google Scholar] [CrossRef]
  2. Fernández, R.; Dextre, J. Elementos de la Teoría del Tráfico Vehicular; Pontificia Universidad Católica del Perú: San Miguel, Perú, 2022. [Google Scholar] [CrossRef] [Scilit]
  3. Alam, S.; Khan, A. The Impact Study of Vehicular Pollution on Environment. Int. J. Sci. Adv. Res. Technol. 2020, 6, 30–37. Available online: https://www.researchgate.net/publication/346946690 (accessed on 15 July 2024).
  4. Macioszek, E.; Jurdana, I. Bicycle Traffic in the Cities. Sci. J. Silesian Univ. Technol. Ser. Transp. 2022, 117, 115–127. [Google Scholar] [CrossRef] [Scilit]
  5. Peñafiel Espinoza, M.M.; López Chila, R.D. Estudio Sobre la Utilización y Efectividad del Comercio Electrónico (E-Commerce) y Propuesta para su Implementación en las PYMES del Sector Comercial de Guayaquil. Master’s Thesis, Repositorio Institucional de la Universidad Politécnica Salesiana, Guayaquil, Ecuador, 2012. Available online: http://dspace.ups.edu.ec/handle/123456789/3168 (accessed on 17 May 2024).
  6. Ortiz Tirado, P.X.; Herrán Gómez, J.; Garcés Velásquez, L.F.; Granda Merchán, J.S.; Altamirano Chingay, K.G.; Guarderas Albuja, M.D.L.P.; Ruiz, M.; Carofilis, C.; Chela, J.A.S.; Medranda, N.; et al. La Salesiana Frente a la Movilización Popular de Octubre de 2019: Memoria de una Acción Solidaria; Editorial Universitaria Abya-Yala: Quito, Ecuador, 2020; Available online: http://dspace.ups.edu.ec/handle/123456789/20082 (accessed on 20 July 2024).
  7. Muñoz, R.; Suquinagua, S.; Mejía Regalado, I. Análisis y solución al congestionamiento vehicular producido en la intersección Av. 24 de Mayo y Max Uhle de la ciudad de Cuenca. Bachelor’s Thesis, Universidad Politécnica Salesiana, Cuenca, Ecuador, 2023. Available online: http://dspace.ups.edu.ec/handle/123456789/25708 (accessed on 10 March 2024).
  8. Quinapanta, I. Estrés y Rendimiento Académico en Estudiantes Universitarios de la Ciudad de Ambato. 2024. Available online: https://repositorio.uti.edu.ec/handle/123456789/6583 (accessed on 10 August 2024).
  9. Chen, X.; Wu, S.; Shi, C.; Huang, Y.; Yang, Y.; Ke, R.; Zhao, J. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. IEEE Sens. J. 2020, 20, 14317–14328. [Google Scholar] [CrossRef] [Scilit]
  10. Chen, S.; Piao, L.; Zang, X.; Luo, Q.; Li, J.; Yang, J.; Rong, J. Analyzing differences of highway lane-changing behavior using vehicle trajectory data. Phys. A Stat. Mech. Appl. 2023, 624, 128980. [Google Scholar] [CrossRef] [Scilit]
  11. Etminani-Ghasrodashti, R.; Hladik, G.; Kermanshachi, S.; Rosenberger, J.M.; Khan, M.A.; Foss, A. Exploring shared travel behavior of university students. Transp. Plan. Technol. 2023, 46, 22–44. [Google Scholar] [CrossRef] [Scilit]
  12. AlQuhtani, S. Ridesharing as a Potential Sustainable Transportation Alternative in Suburban Universities: The Case of Najran University, Saudi Arabia. Sustainability 2022, 14, 4392. [Google Scholar] [CrossRef] [Scilit]
  13. Fehler, H.; Pruckner, M.; Schmidt, M. Mobility to Campus—A framework to evaluate and compare different mobility modes. arXiv 2025, arXiv:2506.13574. [Google Scholar]
  14. Etminani-Ghasrodashti, R.; Patel, R.K.; Kermanshachi, S.; Rosenberger, J.M.; Weinreich, D. Integration of shared autonomous vehicles (SAVs) into existing transportation services: A focus group study. Transp. Res. Interdiscip. Perspect. 2021, 12, 100481. [Google Scholar] [CrossRef] [Scilit]
  15. Flores, E.; Mora, E.; Chica, J.; Balseca, M. Evaluación de la movilidad de estudiantes y accesibilidad espacial a centros de educación en zonas periurbanas. Novasinergia 2022, 5, 128–149. [Google Scholar] [CrossRef] [Scilit]
  16. Aliaga, W. Congestión Vehicular de Transporte Urbano y su Incidencia en Salud y Medio Ambiente en la Ciudad de Puno; Universidad Andina Néstor Cáceres Velásquez: Juliaca, Peru, 2019; Available online: https://revistas.uancv.edu.pe/index.php/RCIA/article/view/746 (accessed on 15 July 2024).
  17. Gallardo, E. Análisis general del acceso al sistema de educación superior del Ecuador 2009–2018. Mag. Cienc. Rev. Investig. Innov. 2023, 8, 21–37. [Google Scholar] [CrossRef] [Scilit]
  18. Carpoolworld. Available online: https://www.carpoolworld.com/create_my_group.html?t=University (accessed on 28 July 2024).
  19. García, J.; Reding, A.; López, J. Cálculo del tamaño de la muestra en investigación en educación médica. Investig. Educ. Méd. 2013, 2, 217–224. [Google Scholar] [CrossRef] [Scilit]
  20. Otzen, T.; Manterola, C. Técnicas de muestreo sobre una población a estudio. Int. J. Morphol. 2017, 35, 227–232. [Google Scholar] [CrossRef] [Scilit]
  21. Bauce, G.; Córdova, M.; Avila, A. Operacionalización de variables. Rev. Inst. Nac. Hig. Rafael Rangel 2018, 49, 43. Available online: http://saber.ucv.ve/ojs/index.php/rev_inhrr/article/view/18686 (accessed on 20 July 2024).
  22. React Native. Get a Head Start with a Framework. Available online: https://reactnative.dev/ (accessed on 25 May 2024).
  23. Flutter. Build for Any Screen. Available online: https://flutter.dev/ (accessed on 25 May 2024).
  24. Ionic. The Mobile SDK for the Web. Available online: https://ionicframework.com/ (accessed on 25 May 2024).
  25. Espinosa, R. Análisis comparativo para la evaluación de frameworks usados en el desarrollo de aplicaciones web. CEDAMAZ 2021, 11, 133–141. [Google Scholar] [CrossRef] [Scilit]
  26. García, R.; Zurita, H. Comparativa de Frameworks para el Desarrollo web en el Lado del Cliente Basado en Métricas de Desempeño Web Vitals. Bachelor’s Thesis, Escuela Politécnica del Ejército (ESPE), Sangolqui, Ecuador, 2023. Available online: https://repositorio.espe.edu.ec/items/517edc46-8441-4da7-b406-4d37d44b47ab (accessed on 25 May 2024).
  27. Toro, R.; Peña, M.; Avendaño, B.; Mejía, S.; Bernal, A. Análisis empírico del coeficiente alfa de Cronbach según opciones de respuesta, muestra y observaciones atípicas. Rev. Iberoam. Diagn. Eval. Psicol. 2022, 63, 17–30. [Google Scholar] [CrossRef] [Scilit]
  28. García Pérez, R. El carpooling o viaje en coche compartido. Especial referencia a BlaBlaCar. CIRIEC-España Rev. Juríd. Econ. Soc. Coop. 2020, 36, 477–483. [Google Scholar] [CrossRef] [Scilit]
  29. Gkiotsalitis, K.; Nikolopoulou, A. The multi-vehicle dial-a-ride problem with interchange and perceived passenger travel times. Transp. Res. Part C Emerg. Technol. 2023, 156, 104353. [Google Scholar] [CrossRef] [Scilit]
  30. OPS/OMS. Defensa del Transporte Público Seguro y Saludable: Fomento de la Participación del Sector Sanitario en un Marco Multisectorial; Organización Panamericana de la Salud: Washington, DC, USA, 2019; Available online: https://iris.paho.org/handle/10665.2/28274? (accessed on 20 January 2024).
  31. Prieto, R.; Vassallo, J.; Gil, S. Transporte Sostenible en Argentina: Costos e Impactos Ambientales de los Distintos Combustibles. 2021. Available online: https://www.researchgate.net/publication/381478976 (accessed on 20 January 2024).
  32. López, R.; Andrade, A. E-Commerce, Rival o Aliado para las Comercializadoras Textiles de Guayaquil; Editorial Universitaria Abya-Yala: Guayaquil, Ecuador, 2020; pp. 146–176. [Google Scholar]
  33. Cronbach, L.J. Coeficiente alfa y la estructura interna de pruebas. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Scilit]
  34. Eboli, L.; Mazzulla, G. A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transp. Policy 2011, 18, 172–181. [Google Scholar] [CrossRef] [Scilit]
  35. Litman, T.A.; Litman, T. Evaluating Public Transit Benefits and Costs; Victoria Transport Policy Institute: Victoria, BC, Canada, 2024; Available online: https://www.vtpi.org (accessed on 19 August 2024).
  36. López-Chila, R.; Caiza Medina, K.; Zurita Peñaloza, R. Methodology to manage mobility factors and optimize university transportation in the Ecuadorian context. Estrateg. Gestión Univ. 2025, 13, e8734. [Google Scholar] [CrossRef]
  37. Van Hoek, R. Review of: ‘Cost & Effect: Using integrated cost systems to drive profitability and performance’. Int. J. Logist. Res. Appl. 1998, 1, 315–317. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Spatial Patterns of and Temporal Variations in Carbon Storage in the Forest and Grassland Ecosystem of China’s Nature Reserves
Previous
The Impact of the Mobility Package on the Development of Sustainability in Logistics Companies: The Case of Lithuania
Next