Search Results (143)

Generation Z, known for its digital fluency and distinct consumer behaviors, is an increasingly influential demographic in the tourism industry. As a sustainability-focused generation, their preferences and behaviors are shaping the future of travel. This study explores the tourism experiences of Romanian Generation Z members, focusing on their travel patterns, motivations, information sources, and service preferences. A bibliometric analysis of the existing literature was conducted to identify research trends and gaps in understanding Generation Z’s tourism behaviors. Using a mixed-method approach, the study integrates survey data from 399 respondents with AI-generated insights from ChatGPT 4o mini to compare traditional research methods with AI-driven analysis. It examines how AI interprets and predicts travel behaviors, highlighting the reliability and biases inherent in AI models. Key discrepancies between the two methods were found: The survey indicated a preference for car travel and commercial accommodation, while AI predictions favored air travel and private accommodation. Additionally, AI emphasized a growing interest in eco-friendly transportation and connections to natural and cultural environments, offering a broader scope than the survey alone. Both methods revealed a trend toward digital platforms for travel planning, moving away from traditional agencies. The findings suggest that AI can complement traditional research by providing actionable insights, though its limitations emphasize the need for a balanced integration of both methods. This study offers new perspectives on Generation Z’s tourism experiences. Full article

This study aims to determine the effects of the variables within the UTAUT 2 model on the intention to use a Fully Electric Car. In this context, data were collected through survey forms from 401 white-collar workers who are considered to have a higher economic status. Initially, validity and reliability analyses were conducted on the scales used in the Smart PLS program, and subsequently, the hypotheses were interpreted using the results obtained from structural equation modeling. In this study, it was found that effort expectancy, social influence, perceived ease of use, hedonic motivation, and habit had a positive and significant impact on the intention to use electric vehicles. Performance expectancy has a negative and significant effect on the intention to use electric cars, while price has no significant effect. It was determined that the intention to use electric vehicles was found to mediate the relationship between perceived ease of use and actual usage behavior. This research can offer significant contributions to literature, particularly by examining the influence of habit on behavioral intention and the effect of hedonic motivation on electric vehicle usage intention. By testing the UTAUT 2 model in the context of electric vehicle acceptance, this study supports the universality and applicability of the model to various technologies. Emphasizing the role of variables such as hedonic motivation and habit in electric vehicle acceptance adds a new dimension to the UTAUT 2 model. Thus, it makes an important contribution to technology acceptance research. Full article

Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large-scale regional simulators are designed and used for research and applications. Calibration is a key issue in the traffic simulation: it finds the optimal system pattern to decrease the gap between the simulator output and the real data, making the system much more reliable. This paper proposes DRBO, a calibration framework for large-scale traffic simulators. This framework combines the travel behavior adjustment with black box optimization, better exploring the structure of the regional scale mobility. The motivation of the framework is based on the decomposition of the regional scale mobility dynamic. We decompose the mobility dynamic into the car-following dynamic and the routing dynamic. The prior dynamic imitates how vehicles propagate as time flows while the latter one reveals how vehicles choose their route according to their own information. Based on the decomposition, the DRBO framework uses iterative algorithms to find the best dynamic combinations. It utilizes the Bayesian optimization and day-to-day routing update to separately calibrate the dynamic, then combine them sequentially in an iterative way. Compared to the prior arts, the DRBO framework is efficient for capturing multiple perspectives of traffic conditions. We further tested our simulator on SFCTA demand to further validate the speed distribution from our simulation and observed data. Full article

The deployment of large installed power capacities from intermittent renewable energy sources requires balancing to ensure the steady and safe operation of the electrical grid. New methods of energy storage are essential to store excess electrical power when energy is not needed and later use it during high-demand periods, both in the short and long term. Power-to-Gas (P2G) is an energy storage solution that uses electric power produced from renewables to generate gas fuels, such as hydrogen, which can be stored for later use. Hydrogen produced in this manner can be utilized in energy storage systems and in transportation as fuel for cars, trams, trains, or buses. Currently, most hydrogen is produced from fossil fuels. Solid-oxide electrolysis (SOE) offers a method to produce clean hydrogen without harmful emissions, being the most efficient of all electrolysis methods. The objective of this work is to determine the optimal operational parameters of an SOE system, such as lower heating value (LHV)-based efficiency and total input power, based on calculations from a mathematical model. The results are provided for three different operating temperature levels and four different steam utilization ratios. The introductory chapter outlines the motivation and background of this work. The second chapter explains the basics of electrolysis and describes its different types. The third chapter focuses on solid-oxide electrolysis and electrolyzer systems. The fourth chapter details the methodology, including the mathematical formulations and software used for simulations. The fifth chapter presents the results of the calculations with conclusions. The final chapter summarizes this work. Full article

Longitudinal motion control is a critical aspect of autonomous vehicle area, requiring a well-designed controller to ensure optimal system performance, safety, and comfort under varying driving conditions. Previous control methods often face challenges such as chattering effects, and model uncertainties. To address such challenges, in this paper, we propose the application of an optimized super-twisting sliding mode control (OST-SMC) for the longitudinal motion control of autonomous vehicles. The motivation is to enhance the robustness and efficiency of the control system while minimizing the chattering problem. The proposed system’s mathematical modeling and control design are presented in detail with stability analyzed using Lyapunov theory. To enhance the controller’s performance, uncertain parameters are optimized using the gradient descent method, a linear regression-based technique. The OST-SMC algorithm shows enhanced robustness against disturbances and parameter uncertainties compared to conventional sliding mode controllers. Simulations in MATLAB/Simulink and CarMaker validate the proposed method, demonstrating strong performance even on downhill roads. The OST-SMC reduces chattering more effectively than traditional SMCs, achieving smooth tracking and consistent robustness under varying road conditions. Full article

Grocery shopping is an integral part of everyday life in every household. Due to the increasing decline in the number of grocery stores, it is difficult to find grocery shops close to home, especially in rural areas. For certain population groups, such as older people and/or people with limited mobility, people living alone and single parents, as well as households without a car, it is difficult to get groceries within walking distance. In addition, the gaps in local supply also mean a decline in the quality of life of the affected population. This study addresses the question of what role small rural grocery stores play in the shopping behavior of residents of rural areas and how they rate them. Using a quantitative consumer survey (n = 238), the shopping behavior and relationship to five sites in the Main-Spessart region of Bavaria, Germany were analyzed. The surveyed customers visit the rural stores several times a week (57.1%), especially for necessities (62.2%) and weekly shopping (13.1%). The product range (including fresh products), proximity to the place of residence, as well as the social function are most valued. Four different customer types were identified: the Uninvolved (35.6%), the Supporters (15.5%), the Motivated (25.8%) and the Socials (23.2%). The study shows that small rural grocery stores can contribute significantly to the food supply in poorly supplied areas. The degree of use varies depending on the individual life circumstances and needs. Using the location as a place for social exchange is a very relevant factor (60.5%). The targeted use of the store as a social meeting place is highly dependent on the additional infrastructure provided (e.g., a café corner). These results can help decision-makers to gain a better understanding of the users and consequently to better assess potentials of small rural grocery stores. Full article

(1) Background: Metro is an important part of urban transportation, carrying huge passenger volume every day. With improvements in people’s living standards, passengers’ demand for a comfortable Metro experience is increasing. In the context of urban development, maintaining a good thermal comfort level of Metro cars is not only conducive to providing a comfortable and healthy environment for passengers, but also has great significance for reducing energy consumption and sustainable urban transportation development. This study provides empirical evidence for Metro design and operation strategies, aiming at creating a safer and more comfortable passenger experience. (2) Methods: By combining passengers’ comfort perception (cognitive value of thermal environment) and rideability perception (confidence in thermal comfort control), this study established a correlation model between thermal comfort and passenger unsafe behavior, namely the integration of SOR (Stimulus-Organism-Response) and TAM (Technology Acceptance Model). This study used methods such as field surveys, structural equation modeling, and reliability and validity analyses to investigate the impact of Metro thermal comfort on passenger behavior safety. (3) Results: This study found that the Metro thermal environment, including temperature, humidity, and airflow velocity, significantly affects passengers’ comfort perception and behavior choices. (4) Conclusions: Passengers may exhibit avoidance behavior in uncomfortable thermal environments, leading to uneven distribution of people in the train car and increasing safety risks. Improving Metro thermal environments can effectively enhance passengers’ perceived comfort and reduce unsafe behavior motivation, which is of great significance for safe Metro operations. Full article

CO₂ emissions from gas-powered cars have a large impact on global warming. The aim of this paper is to develop an accurate estimation method of CO₂ emissions from individual private passenger cars by using actual driving data obtained by telematics. CO₂ emissions from gas-powered cars vary depending on various factors such as car models and driving behavior. The developed approach uses actual monthly driving data from telematics and vehicle features based on drag force. Machine learning based on random forest regression enables better estimation performance of CO₂ emissions compared to conventional multiple linear regression. CO₂ emissions from individual private passenger cars in 24 car models are estimated by the machine learning model based on random forest regression using data from telematics, and the coefficient of determination for all 24 car models is ${\mathrm{R}}^2=0.981$. The estimation performance for interpolation and extrapolation of car models is also evaluated, and it keeps enough estimation accuracy with slight performance degradation. The case study with actual telematics data is conducted to analyze the relationship between driving behavior and monthly CO₂ emissions in similar driving record conditions. The result shows the possibility of reducing CO₂ emissions by eco-driving. The accurate estimation of the reduced amount of CO₂ estimated by the machine learning model enables valuing it as carbon credits to motivate the eco-driving of individual drivers. Full article

The heavy reliance on private cars is linked not only to harmful environmental impacts, such as gas emissions and global warming, but also to other issues like traffic congestion, road infrastructure maintenance, and the limited availability of parking spaces—significant concerns in many cities. To address these challenges, carpooling has been shown to be an effective solution, as it directly reduces emissions, alleviates congestion, and mitigates the environmental effects of transportation.The aim of this research is to enhance the understanding of carpooling in our society. To achieve this, a carpooling initiative among teachers for their commuting journeys was developed and implemented, followed by an evaluation of the system by a panel of experts. The results showed a 31.9% reduction in the number of cars on the road among participants, with a total of 109,080 km saved based on the reduced number of vehicles.The primary motivation for participants to adopt carpooling was fuel savings, while the reduction of physical fatigue from driving was identified as the second most important factor. Although some barriers to participation were identified, their impact was generally lower than that of the perceived benefits. These findings suggest that carpooling programs should focus on optimizing matching conditions and addressing individual concerns to promote wider adoption. Full article

The ubiquitous use of mobile devices along with the amount of traffic, transportation services, and travel pattern data available has led to the emergence and deployment of smartphone applications for providing information about personal travel management. Several of these travel apps are aimed at voluntary travel behavior change (VTBC) to support and increase sustainable mobility, and have led to the development of research to investigate their influence on travel behavior. Here, the aim is to study the role of technophilia and goal-framing theory in the intention to adopt and situationally use a prospective VTBC travel app. A Structural Equation Model is developed with the aim of empirically testing a sample of 971 respondents collected in two suburban corridors in the Lisbon Metropolitan Area. The results support that goal-framing theory is important for explaining the adoption of VTBC travel apps. Gain and normative motives are more relevant than hedonic motives, pointing to the importance of their tangible benefits. Frequent car users may benefit from VTBC travel apps in terms of encouraging behavioral changes, supporting sustainable mobility management solutions. The results also outline the importance of technophilia and the current use of travel apps in influencing the intention to use VTBC apps. Full article

The popularity of electric vehicles offers the opportunity to analyze decision-making processes by examining user behavior. Determining the motivation of the user to use an innovation will guide decision-makers in supporting the innovation in question. This study investigates the factors electric car users in Türkiye consider based on the Technology Acceptance Model. A questionnaire was used to measure Perceived Ease of Use, Perceived Usefulness, and Intention to Use with the external factors of Subjective Norm, Compatibility, and Image. The relationships were analyzed with PLS-SEM established with the participation of 414 electric vehicle users. Subjective Norms and Image directly impact Perceived Usefulness, Perceived Ease of Use, and Intention to Use. It has been determined that Compatibility has a direct effect on Ease of Use and an indirect effect on Usefulness and Intention. According to this study, in which most people are dissatisfied with charging and range issues, the opinion of the social environment and family is the most important external factor affecting intention. Our findings suggest improving the charging station network and technology, as well as implementing informative activities related to the features of electric vehicles, in order to contribute to users’ adoption of electric vehicles. Full article

As conflict dynamics become complex and escalate globally, especially identity-based conflicts, we are witnessing an unprecedented shift in the Conflict Analysis and Resolution and Peacebuilding field toward contextually innovative and effective community-led approaches. The inadequacies of liberal and neoliberal paradigms and the increase in identity-based conflicts, religious pluralism, and differences in communities have motivated evidence-based inter-religious community-level engagements over the past two decades. These interventions rely on the theoretical frameworks of emancipatory peacebuilding and compassionate reasoning, and reflect an in-depth sense of spirituality, longing, and the essence of human relationship building and practice. This study gathers data from primary sources (which include findings from hybrid interviews) through a semi-participatory and empirical qualitative explorative research process in order to critique the underlying philosophies of traditional paradigms and explore emerging alternatives. It also posits that inter-religious community-led interventions are founded on the emancipatory elicitive religious peacebuilding (EERPb) framework. They are adaptive to non-linear (and sometimes non-scientific) approaches and are less focused on international standards. The framework fundamentally embraces phenomenological, metaphysical, and ethical realities in peacebuilding, operationalizes the concept of just peace, and acknowledges a global approach to peace that offers the opportunity to resolve the difficulties encountered by the various CAR and peacebuilding theoretical schools. Full article

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article

Since in the automotive field colors play an important role, the present study tried to answer the following questions: is the perceived quality (PQ) of the vehicle interior color different after visually exploring the car body color? If so, how? Here, exploiting immersive virtual reality simulations and eye-tracking technology, participants were asked to visually explore an unbranded car in different exterior/interior color combinations and rate its PQ. Fixation duration (time eyes are fixed on a target) was considered as an implicit measure of visual attention allocation while PQ evaluations were considered as explicit measures of individual preferences for car colors. As for eye-tracking data, the results showed that white and red car exteriors affected the attention to interiors with the fixation duration being longer for gray than black interiors. Moreover, the subjective evaluations of car PQ predicted eye-tracking patterns: as the negative evaluation increased, the fixation duration on car interiors also increased. Overall, these preliminary results suggested the need to further explore the relationship between PQ and attentional/motivational processing as well as the role of subjective aesthetic preferences for color combinations in the automotive field. Full article

Falls are considered a significant cause of disability, pain, and premature deaths in older adults, often due to sedentary lifestyles and various risk factors. Combining immersive virtual reality (IVR) with physical exercise, or exergames, enhances motivation and personalizes training, effectively preventing falls by improving strength and balance in older people. IVR technology may increase the ecological validity of the assessments. The main goal of our study was to assess the feasibility of using a KAVE-based VR platform combining simulations of Levadas and a cable car to perform a balanced assessment and profiling of the older adult population for high risk of falls and the related user experience. A VR-based platform using a Wii balance board and a CAVE was developed to assess balance and physical fitness. Validated by the Biodex Balance System (BBS), 25 older adults participated in this study. The usability and presence were measured through the System Usability Scale and ITC-SOPI questionnaires, respectively. The IVR system showed a high presence and a good usability score of 75. Significant effects were found in the maximum excursion of the centre of pressure (COP) on the anterior–posterior axis during the cable car simulation (CCS), correlating with BBS metrics. Multiple discriminative analysis models and the support vector machine classified fall risk with moderate to high accuracy, precision, and recall. The system accurately identified all high-risk participants using the leave-one-out method. This study suggests that an IVR-based platform based on simulations with high ecological validity can be used to assess physical fitness and identify individuals at a higher risk of falls. Full article