Search Results (138)

Analyses of student mobility are typically conducted in an urban environment and are informed by socio-demographic or trip attributes. The prevailing focus is on individual modes of transport, different groups of commuters travelling to campus, students’ behavioural perceptions, and the totality of student trips. This paper starts with the identification of the determinants of student mobility that have received insufficient research attention. Utilising surveys, the study captures the mobility patterns of a sample of 1014 students and calculates their carbon footprint (CF; in kg/academic year) to assess whether the factors neglected in previous studies influence differences in the actual environmental load of student commuting. A regression analysis is employed to ascertain the significance of these factors as predictors of sustainable student mobility. This study exclusively focuses on the group of student commuters to campus and analyses the trips associated with compulsory activities at a suburban campus that is distant from the university centre and student facilities, which changes the mobility context in terms of commuting options. The under-researched factors identified in this research have not yet been quantified as CF. The findings confirm that only some of the factors neglected in previous research are statistically significant predictors of the local environmental load of student mobility. Specifically, variables such as student employment, frequency of class attendance, and propensity for ride-sharing could be utilised to forecast and regulate students’ mobility towards more sustainable patterns. However, all of the under-researched factors (including household size, region of origin (i.e., past experiences), residing at term-time accommodation while studying, and the availability of a family car) have an influence on the differences in CF magnitude in the studied campus. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. Full article

Destination Management Organizations (DMO) increasingly harness social media to foster favorable online destination brand experiences (ODBEs) during travelers’ pre-trip planning. However, empirical knowledge about such experiences in ecotourism contexts remains limited. This study addresses the gap by proposing and validating an ODBE measurement scale adapted to ecotourism destinations. An online questionnaire was administered to Facebook users following seven certified Romanian ecotourism destinations, yielding 281 valid responses. Through exploratory factor analysis and confirmatory composite analysis, the scale was refined into three components—hedonic, utilitarian, and spatio-temporal—capturing emotional immersion, rational evaluation, and destination-specific spatial perceptions. Structural equation modeling further demonstrated that ODBEs exert a strong, positive effect on two key behavioral intentions: visiting the destination (β = 0.913) and sharing destination information online (β = 0.875). This study advances theories on tech-mediated pre-travel experiences by emphasizing nature and local culture. The findings provide DMOs with practical guidance for creating effective social media content to enhance destination branding and support sustainable tourism. Full article

Given the importance of youth mental health for public policy, it is crucial to involve young people directly in participatory research to investigate their views and translate their demands into concrete actions. The aim of the study was to define the concept of mental wellbeing as perceived by a group of adolescent and young adult patients in two large hospitals in northwestern Italy and to find out, together with them, what institutions can do to promote youth wellbeing. Thirty-nine participants (13–25 years old, 90% female), divided into four groups, took part in a Photovoice workshop. Individual interviews were conducted with 21 participants and the transcripts were thematically analyzed. The photos were categorized into five themes: nature, traveling, passions and leisure, relationships, and animals. The definition of mental wellbeing can be traced back to eight themes: sharing experiences and emotions with others, calm and tranquility, personal fulfilment, sense of belonging, pleasant physical sensations, freedom and discovery, involvement and commitment, and happiness. The key recommendations for promoting mental wellbeing were providing information about mental health, tackling the stigma of mental illness, and providing psychological support in school and health services. The implications of these findings for policy makers involved in planning health services for young people are discussed. Full article

This study investigates factors influencing Chinese travelers’ behavior in sharing travel experiences on social media, using the frameworks of Perceived Value Theory, the Theory of Reasoned Action, and Social Influence Theory. This study aims to explore the intrinsic motivations and social factors that drive individuals to engage in sharing travel experiences and examine how these factors, along with personal characteristics, influence this behavior. Data from 489 participants were collected using a structured survey method and indicate that convenience value, emotional value, attitude, subjective norm, social identity, and group norm significantly affect sharing behavior, while monetary and social values do not. Additionally, personality traits such as openness, agreeableness, and conscientiousness moderate the relationship between these values and the sharing behavior. This study contributes to the literature by providing a deeper understanding of the motivations underlying travel experience-sharing on social media in China and by examining how both intrinsic motivations and social influences affect this behavior. The findings offer practical implications for tourism marketers to prioritize seamless digital platforms, emotionally engaging experiences, and personalized campaigns. Governments can support these efforts by promoting policies that enhance platform convenience and foster social engagement. Focusing on Chinese travelers, this research also provides a cross-cultural perspective, deepening the theoretical understanding of travel experience-sharing. Full article

Travel mode identification (TMI) plays a crucial role in intelligent transportation systems by accurately identifying travel modes from Global Positioning System (GPS) trajectory data. Given that trajectory data inherently exhibit spatial and kinematic patterns that complement each other, recent TMI methods generally combine these characteristics through image-based projections or direct concatenation. However, such approaches achieve only shallow fusion of these two types of features and cannot effectively align them into a shared latent space. To overcome this limitation, we introduce multi-view contrastive fusion (MVCF)-TMI, a novel TMI framework that enhances identification accuracy and model generalizability by aligning spatial and kinematic views through multi-view contrastive learning. Our framework employs multi-view learning to separately extract spatial and kinematic features, followed by an inter-view contrastive loss to optimize feature alignment in a shared subspace. This approach enables cross-view semantic understanding and better captures complementary information across different trajectory representations. Extensive experiments show that MVCF-TMI outperforms baseline methods, achieving 86.45% accuracy on the GeoLife dataset. The model also demonstrates strong generalization by transferring knowledge from pretraining on the large-scale GeoLife dataset to the smaller SHL dataset. Full article

In the field of multi-robot cooperative localization and task planning, traditional filtering algorithms encounter synchronization and consistency issues during multi-source data fusion. These challenges result in cumulative localization errors and inefficient information sharing, which limits the system’s collaborative capabilities and control accuracy. To overcome these limitations, a distributed cooperative navigation strategy is introduced. Initially, a Distributed Adaptive Extended Kalman Filter (DAEKF) is implemented, which adaptively adjusts the noise covariance matrix to effectively manage nonlinearities and multi-source noise conditions. Subsequently, a Distributed Model Predictive Control (DMPC) framework is introduced. This framework predicts and optimizes each robot’s kinematic model, thereby improving the system’s collaborative operations and dynamic decision-making capabilities. Finally, the efficacy of this strategy is confirmed through detailed simulations and robotic experiments. The simulation results for cooperative localization demonstrate that DAEKF outperforms Kalman Filter (KF) and Extended Kalman Filter (EKF) in terms of localization accuracy. In the straight-line path-tracking experiments, DAEKF effectively reduced both lateral and heading errors for both robots. For Robot 1, DAEKF reduced the lateral error Root Mean Squared Error (RMSE) by 68.87%, 27.80%, and 25.76%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 52.29%, 41.89%, and 36.47%. For Robot 2, DAEKF reduced the lateral error RMSE by 51.30%, 22.88%, and 11.60%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 39.55%, 37.15%, and 26.00%. In the curved path-tracking experiments, both robots demonstrated high trajectory conformity while traveling along a predefined path combining straight-line and circular arc segments, with lateral errors in the straight-line segments all below 0.05 m. The strategy proposed in this study significantly enhanced the precision and stability of multi-robot collaborative navigation, demonstrating strong practicality and scalability. Full article

Autonomous vehicles (AVs) and shared mobility constitute two of the “Three Revolutions” that portend major changes to surface transportation. AVs promise to reduce accidents, expand accessibility, and decrease congestion, while shared mobility provides the benefits of automotive transportation without requiring the purchase of a vehicle or the ability to drive it. Despite great promise to alleviate the negative externalities imposed by transportation, there remains much to be understood about the combined diffusion and impact of AVs and shared mobility. There is little demonstrated experience and application of AVs to the public, and how and where people would use automated shared mobility relative to their current travel is largely unknown. This study advances our understanding by utilizing an intercept survey of 232 respondents in Ann Arbor, Michigan who had made a discretionary trip to one of two central and two suburban locations. The novel approach of using intercept surveys allows us to gather more valid data about the willingness of respondents to replace the mode they just used for either a privately owned or a shared AV and do so for the trip purpose most conducive to using such a vehicle. We incorporate descriptive and spatial analyses and then utilize multinomial logit models to predict the factors influencing the encouragement or discouragement of substituting a private and a shared AV for their previous trip. We found that active mobility and transit trips work in competition with private AVs, while youth encourages interest. Meanwhile, active mobility, increasing age, and one of our measures of density discourage interest, while female respondents and the same measure of density increase interest. The results suggest that future efforts to facilitate the adoption of shared AVs target areas of the city that are relatively dense and residents in these areas where a shared AV would enhance individuals’ mobility. Full article

The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. Managing agents and fostering collaboration in a multi-agent reinforcement learning scenario remains a challenging task. This paper introduces a cooperative multi-agent federated reinforcement learning algorithm named FLDQN to address the challenge of agent cooperation by solving travel time minimization challenges in dynamic multi-agent reinforcement learning (MARL) scenarios. FLDQN leverages federated learning to facilitate collaboration and knowledge sharing among intelligent agents, optimizing vehicle routing and reducing congestion in dynamic traffic environments. Using the SUMO simulator, multiple agents equipped with deep Q-learning models interact with their local environments, share model updates via a federated server, and collectively enhance their policies using unique local observations while benefiting from the collective experiences of other agents. Experimental evaluations demonstrate that FLDQN achieves a significant average reduction of over 34.6% in travel time compared to non-cooperative methods while simultaneously lowering the computational overhead through distributed learning. FLDQN underscores the vital impact of agent cooperation and provides an innovative solution for enabling agent cooperation in a multi-agent environment. Full article

The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness. Full article

In urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of depot sharing among enterprises. By enabling depots belonging to different enterprises to be shared, it would shorten the distance traveled by vehicles returning to depots and reduce carbon emissions. And it would also reduce the number of depots being built. Therefore, a green vehicle routing problem with depot sharing is presented in the paper. To solve this problem, an improved adaptive large neighborhood search algorithm is presented, in which the Split strategy and two new operators are proposed to enhance solution quality and computational efficiency. Extensive numerical experiments are conducted on instances of varying scales to evaluate this algorithm, and also demonstrate its effectiveness and efficiency. Furthermore, the experimental results demonstrate that depot sharing significantly reduces carbon emissions, achieving an average optimization rate of 10.1% across all instances compared to returning to the original depot. Full article

Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station infrastructure. To fully leverage the congestion reduction potential of ETC, this paper addresses the problem of ETC lane allocation at toll stations under heterogeneous traffic flows, modeling it as a mixed-integer nonlinear bilevel programming problem (MINLBP). The objective is to minimize total toll station travel time by optimizing the number of ETC lanes at station entrances and exits while considering ETC-HVs’ lane selection behavior based on the user equilibrium principle. As both upper-level and lower-level problems are convex, the bilevel problem is transformed into an equivalent single-level optimization using the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem, and numerical solutions are obtained using the commercial solver Gurobi. Based on surveillance video data from the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China, numerical experiments were conducted. The results illustrate that the proposed method reduces total vehicle travel time by 90.44% compared to the current lane allocation scheme or the proportional lane allocation method. Increasing the proportion of CAVs or ETC-HVs helps accommodate high traffic demand. Dynamically adjusting lane allocation in response to variations in traffic arrival rates is proven to be a more effective supply strategy than static allocation. Moreover, regarding the interesting conclusion that all ETC-HVs choose the ETC lanes, we derived the relaxed analytical solution of MINLBP using a parameter iteration method. The analytical solution confirmed the validity of the numerical experiment results. The findings of this study can effectively and conveniently guide lane allocation at highway toll stations to improve traffic efficiency. Full article

With the advancement of digital technologies and the gradual waning of the pandemic’s impact, digital nomads have attracted significant attention from academia and industry as an emerging social group. Existing studies regarding the distribution and destination selection preferences of digital nomads remain lacking. This study aims to investigate the distribution patterns and destination selection preferences of Chinese digital nomads, guided by the lifestyle migration theory. Specifically, we address the following research questions: Where do Chinese digital nomads choose to sojourn post-pandemic, and what factors influence their destination preferences? Using a qualitative approach based on grounded theory, we analyze text data from China’s largest video-based social platform to uncover key factors shaping digital nomads’ choices. The findings reveal that Chinese digital nomads consider five key dimensions when selecting a destination: basic living conditions, social environment, work opportunities, travel experience, and local integrated environment. Notably, compared to traditional migrant groups, digital nomads demonstrate stronger online and sharing behaviors in work and social interactions. Additionally, “travel” is identified as a new category. This study makes a theoretical contribution by expanding the application of lifestyle migration theory to digital nomadism, offering new insights into contemporary migration behaviors. It also provides practical guidance for travel destination planning and management to better accommodate the preferences of this emerging group. Full article

This pilot study evaluated the design, usability, and practicality of the dPDT@home kit for treating actinic keratoses (AKs) on the face and scalp. The kit allowed patients to manage their treatment at home, reducing hospital visits and utilizing natural sunlight. While patients were very willing to use the kit again, further studies are required to evaluate outcomes and ascertain the need for additional improvements and support. Background/Objectives: Daylight photodynamic therapy (dPDT) is an established effective therapy for superficial mild-to-moderate actinic keratoses (AKs) on the face and scalp. In this project, we redesigned the delivery of dPDT using design principles and the concept of Realistic Medicine to create the dPDT@home kit. This user-friendly and environmentally conscious kit allows patients to manage their AKs at home, reducing the need for hospital visits and ensuring timely treatment to coincide with appropriate weather conditions and to prevent disease progression due to delays in diagnosis and treatment. The initial pilot phase of the study was to evaluate the usability and convenience of the practicalities of the dPDT@home kit. Methods: Patients were instructed to conduct two dPDT@home kit treatments approximately three weeks apart on suitable weather days. After a follow-up telephone consultation from the specialist PDT nurse following the first treatment, patients then completed an initial questionnaire (Questionnaire 1, Q1) to share their experience. A second questionnaire (Q2) was completed 3–6 months after their final treatment to assess treatment outcomes. Results: A total of 16 patients with AK on the face and/or scalp used the dPDT@home kit. Five patients formed an initial pilot group in 2020/21, whose feedback and involvement informed the final product for the larger group of eleven patients (2021/22). All patients reported no issues with receiving the kit or the pro-drug used in the treatment (Q1). Q2 had an 81.25% return rate, with an average willingness score of 8.9/10 to use dPDT@home again. However, patients expressed doubts about their confidence in the treatment’s efficacy, giving an average score of 6.9/10, with preferences leaning towards other treatments, such as hospital-based PDT or cryotherapy. Conclusions: The pilot deployment of the dPDT@home kit identified suitable patients and highlighted the need for comprehensive training and support for both patients and clinicians to deliver dPDT through this novel approach. The kit can reduce the number of hospital visits, but patients still require supervision, which can be provided remotely. The questionnaire outcomes emphasize the importance of setting patient expectations and taking a holistic approach to managing chronic field-change AK. Additionally, the kit’s recyclable components and reliance on natural sunlight promote sustainability and reduce patient travel. Further evaluation is required to determine cost-efficacy, safety, and the potential place of the dPDT@home kit in the therapeutic management of patients with this common and challenging condition. Full article

Background/Objectives: The scientific literature has provided evidence on the negative health effects experienced by those who suffer gender-based violence (GBV) and isolating gender violence (IGV), the latter being a form of retaliation against those who support GBV victims. However, less attention has been paid to the potential health improvements following the initial support received by victims. Methods: This study examines the positive health outcomes among survivors of GBV and IGV after they engaged with the “MeToo route,” an initiative of the MeToo movement aimed at raising awareness about gender violence and fostering solidarity through support networks that traveled through 13 Spanish universities through more than 20 events in September 2022. Results: Using communicative methodology, survivors shared how their health, previously harmed by their experiences of violence, improved as a result of the support provided after knowing the MeToo support network. Conclusions: The findings highlight the crucial role of solidarity networks in alleviating the health impacts of GBV and IGV and underscore the importance of effective support systems for recovery. Full article