Search Results (276)

Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior. Full article

In the era of digital intelligence, digital media landscapes increasingly influence cultural tourism consumption. Consumerism capitalizes on tourists’ superficial aesthetic commonalities, constructing a homogenized media imagination that leads to collective convergence in travel decisions, which obscures aspects of local culture, poses safety risks, and results in fleeting local tourism booms. In this study, semistructured interviews were conducted with 36 tourists, and NVivo12.0 was used for three-level node coding in a qualitative analysis to explore the digital media attributions of conformist travel behavior. The findings indicate that digital media landscapes exert a “digital persuasion” effect by reconstructing self-experience models, directing the individual gaze, and projecting idealized self-images. These mechanisms drive tourists to follow digital traffic trends and engage in imitative behaviors, ultimately shaping the phenomenon of “group roaming”, grounded in the psychological effect of herd behavior. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

Participation in horse-related activities frequently involves relocating horses from the home stable to an activity venue, which might require local, regional, or international travel. In these circumstances, horses are exposed to unfamiliar surroundings and experience changes to their daily routines, which could have negative welfare impacts. An online survey was conducted in 2021 to ask experienced horse sector participants about the horse management practices that they perceived worked well and provided for positive horse welfare when undertaken at venues. Qualitative analysis identified four themes: ‘managing venues’, ‘monitoring fitness to participate’, ‘maintaining a healthy equine digestive system’, and ‘using horse behaviors to inform decision-making’. The findings indicate that activity-related individuals selected practices that assisted horses to adapt to venue surroundings, remain calm, and stay healthy. The co-authors propose that experienced participants recognize that practices include both provisions (inputs) and outcomes (the horse’s subjective experiences), resonating with the Five Freedoms and Five Domains models. For horse activity organizations proposing to implement the Five Domains model, the findings indicate that reviewing practices and implementing updates is timely and achievable. The authors propose that continuously updating practices will contribute to safeguarding horses and maintaining the sector’s social license to operate. Full article

Virtual fencing (VF) offers a promising alternative to conventional or electrified fences for managing livestock grazing distribution. This study evaluated the behavioral responses of 25 Rarámuri Criollo cows fitted with Nofence^® collars in Pine Valley, CA, USA. The VF system was deployed in chaparral rangeland pastures. The study included a 14-day training phase followed by an 18-day testing phase. The collar-recorded variables, including audio warnings and electric pulses, animal movement, and daily typical behavior patterns of cows classified into a High or Low virtual fence response group, were compared using repeated-measure analyses with mixed models. During training, High-response cows (i.e., resistant responders) received more audio warnings and electric pulses, while Low-response cows (i.e., active responders) had fewer audio warnings and electric pulses, explored smaller areas, and exhibited lower mobility. Despite these differences, both groups showed a time-dependent decrease in the pulse-to-warning ratio, indicating increased reliance on audio cues and reduced need for electrical stimulation to achieve similar containment rates. In the testing phase, both groups maintained high containment with minimal reinforcement. The study found that Rarámuri Criollo cows can effectively adapt to virtual fencing technology, achieving over 99% containment rate while displaying typical diurnal patterns for grazing, resting, or traveling behavior. These findings support the technical feasibility of using virtual fencing in chaparral rangelands and underscore the importance of accounting for individual behavioral variability in behavior-based containment systems. Full article

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

Pastoral activities are an essential part of the cultural and ecological landscape of Central Italy. This traditional practice supports local economies, maintains biodiversity, and contributes to the sustainable use of natural resources. Understanding livestock behavior in response to environmental variability is essential for improving grazing management and animal welfare and ensuring the sustainability of these systems. This study evaluated the movement patterns of sheep grazing on pastures with differing vegetation indices in the Sibillini Mountains. Twenty lactating ewes foraging on two different pastures were monitored from June to October 2023 using GPS collars and accelerometers. GPS tracks were segmented using the Expectation Maximization Binary Clustering (EmBC) method to characterize movement behaviors, such as foraging, traveling, and resting. The NDVI was used to characterize vegetation dynamics, showing notable differences between the two pastures and across the grazing season. Additive mixed models were used to analyze data, accounting for individual variability and temporal autocorrelation in the sample. The results suggest that variations in the NDVI influence grazing behavior, with sheep in areas of lower vegetation density exhibiting increased movement during foraging. These findings provide valuable insights for optimizing grazing practices and promoting sustainable land use. Full article

Role-playing games (RPGs) set in ancient architecture have emerged as a digital tool for enhancing engagement with ancient architectural cultural heritage. This study examines how RPG elements (immersion, narrative, cognitive engagement) influence sustainable tourism outcomes at ancient architectural heritage sites and develops a stimulus–organism–response (SOR)-based framework model to explore their affective and behavioral effects. The results demonstrate that immersion, narrative, and cognitive engagement in RPGs significantly enhance tourists’ affective engagement. Affective engagement, in turn, enhances tourists’ willingness to travel to and support for heritage conservation sites. Mediation analyses indicated that affective engagement partially mediated the effects of immersion and narrative on the willingness to travel and fully mediated the effects of cognitive engagement. Affective engagement positively predicted support for heritage preservation, whereas willingness to travel alone did not exhibit this relationship. Emotional engagement is therefore a critical mechanism by which digital role-playing game experiences drive sustainable tourism behaviors, resulting in outcomes that go beyond individual behaviors to include broader sustainability impacts. By fostering immersive, narrative-rich, and engaging cognitive experiences, RPGs set in ancient architecture can stimulate willingness to visit heritage sites and encourage conservation awareness, providing valuable insights into sustainable tourism and the management of ancient architectural heritage. Full article

Commuting plays a critical role in shaping sustainable transport systems, yet understanding the diverse preferences of commuter groups remains a challenge for policymakers. As cities aim to promote sustainable transport, it is essential to better understand the factors influencing travel behaviors. This study investigates the commuting preferences and behaviors of urban employees in Italy, focusing on identifying distinct user profiles and their implications for policy development. Using a dataset of 2301 participants from Italian cities, the research analyzed transport mode choices, willingness to adopt sustainable transport options, and perceptions of public transport (PT) services, including factors such as travel time, proximity to PT stops, cost, and comfort, rated on a four-point Likert scale. K-modes clustering was employed to segment participants into three clusters based on their travel behaviors. The results revealed three distinct user profiles: (1) car-dependent users with negative perceptions of PT, driven by family obligations and dissatisfaction with PT services; (2) individuals who primarily use cars but are somewhat open to improvements in PT; (3) individuals willing to adopt alternative mobility options, including active and shared transport modes. Significant differences were found across clusters in terms of mode choices, willingness to use sustainable transport, and satisfaction with PT services. Notably, employees showed limited interest in alternative sustainable transport modes such as e-scooters and walking, with 73% and 66% of participants expressing little or no interest, respectively. Despite incentives such as company subsidies for purchasing bicycles or e-scooters, 58% of employees remained uninterested in adopting these alternatives. Additionally, employees’ perceptions of PT services revealed dissatisfaction with factors such as travel time, comfort, and punctuality, with over 70% rating these aspects as “Poor” or “Fair”. These findings suggest that improving the quality of PT services, particularly in terms of travel time, punctuality, comfort, and cost, should be a priority for enhancing user satisfaction. This research provides valuable insights for policymakers seeking to reduce car dependence and promote sustainable urban transport planning. Full article

Food insecurity is not only shaped by behavioral, socioeconomic, and demographic factors but is also determined by an individual’s ability to access food in their community. Analyzing new survey data from a large city located in the southwest USA, this research adds to current dialogues on food insecurity among older adults and individuals with disabilities in economically disadvantaged communities. Using logistic regression, the findings provide nuanced evidence to distinguish between two crucial determinants of food insecurity related to transportation access—the lack of service availability and transportation unaffordability. One-third of respondents missed grocery trips due to a lack of affordable transportation. For individuals who cannot drive or do not own vehicles, access to ride services is critical to overcome exacerbated risks owing to food insecurity compared with those who own personal vehicles. Those relying on community-based ride services are more likely to miss grocery trips due to inadequate services. Our research further provides evidence that inadequate services result in greater food insecurity for specific vulnerable subgroups, such as those with poor health, renters, and those with lower incomes. Our findings highlight the importance of understanding behavioral travel constraints and call for equity-focused improvements in transportation systems to mitigate food access barriers. Full article

Background: Low back pain (LBP) and musculoskeletal disorders are highly prevalent among agricultural workers. However, there is limited epidemiological evidence from rural regions of Ecuador, where working and living conditions may differ substantially from those in other settings. This study aimed to identify predictors of LBP among farmers in rural Ecuador to inform locally relevant prevention strategies. Methods: Participants aged 30 to 60 years (n = 103) were recruited through a traveling health clinic. Participants were assessed with behavioral and sociodemographic self-report questionnaires and anthropometric measurements. Low back pain (LBP) was assessed using the Standardized Nordic Musculoskeletal Questionnaire, which asked about symptoms experienced in the past 12 months. Bivariate (Chi-square and Fisher exact tests) and multivariate (binary logistic regression) analyses were conducted to explore associations between risk factors and LBP in individuals aged 30 to 60 years. Results: LBP was highly prevalent, affecting 78.6% of participants. Behavioral patterns were mixed, with low rates of smoking and moderate alcohol and coffee consumption associated with LBP. A normal body mass index (BMI) was observed in 66% of the sample, and over half reported stable mood and good self-perceived health. In the binary logistic regression analysis, only education level significantly predicted LBP, with secondary education acting as a protective factor. Conclusions: While lower back pain was widespread in the population studied, most risk factors that were analyzed were not significantly associated with its presence. Full article

Psychological theory often reminds us that the best predictor of an individual’s future behavior is their prior behavior. Then, the pandemic happened in 2020, and at least for travel behavior and tourism consumption, everything seemed to change, stressing the importance of re-evaluating predictors. In the present study, we aimed to compare the history of travel behavior and tourism consumption with the predicted travel behavior of students coming from Generation Z, along with intrapersonal characteristics influencing risk perception, like psychological resilience and perceived invulnerability. The findings revealed that the pandemic changed the attitude towards travel for tourism-related purposes in both positive and negative directions, restructuring the attitude towards travel for the majority and also revealing many new prospective travelers. Psychological resilience was a significant variable that differentiated the respondents who changed their attitude towards tourism from those who remained consistent in their non-travel behavior and students who planned to travel more during the pandemic. Although subsamples also differed in the mean value of perceived invulnerability, the variable did not prove statistically significant. Almost 50% of the students predicting that they will travel abroad for non-tourism-related purposes in the future year were students who had not traveled abroad before the pandemic. Full article

Migration is a diverse behavior exhibited by a wide array of organisms. Variability in the type of movements is rooted in their purpose, environmental factors, demographics, and individual physiological condition. The ability of caribou (Rangifer tarandus granti) to efficiently move long distances and have a high degree of plasticity in their movements allows them to respond and be resilient to dynamic and dramatic differences in environmental conditions. I used 88 collared, sympatric, adult, female, barren-ground Nelchina Caribou Herd caribou in east-central Alaska to assess their migratory strategy (as indexed by the distance between winter and summer ranges) and how this might affect their movements. Employing 41,682 movement vectors from 39 of these individuals equipped with GPS collars, I compared the annual and monthly movements of caribou that were found on different winter ranges. Distances between winter and summer ranges for individual caribou were correlated with their annual movement, but not for caribou that wintered within the same area. As expected, caribou with the greatest distance between their winter and summer ranges (300 km) traveled the most annually (2132 km/year), whereas caribou with the shortest distance between ranges (71 km) traveled the least annually (1368 km/year). However, caribou that migrated the furthest exhibited greater movement rates in all non-migratory summer months and most non-migratory winter months, as well as during migration. Movement rates were the greatest in summer, peaking in July, regardless of where caribou wintered. During the winter months, movement rates were similar among caribou found on different winter ranges and decreased over the winter, reaching minimums in January-March. Caribou that migrated the shortest distance and had lower movement rates tended to be found in smaller groups in summer. The connection between group size and movement rates may be a function of competition or a small-scale example of the larger-scale phenomenon of range expansion of large herds. Environmental factors, such as snow depth and temperature, were also correlated (negatively and positively, respectively) with caribou movement rates. Survival was not significantly different for caribou utilizing different winter ranges, which implies that the benefits of this long-distance migration can be offset by its costs. A more detailed understanding of the drivers and variability of caribou movement should help improve the management of this declining species. Full article

Traffic emissions resulting from vehicle travel across origin–destination (OD) pairs pose significant challenges to sustainable urban development, necessitating a systematic understanding of emission patterns to inform effective mitigation policies. Existing studies often focus on the locations where emissions occur, overlooking emission flows between OD pairs, which could lead to incomplete policy formulation. This study proposes a new emission pattern analysis framework. Specifically, we construct the Urban Traffic Emission Flow Network (UTEFN) based on comprehensive individual vehicle data, and then systematically analyze its spatiotemporal characteristics and network structure by using complex network theory. Our findings show that the node weighted degree captures emissions attributable to specific nodes, revealing that critical emission sources may be overlooked in traditional analyses. Edge weights identify high-emission OD edges and associated travel behaviors. Furthermore, the emission distributions for different vehicle types and gases exhibit heavy-tailed scaling laws, indicating that emission reduction policies targeting a few key nodes or edges could impact a notable proportion of traffic emissions. In structural analysis, community detection revealed distinct clusters of emission flows, with the formation of high-emission communities associated with specific spatial configurations and travel behaviors. The findings provide valuable insights into strategic planning for clean traffic systems and urban emission reduction. Full article