Search Results (683)

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

With the advancement of intelligent transportation systems, vehicle trajectory data have become a key component in areas like traffic flow prediction, route planning, and traffic management. However, high-quality, publicly available trajectory datasets are scarce due to concerns over privacy, copyright, and data collection costs. The lack of data creates challenges for training machine learning models and optimizing algorithms. To address this, we propose a new method for generating synthetic vehicle trajectory data, leveraging traffic flow characteristics and road maps. The approach begins by estimating hourly traffic volumes, then it uses the Poisson distribution modeling to assign departure times to synthetic trajectories. Origin and destination (OD) distributions are determined by analyzing historical data, allowing for the assignment of OD pairs to each synthetic trajectory. Path planning is then applied using a road map to generate a travel route. Finally, trajectory points, including positions and timestamps, are calculated based on road segment lengths and recommended speeds, with noise added to enhance realism. This method offers flexibility to incorporate additional information based on specific application needs, providing valuable opportunities for machine learning in intelligent transportation systems. Full article

Purpose—This research paper examines the critical factors in customer satisfaction while purchasing fruits and vegetables at farmers’ markets. Design/methodology/approach—This study was conducted using a prepared questionnaire to collect data on a random sample of 235 customers of farmers’ markets in the state of Maharashtra, India. The research was carried out in the year 2023. Seven hypotheses were tested concerning the relationships between the variables of interest. The variables of convenience, variety, quality, price, health and hygiene, and service conditions were used as independent constructs and were proxied by reflective indicators. Customer satisfaction and customer loyalty were treated as an exogenous variable and an endogenous variable, respectively. Structural equation modeling was used to investigate the model relationships and confirm the theoretical model. Findings—The findings validate all the reflective indicators used in the study. The latent variables of convenience, variety, quality, price, health and hygiene, and service conditions positively and significantly affect customer satisfaction, and customer satisfaction positively and significantly affects customer loyalty toward farmers’ markets. The structural equation explains approximately 55% of the variation in customer satisfaction related to convenience, variety, price, quality, health and hygiene, and service conditions. Significance—The study results provide insights into the factors that influence consumer behavior and attitudes toward farmers’ markets. By identifying these predictors, this study can help farmers’ markets and other stakeholders develop effective marketing strategies to attract and retain customers, ultimately promoting sustainable food production and consumption. Additionally, the results can inform policymakers on how to support and promote farmers’ markets as healthy and sustainable food sources. Practical implication—By implementing the practical suggestions derived from the implications of this research, farmers’ markets can optimize customer satisfaction, boost customer loyalty, and reinforce their position as valuable contributors to local communities’ well-being and sustainability. Originality/value—The acceptance of farmers’ markets in India as an alternative shopping destination for fruits and vegetables is gradually increasing. This exploratory study conducted on farmers’ markets examined several factors, including price, in assessing customer satisfaction and farmers’ markets’ effectiveness at positioning themselves as shopping destinations for consumers in India. Full article

This study proposes a balanced truck dispatching strategy for inter-terminal transportation (ITT) in large ports, incorporating proactive demand outsourcing to address stochastic and imbalanced ITT demand. A portion of ITT tasks are intentionally outsourced to third-party public trucks at a higher cost, so that self-owned trucks can be reserved for more critical tasks. The ITT system is modeled as a closed Jackson network, in which self-owned trucks circulate among terminals and routes. An optimization model is developed to determine the optimal proactive outsourcing ratios for origin–destination terminal pairs and the appropriate fleet size of self-owned trucks, aiming to minimize total transportation costs. Reactive outsourcing is also included to handle occasional truck shortages. A mean value analysis method is used to evaluate system performance with given decisions, and a differential evolution algorithm is employed for optimization. The case study of Shanghai Yangshan Port demonstrates that the proposed strategy reduces total system cost by 9.8% compared to reactive outsourcing. The results also highlight the importance of jointly optimizing outsourcing decisions and fleet size. This study provides theoretical insights and practical guidance for ITT system management under demand uncertainty. Full article

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation. Full article

Carbon transfer embodies the spatial redistribution of carbon emissions resulting from interregional economic activities and trade. In recent years, accelerated regional integration and deepening specialization within industrial chains have rendered traditional bilateral analytical frameworks inadequate for capturing the complexity of interregional carbon transfer networks. This evolving context necessitates the incorporation of spatial interaction effects to elucidate the multi-nodal and multi-pathway characteristics inherent in contemporary carbon transfer patterns. Based on the spatial interaction theoretical framework and a multiregional input–output (MRIO) model, we analyze the spatial dependence characteristics of interregional carbon transfer in China. The results reveal that interregional carbon transfer in China exhibited an upward trend from 2012 to 2017, demonstrating statistically significant positive origin dependence, destination dependence, and network dependence. The distance between regions exerts a significantly negative influence on interregional carbon transfer. Interregional carbon transfer is not merely a bilateral phenomenon; its fundamental nature is characterized as a network phenomenon. Our study demonstrates that precise regulation of the allocation of industrial land and transportation infrastructure land, strengthening the decisive role of market mechanisms in resource allocation for regional low-carbon development, and establishing interregional collaboration mechanisms for low-carbon exchange can effectively reduce the occurrence of interregional carbon transfer. These findings provide policymakers with more precise information to achieve equitable carbon emissions distribution across regions. Full article

The concept ‘mobility of care’ captures all the daily travel necessary for the upkeep of a household, including trips to grocery stores, health-related appointments, errands, and caring activities for dependents. Since it was originally coined in 2009, a handful of studies have shown how poorly mobility of care trips are captured in transportation surveys. These preliminary analyses also find that care trips comprise a substantial proportion of daily mobility. As women disproportionately engage in ‘mobility of care’ travel, the under-consideration of care trips is argued to result in a gender bias in transport planning. Despite this, transport policy related to mobility of care has received less attention. Given that transport policy shapes how transport systems operate, this paper explores the extent to which recent transport policies consider mobility of care. A critical policy review framework is used to systematically examine seven policy documents (435 pages) from the Toronto Transit Commission (TTC), the largest transit agency in Canada. Results indicate that mobility of care is rarely directly considered or significantly discussed. Instead, transport policy often uses the commute to work as the default trip. Mentions of care destinations and trip characteristics associated with mobility of care are more common in recent years and most frequently discussed in relation to the COVID-19 pandemic or specialized services for seniors and people with disabilities. Policies that likely facilitate mobility of care indirectly are also identified, including fare discounts, transfer windows, and accessibility policies. The review concludes with preliminary recommendations on how transit agencies can more directly plan for mobility of care. Full article

Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded correlations. Notably, improved visual walkability in specific zones reduces reliance on ride-hailing by facilitating sustainable alternatives. These findings provide empirical support for optimizing urban infrastructure and land-use policies to promote equitable mobility systems. The proposed methodology offers a replicable framework for assessing transportation–land-use interactions, informing targeted interventions to achieve metropolitan sustainability goals through coordinated spatial planning and pedestrian-centric design. Full article

Tourist destination classification is pivotal for enhancing the travel experience, supporting cultural heritage preservation, and enabling smart tourism services. With recent advancements in artificial intelligence, deep learning-based systems have significantly improved the accuracy and efficiency of landmark recognition. To address the limitations of existing datasets, we developed the Samarkand dataset, containing diverse images of historical landmarks captured under varying environmental conditions. Additionally, we created enhanced image variants by squaring pixel values greater than 225 to emphasize high-intensity architectural features, improving the model’s ability to recognize subtle visual patterns. Using these datasets, we trained two parallel YOLO11 models on original and enhanced images, respectively. Each model was independently trained and validated, preserving only the best-performing epoch for final inference. We then ensembled the models by averaging the model outputs from the best checkpoints to leverage their complementary strengths. Our proposed approach outperforms conventional single-model baselines, achieving an accuracy of 99.07%, precision of 99.15%, recall of 99.21%, and F1-score of 99.14%, particularly excelling in challenging scenarios involving poor lighting or occlusions. The model’s robustness and high performance underscore its practical value for smart tourism systems. Future work will explore broader geographic datasets and real-time deployment on mobile platforms. Full article

Background: Humans interacting with virtual objects is becoming common due to the popularity of the devices adopting the mixed reality (MR) techniques. Assessing hand functions using these devices for medical purposes provides alternatives in addition to the traditional hand function assessment techniques. Objectives: The objectives were to compare the movement time (MT) of handing a real and a virtual object between post-surgery wrist fracture patients and healthy adults and to determine the correlation between the MT and commonly adopted hand function indicators. Methods: An experiment was performed. A total of 29 participants, including 17 patients and 12 healthy adults, joined. All the participants moved a real or a virtual tube from an origin to a destination. A set of MR device was adopted to generate the virtual object. The MTs were analyzed to compare differences between the patients and the healthy adults. Regression models were developed to predict the MT under experimental conditions. Results: The MT of the surgical hand was significantly longer than that of the nonsurgical hand of the patients and was significantly longer than that of the left hand of the healthy adults. The MT was negatively correlated with the commonly adopted hand function indicators, including grip strength, range of motion, hand dexterity score, and Modified Mayo Wrist Score. Conclusions: The anticipation that the MT of interacting with virtual objects for patients may reveal hand function characteristics for post-surgery patients was supported. The regression models developed could reveal the progression of hand function recovery for these patients. Having patients interact with virtual objects could be a supplemental approach in assessing their hand functions. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network. Full article

Understanding how people spend time on daily activities is key to modeling travel behavior. However, accurately estimating the duration of these activities remains a significant challenge, especially when generating synthetic activity-travel data. This article introduces an activity-based approach that addresses this issue by applying statistical and machine learning models to improve the precision of activity duration estimates. The method utilizes real-world Origin-Destination (OD) datasets to generate additional synthetic data that can support transportation planning processes. Unlike conventional approaches that rely solely on OD matrices, this framework incorporates Cox and Cox-based hazard models to more precisely estimate activity durations, as well as arrival and departure times across trip segments. Statistical tests and comparative evaluations show that the proposed method produces more accurate synthetic data than existing open-source tools that do not employ hazard-based modeling. A case study using real-world data from Athens, Greece, demonstrates the effectiveness of the proposed approach. Full article

In Wireless Sensor Networks (WSNs) used for real-time applications, ensuring Quality of Service (QoS) is essential for maintaining end-to-end performance guarantees. QoS requirements are typically defined by a set of end-to-end constraints, including delay, jitter, and packet loss. In multi-hop scenarios, this requires multi-constrained path computation. This research examines the standard Routing Protocol for Low-Power and Lossy Networks (RPL), which employs a Destination-Oriented Directed Acyclic Graph (DODAG) for data transmission. Nonetheless, there are several challenges related to multi-constrained route computation in the RPL: (1) The DODAG originates from an objective function that cannot manage multiple constraints. (2) The process of computing multi-constrained routes is resource-intensive, even for a single path. (3) The collection of QoS-compatible paths does not necessarily form a DODAG. To address these challenges, this paper suggests modifications to the existing protocols that shift computationally demanding tasks to edge servers. Such a strategic adjustment allows for the implementation of QoS-compatible route computation in WSNs using the RPL. It enhances their ability to meet increasingly stringent demands for QoS in numerous application environments. Full article

Urban interstices are transitional spaces that emerge between expanding metropolitan regions. Despite increasing scholarly interest, the empirical analysis of these cities’ spatial development and functional integration remains scarce, particularly within the contexts of state-led urbanization, where administrative boundaries significantly shape development outcomes. This study quantitatively investigates urban interstice dynamics through a detailed analysis of Jurong City, which is located between the cities of Nanjing and Zhenjiang in the Chinese Yangtze River Delta. Utilizing mobile phone signaling data and origin–destination (OD) flow analysis, this research study systematically measures the intensity, directionality, and spatial patterns of human mobility flows between Jurong and its neighboring cities. The findings demonstrate that Jurong has a strong functional connection to Nanjing, with nearly 60% of its outbound mobility directed toward the city, despite being governed by Zhenjiang. This misalignment reveals a structural tension between functional integration and administrative hierarchy, fostering distinct subcenters such as Baohua (residential) and Guozhuang (innovation). Overall, the findings highlight the need to move beyond territorially bounded governance toward functionally coordinated regional strategies. Urban interstices can serve as effective connectors across fragmented systems, supporting more balanced and adaptive metropolitan integration. Leveraging real-time mobility data enables planners to identify spatial–functional linkages that transcend administrative boundaries, informing more responsive governance without requiring formal realignment. Full article