Search Results (141)

Fragmented urban growth in Latin American intermediate cities has increasingly displaced low-income populations to peripheral areas, creating spatial mismatches between residential areas and educational opportunities provided by public higher education institutions. These patterns reflect inequalities that constrain educational accessibility and limit urban resilience. To examine these dynamics, this study applies a geospatial framework to develop an Educational Spatial Injustice Index (ESII) that quantifies disparities in Chihuahua, Mexico. Mobility metrics derived from Google Maps API are combined with census microdata, to analyze 108 high-demand census tracts (AGEBs) linked with seven public higher education institutions. The ESII is constructed through a multi-criteria weighting scheme based on travel time, monetary cost and network distance. Results show a clear north–south pattern, with higher ESII values concentrated in southern peripheries (0.85/1.0), where mean daily travel time reaches 3.45 h. Statistical analysis shows that the Social Lag Index (IRS; ρ = 0.729) has the strongest association with the Global ESII. Furthermore, spatial regression modeling reveals that while operational transit connectivity (bus routes) is a significant mitigating factor (p < 0.001), the mere physical presence of infrastructure (bus stops) is not. These findings suggest that spatial injustice in educational accessibility is more deeply rooted in structural urban growth patterns and socio-spatial inequalities than in simple infrastructure provision. The results support targeted planning strategies to improve accessibility and strengthen urban resilience in intermediate cities. Full article

Urban heat island effects increasingly challenge pedestrian mobility by intensifying thermal stress and reducing the attractiveness of walking during hot periods. However, most pedestrian routing systems still prioritize distance or travel time, while environmental conditions such as heat exposure and shade are rarely incorporated into operational route generation. Existing comfort-aware approaches often rely on static maps, simulated microclimatic indicators, or descriptive greenery measures, limiting their direct integration into user-configurable pedestrian navigation. This study develops a thermal comfort-aware pedestrian routing framework that integrates heterogenic data sources including observed land surface temperature, pedestrian-perspective tree-canopy coverage, and network distance into a unified multi-criteria pathfinding model. The workflow proceeds in four steps: first, airborne thermal imagery is processed to derive a high-resolution land surface temperature layer; second, Google Street View images are sampled at street-segment locations and segmented using SegFormer to extract visible tree-canopy coverage; third, both environmental indicators are aggregated to a cleaned pedestrian network; and fourth, normalized distance, temperature, and canopy attributes are combined through a user-adjustable edge-cost formulation and solved using Dijkstra’s algorithm. The framework is implemented as an operational web-based routing tool for the historic center of Clermont-Ferrand, France. The routable graph includes 551 nodes and 796 edges, with 600 segments carrying GSV-derived canopy information and 623 segments carrying airborne-derived LST values. Across the network, we observed LST ranges from 19.5 °C to 39.1 °C, while canopy coverage ranged from 0 to 70.6%. For a representative origin–destination pair, the coolest route reduces average LST by nearly 5 °C and almost triples canopy coverage compared with the shortest path, although at the cost of a 72% longer distance. These results demonstrate that the framework can generate interpretable comfort–efficiency trade-offs and support user-comfort pathfinding as an operational approach for heat-resilient pedestrian navigation. Full article

This systematic literature review synthesises existing knowledge on volunteer sport tourism—the intersection of volunteering, sport, and tourism—in order to clarify its evolution, theoretical foundations, and practical implications, and to address the fragmentation of the field across disciplines. Following PRISMA 2020 guidelines, peer-reviewed journal articles, books, and doctoral dissertations published in English between January 1974 and December 2025 were retrieved from Web of Science, Scopus, SportDiscus, and Google Scholar, complemented by citation tracking. Studies focused on volunteers travelling to sporting events were included; conference abstracts, editorials, and works addressing only local volunteering or general tourism were excluded. The methodological quality of 32 included studies was appraised narratively by two authors independently against three criteria, with disagreements resolved through discussion. Findings were integrated through a thematic narrative synthesis supported by analytical mapping tables. The review identifies five dominant themes: motivations, volunteer experiences and satisfaction, economic and social impacts, organisational and management perspectives, and destination and legacy dimensions. The synthesis contributes to theoretical development by proposing an integrated tripartite framework that connects volunteer antecedents, event experiences, and legacy outcomes, and offers practical recommendations for event organisers, policymakers, and destination stakeholders. The review was conducted without pre-registration. Full article

Modern route planners such as Google Maps and Apple Maps serve millions of users worldwide, optimizing routes in large-scale road networks where fast responses are required for diverse cost metrics including travel time, fuel consumption, and toll costs. Classical algorithms like Dijkstra or A* are too slow at this scale, and while index-based techniques achieve fast queries, they are often tied to fixed metrics, making them unsuitable for dynamic conditions or user-specific metrics. Customizable approaches address this limitation by separating metric-independent preprocessing and metric-dependent customization, but they remain limited by slower query performance. We recently introduced Customizable Tree Labeling (CTL) as a framework that combines tree labelings with shortcut graphs. The shortcut graph enables efficient customization to different cost metrics, while tree labeling, supported by path arrays, provides fast query answering. Although CTL enables optimizing routes with different cost metrics, it still faces challenges in storing and reconstructing path information efficiently, which hinders its scalability for answering millions of queries. In this article, we build on the CTL framework by developing several algorithmic variants that differ in the information retained within shortcut graphs and path arrays, offering a spectrum of trade-offs between memory usage and query performance. To further enhance scalability, we propose a batch processing strategy that shares path information across queries to eliminate redundant computation. We empirically evaluated the performance of our algorithms on 13 real-world road networks. The results show that they significantly outperform state-of-the-art methods, achieving speedups of up to factor 15 for route computation while maintaining practical memory requirements. Full article

Background/Objectives: Western Canadian and U.S. communities outside urban centres remain underserved by primary, specialist, emergency, mental health, and chronic-disease services. These access problems reflect distance, weather, workforce shortages, specialist maldistribution, primary care attachment gaps, broadband limitations, and the governance realities of Indigenous and Tribal communities. This scoping review with narrative synthesis examined how telehealth and virtual-care models affect rural access in western Canada and the western/frontier United States. Methods: Searches were completed on 21 May 2026 in PubMed/MEDLINE, Embase, CINAHL, Scopus, the Cochrane Library, and PubMed Central. Supplementary searches included Google Scholar, publisher platforms, reference-list checking, and official Canadian and U.S. health-system sources. Peer-reviewed evidence published from 1 January 2016 to 21 May 2026 was eligible when it addressed rural, remote, frontier, Indigenous, underserved, western, or northern healthcare settings and reported access, implementation, safety, continuity, equity, or service-use outcomes. Results: The search identified 112 records; 27 duplicates were removed, 85 records were screened, 37 full texts were assessed, and 28 peer-reviewed records were included. Seven official sources were retained separately. Evidence was mainly observational, qualitative, mixed-methods, implementation-focused, or review-level. Moderate confidence supported telehealth for travel reduction and specialist input, especially through eConsultation, provider-to-provider consultation, telementoring, and real-time emergency support. Confidence was low to moderate for hybrid primary care and telemental health, and low for durable reductions in emergency department use. Conclusions: Telehealth may be most appropriately implemented as a hybrid, locally anchored, culturally safe access model, not as a stand-alone substitute for rural primary care, specialist capacity, or emergency services. Implementation should include broadband support, local physical assessment capacity, documentation, continuity, patient education, and clear escalation pathways. Full article

This study develops novel mathematical models to capture the complexities of international land logistics by extending the classical Traveling Salesman Problem (TSP) within a symmetry-aware optimization framework. A focused review of literature provides the theoretical basis for model formulation and highlights the limitations of conventional distance-only approaches. In international transport, shorter routes are often assumed to reduce energy use; however, this assumption overlooks the decisive influence of travel time and traffic variability. In this context, symmetry offers a useful analytical lens, as balanced relationships among distance, time, and fuel consumption can reveal more efficient logistics structures. Accordingly, two models are proposed: the Traditional Traveling Salesman Problem in terms of Distance Concentration (TTSPD), which minimizes route length, and the Extended Traveling Salesman Problem in terms of Distance and Time Concentration (ETSPDT), which jointly considers distance, travel time, and fuel consumption. Furthermore, TTSPD was employed to validate ETSPDT, since it is based on the traditional TSP. Both models are solved exactly using the Solver Add-in in Microsoft Excel 2024 with data derived from Google Maps. The results show that ETSPDT achieves superior energy efficiency and average speed, demonstrating the practical value of multidimensional, symmetry-informed optimization for sustainable supply chain and logistics management. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Sustainability in tourism is becoming an increasingly significant challenge given the growing environmental, social, and cultural pressures associated with traditional forms of travel. One tool considered in this context is virtual reality (VR), which enables tourism experiences without the need for physical travel. The aim of this article is to examine how individual characteristics and features of the VR experience relate to user experience and changes in the evaluation of tourist destinations. The empirical study is based on surveys conducted before and after VR sessions in which 215 participants used the “Google Earth VR” application and visited locations of their choice. This paper presents the results of the relationship analysis between different variables. The dataset included evaluation of perceived realism and its components (360° representation, graphical quality, lag/smoothness, freedom of exploration, sound quality, tracking accuracy), engagement, emotion in VR, intuitiveness of VR use and more. In terms of the most important results, participants with a higher interest in travel reported stronger emotional responses and higher engagement during the VR experience, while perceived realism showed a weaker but directionally consistent association. VR showed a somewhat stronger, though still small, association with positive change in destination evaluation among participants with low initial tourism interest, for whom the experience may introduce novelty or reduce psychological distance to the destination. The analyses conducted contribute to a better understanding of the factors associated with the virtual tourism experience and highlight the potential of VR as a tool supporting the development of more sustainable forms of tourism experiences. Full article

The Camino de Santiago is one of the most important cultural routes in the world and a privileged laboratory for analysing the challenges of sustainability in long-distance heritage destinations. The aim of this research is to understand the underlying dynamics of the Way, as well as its degree of sustainability. To achieve this, we examine the recent evolution of tourist demand for the Way from a territorial and sustainability perspective, integrating official statistical data with digital interest indicators from Google Trends (2004–2025). The methodology combines quantitative analyses of trends, seasonality, spatial diversification and internationalisation of demand, applying robust techniques such as the Theil–Sen slope and the Mann–Kendall test. The results show structural growth and high resilience of the Jacobean tourism system, even after the disruption caused by COVID-19, together with a growing internationalisation of flows. However, this tourism success is accompanied by strong spatial and temporal imbalances, with a marked concentration on the French Way and in the summer months, which increases environmental and social pressure on the most travelled territories. The analysis of digital interest also reveals a progressive decline in the importance of Holy Years as a driving force for attraction, especially in international markets. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

The emergence of Generative Artificial Intelligence (GenAI), capable of interpreting and reasoning with human language, has catalyzed a paradigm shift across various societal sectors. Within the tourism industry, GenAI is increasingly utilized to facilitate personalized itinerary planning, destination recommendations, and the provision of optimal route information. This study evaluates the reliability of GenAI in identifying the nearest metro station within a walking distance from tourist attractions in Busan, South Korea. Furthermore, it aims to empirically verify the determinants influencing the correctness of AI-generated responses compared to network-based shortest-path analyses. The empirical results demonstrate that Google’s Gemini 3 Pro model achieved superior performance, recording an accuracy rate of 65.0%. Regression analysis revealed that for both Gemini and GPT models, the volume of news articles associated with an attraction—representing media visibility—significantly increased the likelihood of accurate information provision. Notably, the Gemini model exhibited distinct sensitivity to geographic factors and text similarity metrics, suggesting a difference in how it processes spatial context compared to other models. Consequently, this study underscores the importance of high-quality AI-generated tourism data and offers significant contributions to the advancement of sophisticated personalized travel planning systems and GeoAI research focused on spatial problem-solving. Full article

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

Travel-associated Legionnaires’ disease (TALD) events can generate public concern when environmental surveillance findings are communicated without an adequate explanation of the results. This study examined how surveillance data on Legionella spp. were framed and amplified during a TALD-related investigation in Crete, Greece, from June to July 2025. A mixed infodemiology and environmental surveillance approach was applied, including the analysis of 95 online media items across nine languages, Google Trends search-interest data, and hotel water-system surveillance data from epidemiologically linked facilities. Sampling conducted in a limited number of hotels associated with TALD cases indicated that approximately 50% of the water samples exceeded the laboratory reporting limit of ≥50 CFU/L for Legionella spp., a numerically correct but context-specific finding. Numerical misframing occurred in 83.7%, 41.7%, and 18.2% of Greek, German, and English language items, respectively, with significant differences across language markets (χ² (8) = 43.75, p < 0.0001; Cramér’s V = 0.679). Public search-interest signals were transient and geographically limited. Environmental surveillance showed no increase in Legionella pneumophila risk, with similar proportions of samples ≥50 CFU/L in the pre-/peri-infodemic (January–July 2025) and post-infodemic (August–November 2025) periods (23.11% [95% CI: 18.21–28.87] vs. 24.45% [19.34–30.41]) and similar exceedance of ≥1000 CFU/L (13.45% [9.69–18.36] vs. 14.41% [10.45–19.55]). Overall, the loss of contextual interpretation of surveillance results and conflation of laboratory reporting limits with regulatory thresholds were associated with inconsistent public risk perception, without evidence of increased environmental hazard. Full article

With the rise in experience economy and the popularization of digital technology, user-generated content (UGC) has become a core data source for understanding tourist needs and evaluating the service quality of venues. As a landmark venue that combines science education, interactive experience, and landscape viewing, the service quality of the Macau Science Center directly affects tourists’ travel experience and word-of-mouth dissemination. However, existing studies mostly rely on traditional questionnaire surveys and lack multi-technology collaborative analysis. In order to accurately identify the factors affecting satisfaction, this study uses 788 valid UGC data from five major platforms, namely Google Maps reviews, TripAdvisor, Sina Weibo, Xiaohongshu (Rednote), and Ctrip, from January 2023 to November 2025. It integrates word frequency analysis, semantic network analysis, latent Dirichlet allocation (LDA) topic modeling, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment computing to construct a systematic research framework. The study found that (1) the core attention dimensions of users cover the needs of parent–child and family visits, exhibitions and interactive experiences, ticketing and consumption services, surrounding environment and landscape, emotional evaluation, and recommendation intention. (2) The keyword association network has gradually developed from a loose network in the early stage to a comprehensive experience-dense network. (3) LDA analysis identified five main potential demand themes: comprehensive visiting experience and scenario integration, parent–child interaction and characteristic scenario experience, core venue facilities and ticketing services, visiting value and emotional evaluation, and transportation and surrounding landscapes. (4) User emotions were predominantly positive, accounting for 82.7%, while negative emotions were concentrated in local service details, and the emotional scores showed a fluctuating upward trend. This study provides targeted suggestions for the service optimization of the Macau Science Center and also provides a methodological reference for UGC-driven research in similar cultural venues. Full article

Highway interchanges are vulnerable components of transport networks, often prone to congestion and crashes. Traditional monitoring methods like loop detectors or travel time queries often fail to capture the granular spatiotemporal distribution of bottlenecks in detail. To address this gap, this study introduces a new approach to quantify congestion and analyze bottleneck dynamics at Atlanta’s Tom Moreland Interchange, one of the nation’s most congested sites. A percent Traffic Congestion (pTC) metric was developed from the Google Maps Traffic Layer for twelve directional routes and validated against observed travel times obtained independently through the Google Maps Routes API. Traffic imagery collected every ten minutes for four months and 746 crash records were analyzed. Findings reveal distinct spatial patterns and temporal dynamics of congestion, with northbound I-85 and eastbound I-285 most affected during afternoon peaks. A quadratic model provided the best fit between pTC and travel times (R² = 0.85), confirming pTC as a reliable congestion indicator. An LSTM model using pTC time series also accurately predicted mobility trends at the I-285 west to I-85 north bottleneck. Additionally, Seasonal-Trend decomposition using LOESS (STL) identified congestion anomalies, and their association was analyzed with crashes. The proposed methodology offers transportation agencies a cost-effective framework for monitoring, measuring, and understanding congestion in complex interchanges. Full article