Search Results (8)

Shopping malls have become major retail and lifestyle destinations that influence travel demand, mode choice, traffic congestion, private vehicle dependence, and transport-related environmental impacts. However, research connecting shopping mall visitation with sustainable urban mobility remains fragmented across transport, urban planning, retail, consumer behaviour, and environmental sustainability. This study uses bibliometric analysis to investigate publication patterns, intellectual structures, international collaboration, thematic clusters, and emerging trends in this interdisciplinary field. A total of 567 English-language articles and reviews published between 1 January 2000 and 7 May 2026 were retrieved from Scopus and analysed using descriptive indicators and VOSviewer-based document co-citation, country-level co-authorship, and keyword co-occurrence analyses. The findings indicate that publication activity has grown considerably since 2017, with China emerging as the most productive contributor and as a central hub for international research collaboration. Document co-citation analysis revealed six intellectual clusters related to parking and traffic congestion, online and in-store shopping behaviour, the built environment and land use, consumer value and retail experience, retail attractiveness and sustainable community planning, and retail revitalisation and urban regeneration. The keyword analysis revealed five main themes: urban transport, carbon emission, travel behaviour, shopping mall, and shopping activity. More recently, carbon emissions, sustainable development, online shopping, energy efficiency, and data-driven solutions have attracted increasing research attention. This study synthesises the fragmented knowledge base and suggests future research directions for low-carbon shopping mobility and sustainable access to retail destinations. Full article

Microplastic contamination is an emerging global concern, but its occurrence in high-altitude protected areas has been understudied. This study systematically assessed microplastic abundance, morphology, and spatial distribution in Sagarmatha National Park (SNP), Nepal, a UNESCO World Heritage Site. Soil samples were collected from 25 sites across four land-use types, including settlement, farmland, forest, and floodplain, at two depths along the Lukla–Phortse trekking corridor during the pre-monsoon season of 2023. Samples were pretreated by density separation and Fenton’s reagent digestion, and microplastics were then detected using a YOLOv11n-seg instance segmentation model. A subset of extracted particles was chemically confirmed by optical photothermal infrared (O-PTIR) spectroscopy. Microplastics were present in all samples, with concentrations ranging from 80 to 960 particles·kg⁻¹. Fragments were the dominant morphological type, accounting for 65.7% of all particles, followed by fibers and films. Negative binomial regression revealed significant effects of land use and soil depth and their interaction on microplastic abundance. Settlement soils showed the highest concentrations with significant surface enrichment, while farmland soils showed no significant depth effect, consistent with human-dominated plastic sources and tillage-driven redistribution. Elevation was not a significant predictor of contamination. Hotspot analysis identified statistically significant clustering around Lukla and Namche, the two primary tourism hubs. The baseline established in this study provides a foundation for long-term monitoring and targeted waste management in SNP and offers insights to other protected high-altitude environments. Meanwhile, the application of deep learning and O-PTIR to balance the counting efficiency and accuracy is a novel approach that could be adopted by other researchers. Full article

In response to environmental degradation and social inequities exacerbated by automobile-dependent urban sprawl, this study proposes a framework for dynamic delineation and vitality assessment of 15-min walkable neighborhoods, using Baohe District, Hefei, China as a case study. Static service catchments were constructed using POI and road network data, then refined using one week’s mobile phone signaling trajectories calibrated to actual walking behavior, yielding 143 validated living circles (out of 156 initially delineated). These circles are classified into five typologies: commercial-residential, industrial-residential, educational-residential, predominantly residential, and public-service-oriented. A dual-index system—Facility Vitality Index (FVI) and Population Vitality Index (PVI)—is developed and synthesized into a Composite Vitality Index (VI) through normalization and weighting. Results show that only 27.3% of living circles achieve high vitality in both dimensions, indicating widespread service–demand misalignment. Conversely, 61.5% exhibit low or very low vitality, forming a “vitality depression” around the urban periphery—a pattern of service poverty with significant socioeconomic implications. High-vitality circles cluster along the Binhu New District corridor, while low-vitality circles concentrate in industrial parks (e.g., Feinan Industrial Park) and transport hubs (e.g., Hefei South Railway Station). The historic core lacks micro-infrastructures, whereas new districts—despite high-standard amenities—suffer from weak pedestrian activity. To address these deficiencies, we propose a differentiated zoning strategy: retrofitting micro-infrastructures in legacy neighborhoods, applying Transit-Oriented Development (TOD) principles in new urban extensions, and integrating community-serving functions within industrial peripheries. This framework provides actionable protocols for data-informed governance of 15-min living circles. Full article

In the context of rapid urban tourism expansion and the growing emphasis on equitable and sustainable transport development, understanding how transport systems support different types of attractions has become increasingly important. This study investigates how attraction hierarchy and functional type interact with public transport accessibility to shape urban tourism patterns and equity. Whereas prior work emphasizes objective metrics, the alignment between perceived accessibility and actual transport conditions remains understudied. Using Wuhan’s A-rated and popular unrated attractions as a case, we have developed an innovative “ objective–perceived coupling framework that integrates GIS network analysis, travel cost matrix, non-parametric testing, and online comment text mining methods to examine how scenic spot levels (A-level and unrated popular scenic spots) and functional types interact with the public transportation system from both objective and perceptual dimensions. Results show: (1) A-rated attractions cluster in suburbs with low accessibility, while unrated sites concentrate centrally with high rail-bus connectivity, revealing a “high-grade–low-accessibility” mismatch. (2) Accessibility varies by type: natural sites are lowest, cultural/leisure venues intermediate, and comprehensive sites highest due to multimodal hub proximity. (3) Sentiment and topic analyses based on transport-related review content suggest that some A-rated attractions receive less favorable evaluations of access conditions (e.g., transfers, waiting, last-mile walking, wayfinding, and parking), whereas many popular unrated sites are evaluated more positively in these transport-specific aspects. (4) Quadrant analysis shows many highly rated attractions fall into a “low objective–low perceived” disadvantage, while most unrated ones exhibit strong objective–perceived coupling. These findings underscore structural imbalances among administrative grading, attraction function, and transit provision, offering evidence for optimizing public transport service to tourist attractions. They help optimize the spatial structure of urban tourism, improve resource allocation efficiency, guide differentiated scenic spot development strategies, and promote sustainable and experience-oriented urban tourism governance. Full article

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article

The purpose of this study is to explore and analyse user perceptions of peri-rural landscapes in the Seoul metropolitan region, using Icheon City as a case study. While the multifunctionality of peri-rural areas—providing ecological, cultural, and socioeconomic benefits—is increasingly recognised, the perceptual and experiential dimensions remain underexplored in South Korea. To address this gap, 10,578 Naver Blog posts were collected and refined, resulting in 8078 valid entries. Methodologically, this study introduces an innovative approach by integrating centrality analysis with latent Dirichlet allocation (LDA) topic modeling of user-generated content, supported by a bespoke dictionary of 170 local landscape resources. This combined framework allows simultaneous examination of structural associations and thematic narratives within user perceptions. The results indicate that resources such as Seolbong Urban Park, Seolbong Mountain, and the Cornus Fruit (sansuyu) Villages function as symbolic hubs in the perceptual network, while thematic clusters capture multi-dimensional concerns spanning leisure, ecology, culture, suburbanization, and real estate. Synthesised together, these findings demonstrate that user perceptions construct peri-rural landscapes not as isolated sites, but as spatially cohesive and thematically interconnected systems that mediate between urban and rural domains. Overall, this study contributes to metropolitan planning discourse by highlighting perceptual dimensions alongside functional and ecological dimensions. It shows that users cognitively construct peri-rural landscapes as systems that are both spatially cohesive and thematically interconnected, and that function as spaces that link urban and rural areas. Crucially, this study provides a replicable framework for using user-generated content to inform the planning and management of peri-rural landscapes in metropolitan areas. Full article

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent. Full article

Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has already taken action to install new on-street parking meters and release real-time parking occupancy information to the public. The data have been released for months, yet, to the best of our knowledge, there has been no study analyzing the data and identifying their unique characteristics for Hong Kong. In view of this, we examined the spatio-temporal patterns of on-street parking in Hong Kong using the data from the new meters. We integrate the t-SNE and k-means methods to simultaneously visualize and cluster the parking occupancy data. We found that the average on-street parking occupancy in Hong Kong is over 80% throughout the day, and three parking patterns are consistently identified by direct data visualization and clustering results. Additionally, the parking patterns in Hong Kong can be explained using land-use factors. Overall, this study can help the government better understand the unique characteristics of on-street parking and develop smart management strategies for Hong Kong. Full article