Search Results (201)

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway system, offers a comprehensive rail network, making it an ideal case for exploring the effects of transit density on jobs–housing balance. This study utilizes medium-scale panel data from Beijing (2009–2022) and employs a fixed-effects model to systematically examine the impact of rail transit station density on jobs–housing balance and its underlying mechanisms. The results indicate that increasing transit station density tends to aggravate jobs–housing separation overall, with pronounced effects in central and outer suburban areas but negligible effects in near suburban areas. Mechanism analysis reveals two primary pathways: (1) improved accessibility draws employment toward transit-rich areas, reinforcing the attractiveness of central districts; (2) rising housing prices elevate residential thresholds, pushing lower-income populations toward outer suburbs. While enhanced transit density improves commuting convenience, it does not effectively reduce jobs–housing separation. These findings offer important policy implications for optimizing transit planning, improving jobs–housing alignment, and promoting sustainable urban development. Full article

Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only significant for commercial stations. Furthermore, these influences exhibit spatial heterogeneity. For instance, at office-oriented stations, population shows both positive and negative effects across areas, while residential density has a generally negative impact. These findings enhance our understanding of how the built environment shapes bike-sharing–metro integration patterns and support more targeted planning interventions. Full article

Transit deserts, defined by insufficient public transit provision relative to demand, aggravate socio-economic inequalities by restricting access to employment, education, and healthcare. With increasing urbanization and growing disparities in public transport accessibility, identifying transit deserts is critical for equitable mobility planning. As urban populations expand, addressing transit accessibility requires advanced data-driven approaches. This study applies machine learning (ML) models, decision trees (DTs), logistic regression (LR), and random forest (RF), within an Intelligent Transport System (ITS) framework to detect transit deserts in Lucknow, India. Employing a 100 × 100 m spatial grid data, the models classify transit accessibility based on economic status, trip frequency, population density, and service access. The results indicate that RF achieves superior classification accuracy, while DT offers interpretability with slightly lower recall. LR underperforms due to its linear assumptions. The findings reveal the spatial clustering of transit deserts in socio-economically disadvantaged areas, highlighting the need for targeted interventions. This study advances ML-driven ITS analytics, offering a novel approach for classifying transit accessibility patterns at a granular level, thereby aiding policy interventions for improved urban mobility. Full article

Biodegradable mulch films were developed over the last decades to replace polyethylene, but their short durability and higher costs still limit their diffusion. This work aimed to test an innovative composite mulching film constituted by a mixture of carboxylmethyl cellulose, chitosan and sodium alginate, enriched or not with an inorganic N- and P-source to help the microbial breakdown in soil. The trial was carried out using outdoor mesocosms cultivated with lettuce plants with high-density planting. Commercial Mater-Bi^® and a polyethylene film were taken as control treatments. Air temperature and humidity monitored daily during the 51 d cropping cycle remained within the ideal range for lettuce growth with no mildew or fungi infection. Visible mechanical degradation of the experimental biopolymers occurred after 3 weeks; however, Mater-Bi^® and polyethylene remained unaltered until harvest. Chemical soil variables (TOC, TN, CEC, EC) remained unchanged in all theses, whereas the pH varied. The yield, pigments, total phenols, flavonoids and ROS scavenging activity of lettuce were similar among treatments. Despite their shorter life service (~3 weeks), polysaccharide-based mulching films showed their potential to protect lettuce plants at an early stage and provide yield and nutraceutical values similar to conventionally mulched plants, while allowing a reduced environmental impact and disposal operations. Full article

Drawing on the longitudinal dataset from 262 cities at the provincial tier and higher across China between 2011 and 2022, this research employs the production model to formulate the China Urban Industrial Land Mismatch Index, quantifying the extent of industrial land misalignment across China. It also analyzes its spatiotemporal evolution characteristics and regional differentiation characteristics, and explores the influence of China’s urban industrial land discordance on the advancement of urban ecological modernization. The key insights are outlined below. Firstly, across the entire spectrum of Chinese urban centers, cities from the eastern, central, and western zones, as well as those situated along the Yangtze River and the Yellow River basins, exhibit comparable patterns in industrial land misalignment. The extent of industrial land discordance has diminished, regional disparities have lessened to some degree, and there is an absence of polarization or the Matthew effect. Secondly, the variation in industrial land discordance within cities in the eastern region is the most pronounced, followed by the central region, with the western region showing the least disparity. The greatest contrast in the urban industrial land mismatch is found between the eastern and central regions. The primary driver of the discrepancy in industrial land misalignment across the eastern, central, and western regions is predominantly the ultra-variable density, followed by intra-regional disparities, with inter-regional differences contributing the least. Furthermore, the variation in the industrial land mismatch within cities in the Yangtze River Basin surpasses that within cities in the Yellow River Basin. The disparity in industrial land misalignment between the two follows a pattern of initially increasing, then decreasing, and subsequently rising again. The primary origin of this discrepancy lies within regional variations, followed by ultra-variable density, with inter-regional differences contributing the least. Thirdly, the regression analysis reveals that the discordance in industrial land use across Chinese cities exerts a substantial negative influence on urban ecological evolution. This effect operates through technological innovation and the employment levels in the secondary sector. Fourthly, industrial land discordance significantly hampers urban ecological advancement in the eastern region, shows a negative but statistically insignificant impact in the central region, and has a positive yet inconsequential effect in the western region. Moreover, the misalignment of industrial land exerts a notable suppressive influence on the ecological modernization of cities within the Yangtze River Basin, while it plays a significant role in fostering the ecological modernization of cities in the Yellow River Basin. Fifth, the mismatch of urban industrial land has produced significant negative spatial spillover effects on urban ecological modernization. Full article

Metaverse environments aim at replicating physical reality at various scales. While the potential growth of digital land is limitless, certain development factors may drive greater growth and lead to changes in surrounding land use and the expansion of developed land. In this study, we are bridging digital and physical world living environments using physical land as an example. Specifically, we focus on Tennessee as a study area and offer a spatial perspective on factors of urban land growth and study the relationship between infrastructure (road) development, employment, mobility, and land use change in the physical world, which may help understand this connection in the digital land and real estate domain. We show a significant role of employment hubs in driving developed land growth (i.e., land development). Economic activity consistently appears significant in urban land expansion, with the land development effect of employment stretching over a larger area well beyond immediate proximity to road infrastructure. Land development is measured in this study by a developed land density in both urban and rural areas. Mobility (measured by VMT) has a weaker impact on land development though still positive and statistically significant. This research is crucial for developing sustainable land growth strategies and informing future transport planning and land-use policies. Full article

As the severe environmental impacts of plastic pollution demand determined action, the European Union (EU) has included recycling at the core of its policies. Consequently, evolving jurisdiction now aims to achieve a recycling rate of 65% for non-PET plastic bottles by 2040. However, the widespread use of post-consumer high-density polyethylene (rPE-HD) recyclates in household chemical containers is still limited by PP contamination, poor mechanical properties, and low environmental stress cracking resistance (ESCR). Although previous studies have explored the improvement of regranulate properties through additives, few have examined whether reducing the variety of extrusion blow-moulded PE-HD packaging could offer similar benefits. Therefore, two sorted fractions of rPE-HD hollow bodies were processed into regranulates under industrial conditions, including hot washing, extrusion, and deodorisation. Subsequently, both materials underwent comprehensive characterisation regarding their composition and performance. The opaque material, which was sourced from milk bottles in the UK, exhibited greater homogeneity with minor impurities, leading to improved ductility and melt strain hardening at moderate strain rates compared to the mixed material stream, which contained approximately 2.5% PP contamination. However, both rPE-HD recyclates exhibited similar short-term creep behaviour, relatively low strain hardening moduli, and were almost devoid of inorganic particles. Considering the sum of the investigated properties, melt blending with suitable virgin material is likely one of the most effective options to maximise regranulate utilisation in hollow bodies, followed by recycling-oriented packaging design (e.g., for efficient sorting), and the employment of advanced sorting technology. Full article

Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article

Urban vitality is a critical metric for assessing the development and appeal of urban areas, playing a pivotal role in urban planning and management. Traditionally, surveys and census data have been used to measure urban vitality; however, these methods are often time-consuming, resource-intensive, and limited in coverage. This study addresses these limitations by employing mobile phone signaling data to develop a model for quantifying urban vitality and exploring its spatiotemporal distribution patterns. By integrating socioeconomic, street view, and points-of-interest (POI) data, this study utilizes linear regression and geographically weighted regression (GWR) models to analyze the influence of various factors on urban vitality. The SHapley Additive exPlanations (SHAP) method is then applied to interpret model predictions and identify key determinants of urban vitality. Using Shenzhen as a case study, the results reveal pronounced spatial disparities in vitality. Among all variables, bus stop density, cultural services, and employment density consistently exhibit significant effects on urban vitality. The proposed urban vitality quantification framework enables high-resolution and wide-coverage monitoring of urban vitality, providing scientific support and decision-making guidance for understanding the dynamic characteristics of urban spaces and optimizing urban functional layouts. Full article

Marine litter (ML), encompassing human-made objects in marine ecosystems, poses significant threats to the coasts of some Adriatic islands, despite their remoteness and sparse populations. These islands, reliant on tourism, are particularly vulnerable to ML pollution. This study hypothesized that the natural features of the islands influence ML distribution. It employes an integrated geographic approach combining the results of field survey (via sea kayaking) with various indicators which include: (1) coastal orientation and number density of bays, (2) vegetation exposure and biomass share, (3) island area and number density of bays, (4) bay openness and ML quantity, and (5) bay openness and plastic prevalence in ML. Focusing on islands of Lošinj, Pašman, Vis, and the Kornati and Elaphiti archipelago, the study analyzed data collected over six years (2018–2023). Results highlighted that NW-SE and W-E coastal orientations are particularly susceptible to ML accumulation, especially in the southern Adriatic. Linear Fitting Regression analyses revealed a stronger correlation between number density of polluted bays and the surface area of smaller islands (<10 km²) compared to larger islands (>10 km²). The following findings underscore the need for international collaboration and stringent policies to mitigate ML pollution, ensuring the protection of Adriatic marine ecosystems and the sustainability of local communities. Full article

Micro-size light-emitting diodes (μLEDs) are high-brightness, low-power optoelectronic devices with significant potential in display technology, lighting, and biomedical applications. AlGaInP-based red LEDs experience severe size-dependent effects when scaled to the micron level, and addressing the fabrication challenges of GaN-based red μLED arrays is crucial for achieving homogeneous integration. This study investigates the employment of KOH wet treatments to alleviate efficiency degradation caused by sidewall leakage currents. GaN-based red μLED arrays with pixel sizes ranging from 5 × 5 µm² to 20 × 20 µm² were grown using metal-organic chemical vapor deposition (MOCVD), and then fabricated via rapid thermal annealing, mesa etching, sidewall wet treatment, electrode deposition, sidewall passivation, chemical-mechanical polishing, and via processes. The arrays, with pixel densities ranging from 668 PPI (Pixel Per Inch) to 1336 PPI, consist of 10,000 to 40,000 emitting pixels, and their optoelectronic properties were systematically evaluated. The arrays with varying pixel sizes fabricated in this study were subjected to three distinct processing conditions: without KOH treatment, 3 min of KOH treatment, and 5 min of KOH treatment. Electrical characterization reveals that the 5-min KOH treatment significantly reduces leakage current, enhancing the electrical performance, as compared to the samples without KOH treatment or 3-min treatment. In terms of optical properties, while the arrays without any KOH treatment failed to emit light, the ones with 3- and 5-min KOH treatment exhibit excellent optical uniformity and negligible blue shift. Most arrays treated for 5 min demonstrate superior light output power (LOP) and optoelectronic efficiency, with the 5 µm pixel arrays exhibiting unexpectedly high performance. The results suggest that extending the KOH wet treatment time effectively mitigates sidewall defects, reduces non-radiative recombination, and enhances surface roughness, thereby minimizing optical losses. These findings provide valuable insights for optimizing the fabrication of high-performance GaN-based red μLEDs and contribute to the development of stable, high-quality small-pixel μLEDs for advanced display and lighting applications. Full article

To achieve the “dual-carbon” target and ensure energy security, there is an urgent need to promote the transformation of the energy system, of which the coal industry is the main battlefield. In order to study the spatial and temporal characteristics and influencing factors of the coal industry transformation and development (CITD), this article establishes an evaluation index system for the transformation and development of the coal industry, including 17 indicators in six dimensions. The projection pursuit (PP) model, which relies on the Real Coded Accelerating Genetic Algorithm (RAGA), is applied to assess the CITD index in 23 Chinese provinces between 2011 and 2021. The findings indicate that (1) the CITD index in China as a whole shows an upward trend, and the regional differences are more obvious, in the following order: eastern, central, and western. (2) There is striking spatial autocorrelation in the CITD in China, and the CITD in this region has a striking spatial spillover effect on the neighboring regions. (3) Human capital, foreign direct investment level, and employment density are positively correlated with CITD, while industrial development level and government intervention extent are negatively correlated with it. Policymakers should incorporate the findings of the study and formulate targeted policies to provide ideas for fueling the transformational development of the coal industry. Full article

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior. Full article

While the literature has investigated the associations between urban environments and COVID-19 infection, most studies primarily focused on urban density factors and early outbreaks, often reporting mixed results. We examined how diverse urban factors impact COVID-19 cases across 229 administrative districts in South Korea during Pre-Omicron and Post-Omicron periods. Real-time big data (Wi-Fi, GPS, and credit card transactions) were integrated to capture dynamic mobility and economic activities. Using negative binomial regression and random forest modeling, we analyzed urban factors within the D-variable framework: density (e.g., housing density), diversity (e.g., land-use mix), design (e.g., street connectivity), and destination accessibility (e.g., cultural and community facilities). The results revealed the consistent significance of density and destination-related factors across analytic approaches and transmission phases, but specific factors of significance varied over time. Residential and population densities were more related in the early phase, while employment levels and cultural and community facilities became more relevant in the later phase. Traffic volume and local consumption appeared important, though their significance is not consistent across the models. Our findings highlight the need for adaptive urban planning strategies and public health policies that consider both static and dynamic urban factors to minimize disease risks while sustaining urban vitality and health in the evolving pandemic. Full article