Search Results (22,439)

It is the key premise of this review article that efforts by cities to shift away from car dependence or car-centric mobility will provide unusual opportunities to renaturalize and rewild cities and urban spaces, and represent a key approach to implementing the emerging vision of Biophilic Cities. A remarkable amount of urban space in cities is devoted to automobiles and to car-based mobility; even a small shift away from cars will open up significant new possibilities for planting trees and forests and other forms of nature. In many cities, this shift will be necessary to reach ambitious urban nature targets (e.g., forest canopy goals). The article describes many ways that cities can begin to rethink their streets and car mobility and provides an initial typology of biophilic streets and spaces that will be useful in this biophilic urban transition. A variety of emerging ideas and early examples from cities around the world are presented; from Paris to New York City to Seoul, there are emerging examples of what is possible, including open streets, green streets, urban trails and nature pathways, and wildlife-friendly streets and roads, among many others. The article speculates about future directions, as well as key obstacles cities will likely face as they implement these new biophilic mobility options and ideas. Full article

Within the “15-min community life circle”, spatial accessibility of basic services is critical to ensuring social equity for people with disabilities. However, existing wheelchair accessibility assessments predominantly focus on static “origin-destination” node configurations, treating the road network as homogeneous and failing to capture how micro-level physical barriers induce macro-level accessibility decay through network connectivity transmission. This study addresses this gap by introducing an “ideal-realistic” scenario comparison framework combined with weighted edge betweenness centrality. This approach backward-traces resource accessibility loss to two dimensions of micro-level road-segment failure, namely topological function loss and service resource loss, shifting the analytical paradigm from resource configuration assessment to network connectivity diagnosis. Taking the central Xinhai Subdistrict of Lianyungang, China, as a case study, this paper constructs a dual-scenario network based on field-surveyed barrier data and establishes a two-dimensional measurement system centered on segment-level function loss and resource loss. The findings reveal that realistic physical barriers cause significant deprivation in wheelchair users’ access to life-circle services. Moreover, this decay is not homogeneously distributed but is highly contingent on the topological role of road segments, exhibiting observed spatial patterns such as skeletal network collapse, critical entrance blockages, and isolated terminal resource clusters. The analysis elucidates how micro-level physical barriers are propagated through spatial connectivity, offering a new perspective for understanding impedance dynamics in existing urban slow-traffic environments. The results can inform precision interventions in pre-standard neighborhood renewal: critical bottleneck segments provide spatial targets for prioritizing resource allocation and context-specific strategies across communities. Full article

Dairy cold-chain network planning requires coordinated decisions under demand variability, product perishability, and environmental constraints. To address these interrelated challenges, this study formulates an order-driven multi-objective mixed-integer nonlinear programming (MINLP) model for the tactical planning of a three-echelon dairy cold-chain network. The model coordinates distribution-center selection, inventory, transportation allocation, vehicle configuration, and refrigeration decisions to minimize total cost, transportation-related carbon emissions, and the quantity- and importance-weighted average freshness-loss rate. Demand variability is represented through service-level-based safe demand, whereas product freshness is evaluated using Weibull-based shelf-life reliability and inventory–transportation exposure. Transportation congestion is further incorporated to capture its effects on travel time, refrigeration emissions, and freshness deterioration. NSGA-II is employed to generate Pareto solutions, with entropy-weighted TOPSIS used for compromise-solution selection and MOEA/D serving as the benchmark algorithm. Numerical results indicate that NSGA-II achieves favorable convergence performance and comparable solution diversity relative to MOEA/D, while small-scale mixed-integer approximation tests support the quality of the obtained solutions. Multi-scale experiments demonstrate stable computational performance as network size increases. Sensitivity and scenario analyses further reveal distinct effects of service levels, shelf-life characteristics, and road capacity on economic, environmental, and freshness performance. The proposed framework provides tactical decision support for coordinating demand-responsive supply, low-carbon operations, and freshness preservation in dairy cold-chain networks. Full article

This article summarizes the results of a demand-and-supply analysis of hydrogen road transport development in Poland, conducted as part of a research project at the Motor Transport Institute in 2025, with a time horizon extending to 2040. The central aim of the study is to identify optimal hydrogen refueling station locations and develop a phased deployment plan. It also assesses the potential for using hydrogen as an alternative transport fuel in Poland compared with other European countries. The article presents a multi-criteria decision-making model developed to support the planning of hydrogen infrastructure for the Polish transport system. The model’s assumptions and the research results concerning the selection of hydrogen refueling station locations in large cities are discussed. Technical, ecological, logistical, legal, and social aspects related to the implementation of hydrogen infrastructure are analyzed. The article also outlines methods for parameterizing and assessing the potential for hydrogen adoption in transport. Forecasts indicate that the development of a hydrogen vehicle fleet could contribute to improved public health, sustainable development, and a reduction in the negative effects of high emissions. The objective of the research is to determine the environmental and energy-transition impacts of shifting from combustion-powered transport to hydrogen over the coming decades. The study introduces an innovative modeling approach, as previous research has not examined city rankings and urban mobility plans with comparable depth to identify, structure, and weight the criteria used for selecting optimal hydrogen refueling station locations. Although independent validation remains unattainable due to the absence of certain operational data, the study’s findings can support transport, energy, and climate strategies, with robustness defined strictly as internal stability and cross-method consistency. Full article

Road safety authorities each hold accident records that, when combined, could train stronger severity prediction models, yet these records can be neither centralized, for privacy and governance reasons, nor naively merged, because jurisdictions encode severity under incompatible ontologies. This paper recasts that impasse as an information fusion problem and fuses model updates from multiple road safety data silos into a single severity model while every raw record stays at its source. Three components act together: model-level fusion under differential privacy, a reliability-weighted aggregation rule that trusts each source based on its measured quality rather than its size, and a per-source centered logit adjustment layer that reconciles mismatched label priors without double-correcting the shared class imbalance. The primary evaluation is a clean cross-silo setting: five United States state datasets (US Accidents) that share one severity ontology but are held by distinct custodians. Here, over five seeds with 95% confidence intervals, effect sizes, and Holm–Bonferroni correction, private fusion recovers most of a centralized upper bound while keeping data local ($0.599$ balanced accuracy versus $0.624$ when centralized and $0.544$ when local-only), and reliability-weighted fusion attains the highest macro F1 of all methods ($0.567$). Reliability weighting yields a small but consistent robustness advantage under privacy noise; in leave-one-state-out transfer, its improvement over uniform averaging is large on every held-out state but, after Holm correction, survives in two out of five cases. Crucially, we also report a boundary honestly; a United Kingdom source that encodes injury severity—an ontologically different target from the US traffic impact scale—is used as a deliberate out-of-ontology transfer stress test, and a transfer to it collapses to chance ($0.50$, $p=0.62$). Two further honest results are reported: alignment raises accuracy everywhere but does not close the across-source gap, and a membership inference attack reveals no measurable leakage for differential privacy to remove. Full article

Coworking spaces have become an increasingly important component of contemporary urban economies, yet their spatial organisation remains poorly understood in rapidly developing Gulf cities. This study examines the spatial clustering and urban correlates of coworking space locations in Jeddah, Saudi Arabia. A dataset of 53 coworking spaces was integrated with points of interest, built-up land, and road-network data within a GIS framework. Spatial pattern was evaluated using Monte Carlo nearest-neighbour analysis, kernel density estimation, and Ripley’s L-function. A 1 km grid was subsequently used to model coworking space presence through binary logistic generalised linear regression, with a 2 km grid used for sensitivity analysis. Coworking spaces exhibited pronounced spatial clustering: the observed mean nearest-neighbour distance was 1023 m compared with 2667 m under complete spatial randomness (nearest-neighbour ratio = 0.384, Monte Carlo p = 0.001). The observed centred Ripley’s L-function exceeded the upper pointwise 95% CSR simulation envelope throughout the evaluated 0.25–10 km range, indicating multiscale spatial concentration. In the final 1 km model, business density (OR = 2.13, p < 0.001), built-up proportion (OR = 1.042 per percentage point, p < 0.001), and road density (OR = 1.075, p = 0.008) were positively associated with coworking space presence. Business density and built-up proportion remained significant at the 2 km scale, whereas the road-density association weakened. These findings suggest that coworking spaces in Jeddah are primarily embedded within business-intensive, highly urbanised, and accessible parts of the metropolitan area while also demonstrating sensitivity to spatial analytical scale. Full article

Background: Our goal was to evaluate the radiological patterns and clinical outcomes of brain injuries related to trauma, offering insight into the distribution of brain injuries, facial fractures and outcomes across road traffic accidents (RTAs), falls and other causes. Methods: Cerebral CT scans of 536 cases obtained between January 2019 and January 2024 were retrospectively reviewed. The collected data included demographics, etiology, transportation groups (driver, car occupant, pedestrian, motorcyclist, bicyclist), GCS (Glasgow Coma Scale), clinical outcome (ICU (intensive care unit) admission, neurosurgical intervention, mortality) and CT imaging features. Images were analyzed independently by two physicians. The findings reflect a selected population of polytrauma patients who underwent cerebral CT at a single emergency hospital. Results: Brain injury was identified in 39% of all subjects, the most frequent type of lesion being intraparenchymal hematoma (24%), followed by subdural hematoma (23%). Brain injury was numerically more frequent among patients with falls (45%) than among those involved in RTAs (37%), although this difference did not reach statistical significance in either unadjusted or age-, sex-, and mechanism-adjusted analysis (p > 0.05). CT patterns differed across transportation categories, with bicyclists showing associations with intraparenchymal hemorrhage and diffuse brain swelling and pedestrians with intraventricular hemorrhage (p < 0.05). However, given the small bicyclist subgroup (n = 6), these findings should be considered exploratory. Car occupants showed lower unadjusted odds of brain injury compared with drivers (OR: 0.50; 95% CI: 0.30–0.80), but this association was attenuated and no longer statistically significant after adjusting for age and sex (adjusted OR: 0.55; 95% CI: 0.27–1.15; p = 0.112). In multivariable analysis of in-hospital mortality, only age and severe GCS category remained independently associated with death; neither injury mechanism nor transportation category showed an independent association once these factors were accounted for. Conclusions: Brain injury was more frequently observed among patients with falls than among those involved in RTAs. Nevertheless, road traffic accidents accounted for the majority of polytrauma admissions. These findings highlight the importance of both road traffic safety measures and fall-prevention strategies, particularly among older adults. Full article

Young and novice drivers have the highest representation of driver injuries and fatalities. While driver education provides in-class and in-car exposure to the driving environment, the use of driving simulators can further expose challenging driving environments that are not typically provided as part of current driver education programs. The aim of this study was to examine and compare the delivery of a driver training program on simulated driving performance and confidence in young drivers. Sixty participants (aged 15–20; 66% male) completed cognitive and visual assessments, a driving comfort scale, and a pre-test simulated drive. Subsequently, participants were assigned to: (1) an intervention with feedback group (n = 20), (2) an intervention without feedback group (n = 20), or (3) control group (n = 20). Intervention group participants completed fifteen training scenarios, and all participants completed a post-test simulated drive and driving confidence scale. Outcome measures assessed on the simulator were driving errors (e.g., speed, vehicle position, braking distance) and the number of collisions. Across all groups, simulated crashes significantly decreased and driving confidence significantly increased post-test. Training with feedback produced the most consistent improvements, including improved speed regulation and safer following distances. All participants reported greater confidence in challenging conditions such as heavy rain, snow, icy roads, and night driving. Findings indicate that high-fidelity simulation, particularly when paired with structured feedback, can improve novice drivers’ skills and confidence, supporting simulation as a valuable complement to traditional driver education. Full article

Platform-listed destinations are widely used to represent urban activity opportunities, yet their correspondence with realized activity may vary across local contexts. This study examines destination–activity representation gaps in four bounded local activity systems in Guangzhou, China. Mobile positioning activity, platform-listed destinations, population-positioning surfaces, roads, land cover, and street-view imagery were integrated at a 25 m grid scale. A spatially out-of-fold ridge model estimated activity from destination context and within-area percentiles of observed and destination-predicted activity were used to identify contrasting representation gap types. Destination correspondence was modest overall and spatially uneven across the four cases. Activity-rich/destination-light cells were more prevalent in the two island/peripheral cases, while their intraday profiles resembled those of destination-supported high-activity cells. These cells also showed lower population co-location on average, while contiguous patches revealed substantial local heterogeneity. Roads and land cover provided limited transferable discrimination, while selected raw street-view elements modestly improved annual activity prediction within the central sample. The findings show that platform-listed destinations and realized activity are related but non-equivalent representations and that their divergence varies across local activity systems. Full article

With the development of artificial intelligence and automated driving technologies, traffic signal control is evolving toward greater flexibility and faster response. From the perspective of the Transportation Cyber-Physical System (T-CPS), this paper focuses on mixed traffic scenarios involving connected and automated vehicles (CAVs) and human-driven vehicles (HVs). It proposes integrating a Large Language Model (LLM) into signal control: roadside devices perceive traffic states, prompt engineering is constructed, and the LLM is driven to reason and generate control signals. On this basis, a CAV speed guidance algorithm is proposed. Controlled SUMO simulations of a single isolated intersection under ideal V2X communication assumptions show that the proposed method improves delay performance under the tested mixed-traffic conditions. As the CAV penetration rate increases, traffic performance is further improved. Additional experiments under emergency-vehicle priority, road-construction constraints, different traffic-demand levels, perception noise, and different decision intervals and guidance ranges provide simulation-based evidence of training-free scenario adaptability and robustness within the examined scope. Although inference latency and remote-API delays constrain the timely availability of fresh LLM actions, the hard-deadline policy and deterministic fallback mechanism maintain continuous signal execution and favorable traffic performance in the controlled SUMO simulations. Full article

In the context of the sustainable transformation of the blue food system, aquatic product exports are shaped by trade scale, institutional conditions, and market access requirements. Using an unbalanced panel of China’s aquatic product exports to 39 countries along the Belt and Road from 2006 to 2023, this paper applies the BC95 one-step stochastic frontier gravity model to estimate trade efficiency and export potential. The results show that: (1) Significant trade inefficiency exists, with an average efficiency of 0.3696. (2) In the baseline model, China’s GDP, destination-country GDP and population, and common language are positively associated with exports, while geographical distance is negatively associated with exports. Political stability, government effectiveness, and trade freedom are associated with lower trade inefficiency, whereas destination-country aquatic product production and halal certification requirements are associated with higher trade inefficiency. Robustness tests broadly support the main findings, although the estimates for destination-country population, common language, and government effectiveness are more specification-sensitive. (3) A scale–efficiency matrix classifies 33 export markets into four sample-relative groups. The findings suggest that China’s aquatic product exports should shift from scale expansion toward a sustainable trade development model that places greater emphasis on efficiency, quality, compliance, and stability, with differentiated strategies across export markets. Full article

As electromobility reduces tailpipe emissions, regulatory focus, including the upcoming Euro-7 standard, shifts to non-exhaust emissions (NEEs). Brake wear particulate matter (PM) significantly contributes to urban pollution and severe health risks. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Reviews (ScRs) compliantly reviews systematically mapped research on brake dust generation, measurement methodologies, and mitigation strategies. Following a literature search across Scopus, Web of Science, and EBSCOhost for English and German publications, 125 studies were extracted using the AI tool Elicit and manually verified. The synthesis indicates that coarse particles (PM₁₀ and PM_2.5) originate primarily from mechanical abrasion and tribo-oxidation, while ultrafine particles (UFPs) form via thermal decomposition of organic binders at critical temperature thresholds. For quantification, enclosed inertia dynamometers with constant volume sampling (CVS) show clear convergence as the standard. Effective mitigation includes wear-resistant hard coatings, low-emission pad formulations, active on-board filtration, and enclosed drum or encapsulated wet brakes. Furthermore, regenerative braking in electric vehicles (EVs) can reduce particulate emissions by up to 95% under standardized driving cycles or optimal operating conditions. Despite these advancements, knowledge gaps remain. Future research must prioritize standardizing real-world on-road measurement protocols, enabling wet braking concepts for automotive applications by addressing drag losses and performance limits, and developing and validating coupled predictive models as a basis for future digital twins to design zero-emission braking architectures. Full article

The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to improving the efficiency and sustainability of modern transportation systems. The aim of this paper is to present and systematize the application of modern ITS technologies for improving road safety, reducing emissions, and shortening travel time. Based on an analysis of the relevant literature, the fundamental components and architecture of ITS are presented, including sensor systems, V2X communication, IoT, cloud and edge computing, as well as the application of artificial intelligence in traffic data processing and prediction. The analyzed studies demonstrate that ITS enables dynamic traffic flow management, route optimization, reduction in congestion and emissions, and more efficient responses to emergency situations. Particular attention is devoted to the possibility of simultaneously considering travel time, energy consumption, emissions, noise, and road safety. As a synthesis of the analyzed findings, an integrated algorithm for intelligent traffic management is proposed, operating as a closed feedback loop encompassing data collection, state assessment, prediction, optimization, and control. Future ITS development is expected to focus on the integration of AI, IoT, 6G, and edge computing technologies and their validation using real-world traffic data. Full article

Auxiliary energy consumption rises sharply at low temperatures, reducing the accuracy of driving range prediction and vehicle energy management. Most previous studies have focused on battery electric vehicles, whose operating characteristics do not fully represent the powertrain architecture of range-extended electric vehicles (REEVs). This study analyzes REEV auxiliary energy consumption and develops prediction models for operation at low temperatures. Approximately 3600 km of actual road driving data were collected. Auxiliary energy consumption was examined across four dimensions: power mode, trip scale, thermal management load, and range-extender operating share. Engine waste heat reduced auxiliary energy consumption by more than 59% in range-extended mode compared with pure-electric mode. Random Forest (RF), Least-Squares Boosting (LSBoost), and Multilayer Perceptron (MLP) methods were used to develop a trip-scale auxiliary energy consumption prediction model (trip-scale model) and a second-scale auxiliary energy consumption prediction model (second-scale model). The best test-set R² was 0.826 for the trip-scale model. For the second-scale model, R² increased from 0.857 at 30 s to a maximum of 0.872 at 60 s; considering that doubling the sample duration yielded an R² improvement of only 0.015, the 30 s LSBoost model was selected for subsequent integrated prediction. In the integrated application, the selected model predicted the mean auxiliary power over the remaining trip to estimate the remaining auxiliary energy. Although the departure estimate had a 9.55% error, iterative updates kept the entire estimate close to the measured value, with a maximum absolute residual of 0.022 kWh. Full article

Aggregate livability indices consistently rank Fargo, North Dakota, among the most livable small-to-mid-sized U.S. cities, yet city-level scores obscure whether favorable average performance reflects equitable neighborhood-level access to essential amenities. However, evidence on whether favorable citywide livability rankings correspond to equitable neighborhood-level access in small- and mid-sized U.S. cities remains limited. To address this gap, this study conducts a tract-level spatial accessibility analysis in Fargo, examining access to grocery stores, healthcare facilities, and parks across 38 census tracts using road network-based nearest-facility assignment, a Composite Accessibility Index (CAI), and Gini coefficients. Results reveal that despite strong aggregate rankings, approximately 9700 residents (8.0% of the study-area population of 121,219) reside in the bottom composite accessibility quartile. Standard Gini coefficients ranged from 0.081 to 0.093, while population-weighted Gini coefficients were lower (grocery = 0.040; healthcare = 0.042; parks = 0.029; CAI = 0.029), indicating modest citywide inequality. Nevertheless, the worst-served peripheral tract recorded a CAI of 0, with network distances ranging from 6.9 to 7.8 miles across the three amenity types. A sensitivity analysis confirms robustness across threshold choices. These findings indicate that modest citywide inequality can coexist with localized accessibility disadvantages in peripheral tracts, supporting spatially disaggregated, equity-oriented livability assessment. Full article