Search Results (5,483)

Temporary sidewalk disruptions caused by construction, maintenance, or other obstructions can significantly affect pedestrian mobility, particularly for those with mobility impairments. Although accessibility standards and universal design principles exist, temporary pedestrian detours do not always provide safe, continuous, and accessible routes, creating barriers to independent mobility. This study examines how municipalities plan, approve, and monitor sidewalk detours, focusing on accessibility. A qualitative descriptive approach was used, combining a review of guidance documents and grey literature with semi-structured interviews with seven professionals from five municipalities and one transportation organization in Québec, Canada (April–August 2024). Thematic analysis allowed the identification of four main themes concerning the detour design process, the criteria used, accessibility-related challenges, and the use of geospatial technology. The findings show that pedestrian safety and continuity of movement are central concerns, while accessibility is considered unevenly across organizations. Organizations in larger cities described more detailed accessibility-related criteria, such as width, slope, surface condition, curb ramps, and corridor delineation, but even this level of detail did not translate into consistent application. They indicated that applying these criteria is sometimes hindered by existing infrastructure, limited space, time constraints, variable guidance application, and a lack of decision-support tools. These findings provide an empirical basis for understanding current detour design practice and for developing geospatial decision-support tools for accessible detour planning. Full article

Woody biomass is often promoted as a low-carbon energy source in global decarbonization efforts. However, LCA (life cycle assessment) evaluations of woody biomass-to-energy systems show very different environmental performance. Variations in technology and methodology across investigations can cause these inconsistencies. This review paper analyzes methodologies of LCAs of woody biomass conversion routes such as combustion, combined heat and power, gasification, pyrolysis, torrefaction-assisted systems, and new bioenergy with carbon capture configurations. A systematic literature review was conducted using Scopus, SpringerLink, and ScienceDirect, identifying 4272 records, of which 98 studies were retained for detailed analysis following the application of defined inclusion and exclusion criteria. Functional unit selection, from biomass mass per unit to power or heat per unit, is highly variable, affecting comparability. Forest carbon stock fluctuations, infrastructure, and end-of-life treatment are inconsistently included in cradle-to-grave system boundaries. Static GWP100 methods are often used in biogenic carbon removal without considering temporal carbon dynamics. The importance of pretreatment steps like drying, pelletizing, and torrefaction cannot be overstated, even though they have a direct impact on the quality of the fuel, the efficiency of transportation, and the effectiveness of the conversion process downstream. The large range of stated emission levels for comparable technologies is further influenced by logistics assumptions, plant scale, and allocation mechanisms in cogeneration systems. The review synthesizes these methodological differences and proposes a harmonization methodology to increase woody biomass LCA transparency and comparability. By identifying important sources of outcome variability, this study helps policymakers, project developers, and industry stakeholders evaluate biomass energy investments and bring clarity to environmental decisions. Full article

Coupling coordination degree (CCD) between persistent late-night radiance and population density may vary nonlinearly with urban functional density, yet linear and single-city analyses cannot distinguish shared from city-specific patterns. A method-controlled dual-city XGBoost-SHAP framework was applied to 14,215 H3 cells in Shanghai and 31,067 in Beijing using six point-of-interest density factors. XGBoost outperformed OLS, with random hold-out R² values of 0.899 and 0.918 vs. 0.617 and 0.626. Public service (X2) ranked first in both cities, accounting for 39.77% and 58.73% of total mean absolute SHAP magnitude. The secondary hierarchy diverged as follows: commercial finance (X3) ranked second in Shanghai at 27.91% and formed the strongest interaction with X2, whereas transport infrastructure (X6) ranked second in Beijing at 18.63% and formed the strongest interaction with X2. Nonlinear analysis identified reproducible negative-to-positive crossings for X2, X3, and X6, peak-type responses for X4 and X5, and no stable second saturation threshold. Spatial OOF, grid, rank, LOWESS, and residual checks supported the leading-factor contrast while showing scale sensitivity and residual spatial dependence. The results identify a common leading attribution alongside city-specific secondary, nonlinear, and spatial patterns within the two observed megacities. Full article

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

Corrosion of reinforcing steel remains a major cause of premature deterioration in concrete infrastructure, motivating the development of inhibitors with lower toxicity and reduced environmental impact. This review critically examines recent advances in green and bio-based corrosion inhibitors for reinforced concrete, including plant extracts, agro-industrial residues, naturally occurring organic compounds, proteins, polysaccharides, bio-based coatings, hybrid formulations, and microbial systems. The available evidence is synthesized in terms of chemical functionality, delivery route, adsorption and film-forming mechanisms, electrochemical response, compatibility with cementitious materials, and durability under chloride- and carbonation-related exposure. Many formulations provide substantial inhibition under optimized laboratory conditions through interfacial adsorption, coordination with iron species, passive-film stabilization, suppression of anodic and cathodic reactions, and restriction of aggressive-species transport. However, reported efficiencies are not directly comparable because experimental scale, exposure conditions, dosage, steel preparation, and calculation methods vary considerably. Moreover, long-term reinforced-concrete and field studies remain scarce, while extract standardization, cement compatibility, toxicity, biodegradability, and life-cycle performance are frequently insufficiently addressed. Green and bio-based inhibitors therefore represent a promising but heterogeneous technology class. Their practical implementation requires chemically reproducible formulations, complementary electrochemical and surface evidence, concrete-scale durability assessment, environmental validation, and stage-gated progression toward monitored field applications. Full article

Dust storms are a major environmental hazard across the Arabian Peninsula, affecting air quality, human health, transportation, and infrastructure. Despite their frequency in Riyadh, event-based studies integrating aerosol optical observations, atmospheric transport analyses, potential source-region identification, and surface validation remain limited. This study characterized dust events over Riyadh during 2024–2025 using AERONET aerosol observations, NOAA Integrated Surface Database (ISD) meteorological records, HYSPLIT backward trajectories, and potential source contribution function and concentration-weighted trajectory analyses. Dust events were identified using a dual optical criterion based on aerosol optical depth (AOD) at 500 nm (AOD₅₀₀ ≥ 0.50) and the 440–870 nm Ångström exponent (≤0.50) and were compared with co-located NOAA ISD present-weather and visibility observations. Among the 34 identified dust events, 82% occurred between March and May. The events were dominated by coarse-mode aerosols, with a mean Ångström exponent of 0.28 and a mean fine-mode fraction of 0.26. Same-day dust-related present-weather reports were recorded for 26 events (76%), while the remaining eight events (24%) were classified as elevated because no dust-related present-weather code was reported at the co-located ISD station during the corresponding event day. The trajectory and source–receptor analyses showed a consistent spatial association with the Mesopotamian Basin as a major potential source region and the northern Rub’ al-Khali as a secondary potential source region. Surface wind speed was significantly and positively associated with coarse-mode aerosol loading and dust-event severity, whereas sea-level pressure showed no statistically significant association with severity. These findings provide an updated observational characterization of dust events affecting Riyadh during 2024–2025 and demonstrate the complementary value of aerosol optical observations, co-located surface records, atmospheric transport analysis, and source–receptor methods for examining dust-event characteristics and transport over central Saudi Arabia. Full article

Road surface hazards remain a persistent concern for vehicle safety, passenger comfort, and the operational continuity of transport infrastructure. Among these hazards, potholes are particularly significant because they can cause tire damage, suspension wear, wheel misalignment, and sudden vehicle instability. In addition to direct mechanical damage, potholes may reduce driving comfort, increase maintenance costs, and degrade traffic efficiency in urban environments where roads are heavily used and rapidly deteriorate. For these reasons, the timely detection of potholes is an important requirement for road safety and infrastructure management. This work presents a tailor-made dataset for road pothole detection in urban environments, referred to as the Bridgestone Dataset (BS Dataset). The dataset was designed to support object detection from vehicle-mounted imagery collected from a test vehicle under realistic road conditions, thereby aligning the training data more closely with the target deployment scenario. The resulting dataset is intended to support real-time monitoring systems for road hazard detection and maintenance planning. The dataset was also designed as a multimodal resource. In addition to pothole bounding-box annotations, it provides accelerometer and GPS signals to characterize the vehicle dynamics during operation which might help identifying hazard severity and the potential risk to the vehicle. To collect the dataset, the authors developed a smartphone application, which supports the acquisition of both images and vehicle telemetry by leveraging the device’s internal sensors. Full article

Trespassing on railway tracks represents the main cause of accidents in railway transport, often resulting in severe injuries and fatalities. Understanding railway trespassing requires integration of multiple data sources, as accident data alone do not capture its spatial and behavioural characteristics. This study identifies key parameters (variables) for developing a QGIS-based trespassing database. It represents the first step toward creating a comprehensive risk assessment tool for the railway network’s susceptibility to trespassing and the potential risk of trespass-related accidents. The methodology integrates secondary data sources, including accident records, railway infrastructure data, and demographic and socioeconomic data, with primary data collected at trespassing locations (GPS coordinates, behavioural observations, patterns, and environmental characteristics). A key challenge was integrating data from multiple sources into a unified database suitable for GIS spatial analysis. The results include mapping trespassing locations by kilometre position and GPS coordinates, as well as creating a structured multimodal database containing detailed information. The developed database enables spatial analysis, supports the identification of trespassing hotspots and provides a foundation for trespassing prevention. Full article

Urban mobility systems generate large volumes of heterogeneous geospatial data that differ in temporal resolution, spatial coverage, update frequency, and semantic structure, making integrated near-real-time analysis difficult. This paper presents a geospatial big-data framework for integrating and analyzing multimodal urban mobility data from the Norwegian transport ecosystem, including public transport, micromobility, road infrastructure, weather sensing, and civil aviation. The framework is implemented as a modular pipeline for data ingestion, source-specific normalization, temporal alignment, and analytical processing, enabling minute-level comparison across heterogeneous operational feeds. The proposed approach preserves source-level semantics while supporting unified spatiotemporal analysis across transport modes with different operational characteristics. The framework is evaluated through analytical scenarios focused on peak and off-peak mobility dynamics, weather-related multimodal variability, and spatial autocorrelation of public transport activity and delay across four analysis windows and six Norwegian cities. The results show that mobility–weather relationships vary across transport modes and temporal windows, particularly in public transport activity, cycling behavior, and delay patterns, and that spatial clustering of public transport activity and delay is itself city- and window-dependent, with some cities showing strong, stable clustering and others showing none. The findings indicate that multimodal urban mobility should be interpreted as a context-dependent and interconnected spatiotemporal system rather than through isolated modal indicators. The study demonstrates how geospatial big-data integration can support near-real-time urban mobility monitoring and operational analytics in smart-city environments. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

This study presents a comprehensive spatial analysis of the public transport network across the Almaty agglomeration, encompassing 211 routes within eight urban districts and seven adjacent administrative divisions. Drawing on digitized geodata from 3634 unique bus stops, the research methodology integrates quantitative and qualitative spatial metrics, including distribution density, average stop spacing, elevation gradients, and topological connectivity. The analysis highlights significant territorial imbalances: while 70.53% of all unique stops are concentrated within Almaty’s city limits, the surrounding regional network is highly fragmented, with an average stop spacing of 2579.53 m. Furthermore, topographic assessments confirm substantial operational challenges for north–south routing due to steep elevation changes, reaching 984.48 m within the city and 1243.23 m regionally. A critical evaluation of topological connectivity reveals that 44.63% of suburban bus stops lack transfer intersections, underscoring severe deficits in peripheral public transport provision. By assessing 56 distinct connection types, the study categorizes administrative districts based on their route integration levels. Ultimately, the derived spatial parameters offer a robust evaluation of the current transport framework. These insights establish a crucial scientific and empirical foundation for optimizing route geometries, bridging infrastructural gaps, and guiding sustainable transit planning in alignment with Almaty’s transition toward a polycentric urban model. Furthermore, the developed 3D spatial-topological framework provides a scalable, data-driven blueprint for municipal authorities to prioritize infrastructure investments, deploy multimodal hubs and enhance transit equity in other rapidly growing and topographically complex agglomerations worldwide. Full article

The logistics industry remains a critical bottleneck for decarbonization due to its extensive physical networks and high-carbon path dependencies. To address the spatial heterogeneity and transition constraints in the logistics industry of China, this study proposes a Performance–Topology–Mechanism (PTM) framework. Analyzing panel data from 30 Chinese provinces, we integrate a Super-SBM model with the Global Malmquist–Luenberger (GML) index, Dagum Gini decomposition, and a panel Tobit model to decode the spatiotemporal dynamics of green innovation performance (GIP) and its underlying spatial lock-in mechanisms. The results reveal a steady but spatially uneven increase in the national GIP (from 0.4526 to 0.5724), characterized by a leading East and a lagging West/Northeast. GML decomposition indicates this growth is predominantly driven by outward shifts in the technological frontier rather than efficiency improvements, highlighting the weak spatial conversion of green technologies into transport optimization. Topological tracing demonstrates that inter-regional disparities have become the dominant source of spatial inequality, with their contribution rising from 60% to 75%. Spatial autocorrelation further exposes a deepening core–periphery polarization and persistent low-performance spatial lock-in. Crucially, the mechanism analysis identifies a pronounced infrastructure-based carbon lock-in. While economic capacity and green patents stimulate GIP, road network density exerts a significant negative effect, reflecting a systemic path dependence on high-carbon road freight. The findings suggest that decarbonizing transport logistics requires shifting from singular technological investments toward multimodal transport restructuring, overcoming physical network dependencies, and promoting the regional diffusion of green innovations. These findings provide an empirical basis for differentiated green-logistics policies, coordinated low-carbon transport infrastructure planning, and cross-regional diffusion of green technologies. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article

The increasing adoption of hydrogen as an energy carrier requires advanced monitoring solutions for transport infrastructures, where intelligent sensing and data-driven analysis can play a key role in improving safety and operational efficiency. However, anomaly detection in hydrogen transport networks remains challenging due to the limited availability of operational data and the complexity of transient behaviors associated with these systems. This work investigates a weakly-supervised anomaly detection framework for hydrogen transport networks based on high-fidelity simulation and data-driven analysis. The proposed methodology combines temporal deep learning architectures and unsupervised representation learning models with an operational threshold calibration strategy based on the trade-off between false positives and false negatives. The proposed framework is validated using a high-fidelity simulation environment that reproduces normal and anomalous operating conditions, including leaks, compressor malfunctions, and delayed activation events. The framework is evaluated through comparative experiments involving different anomaly detection architectures, robustness analysis under measurement noise, and leave-one-topology-out generalization tests. Results demonstrate that the proposed approach can effectively identify abnormal behaviors while maintaining robustness against degraded signal quality and previously unseen operating configurations. The obtained results highlight the effectiveness of the proposed methodology as a framework for developing and validating intelligent monitoring strategies for hydrogen transport infrastructures. Full article