Search Results (192)

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

The October 2024 cut-off low event triggered one of the most catastrophic floods recorded in the Valencia Metropolitan Area, exposing significant vulnerabilities in urban planning, infrastructure resilience, and emergency preparedness. This study presents a novel comprehensive assessment of the event, using a multi-sensor satellite approach combined with socio-economic and infrastructure data at the metropolitan scale. It provides a comprehensive spatial assessment of the flood’s impacts by integrating of radar Sentinel-1 and optical Sentinel-2 and Landsat 8 imagery with datasets including population density, land use, and critical infrastructure layers. Approximately 199 km² were inundated, directly affecting over 90,000 residents and compromising vital infrastructure such as hospitals, schools, transportation corridors, and agricultural lands. Results highlight the exposure of peri-urban zones and agricultural areas, reflecting the socio-economic risks associated with the rapid urban expansion into flood-prone plains. The applied methodology demonstrates the essential role of multi-sensor remote sensing in accurately delineating flood extents and assessing socio-economic impacts. This approach constitutes a transferable framework for enhancing disaster risk management strategies in other Mediterranean urban regions. As extreme hydrometeorological events become more frequent under changing climatic conditions, the findings underscore the urgent need for integrating remote sensing technologies, early warning systems, and nature-based solutions into regional governance to strengthen resilience, reduce vulnerabilities, and mitigate future flood risks. Full article

Historical towns embody the origins and continuity of urban civilization, preserving distinctive spatial fabrics, cultural lineages, and latent economic value within contemporary metropolitan systems. Their integrated conservation directly aligns with SDG 11.4, and advances the holistic preservation objectives of historic urban landscapes (HULs). However, achieving these objectives cannot be solely dependent on modern remote sensing technologies; it necessitates the integration of historical geographic information system (HGIS) theoretical frameworks and methodological approaches. Leveraging HGIS and multisource data—including SDGSAT-1 nighttime light imagery, textual documents, and historical maps—this study reconstructed the spatial extent of historical towns in suburban Shanghai and assessed their present-day economic vitality through light-based spatial proxies. Key results comprised the following. (1) Most suburban historical towns are small, yet nighttime light intensity varies markedly. Jiading County, Songjiang Prefecture, and Jinshan Wei rank highest in both spatial extent and brightness. (2) Town area exhibits a strong positive relationship (R² > 0.80) with the total nighttime light index, indicating that larger settlements generally sustain higher economic activity. (3) Clusters of “low area–low light” towns showed pronounced intra-regional disparities in economic vitality, underscoring the need for targeted revitalization. (4) Natural setting, historical legacy, policy interventions, and transport accessibility jointly shape development trajectories, with policy emerging as the dominant driver. This work demonstrates a transferable framework for multidimensional assessment of historical towns, supports differentiated conservation strategies, and aids the synergistic integration of heritage preservation with regional sustainable development. Full article

The COVID-19 pandemic has caused a profound interruption in the way people travel and has had a very negative impact on tourism and economics throughout the world, especially on the coastal fishing communities in South Korea. These previously problematic areas, having suffered a decrease in the local population as well as stood in the midst of the economic downturn, experienced a great cut in the number of tourists coming from far away, which additionally caused their collapse of resilience and sustainability. This research investigates the recovery trends of 45 seashore-fishing districts in South Korea and how the change in travel distance and the number of visitors before and after the pandemic have affected these trends. Through the utilization of big data from the Korea Tourism Data Lab (2019–2023) and Geographic Information System (GIS) analysis, we observe the changes in visitor flows, use the indices of resilience as an indicator to measure them, and investigate how proximity affects travel recovery. The survey results indicate that the regions neighboring metropolitan zones were not only the ones that suffered the most from travel distance during the pandemic but also experienced quick recovery after the pandemic. The new promotional campaigns, in tandem with an improved network of transportation, contributed to the swift recovery of these areas. The remote areas, on the other hand, persist in fighting the problems of regionalized tourism and have only limited accessibility. The proposition of “distance-dependent resilience” theory as well as the Blue Urbanism framework is offered in order to bring up the ideas of sustainable tourism and population stabilization. The study is expected to serve as a cornerstone for the practice of adaptive governance and strategic planning in the matter of the coastal areas after the pandemic. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article

Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded correlations. Notably, improved visual walkability in specific zones reduces reliance on ride-hailing by facilitating sustainable alternatives. These findings provide empirical support for optimizing urban infrastructure and land-use policies to promote equitable mobility systems. The proposed methodology offers a replicable framework for assessing transportation–land-use interactions, informing targeted interventions to achieve metropolitan sustainability goals through coordinated spatial planning and pedestrian-centric design. Full article

The objective of this study is to explore the cold chain system in a metropolitan area, focusing on the overall system cost encompassing both distribution centers and transportation. The research delves into the planning of urban cold chain systems, considering fluctuating minimum customer demands, the traffic conditions of potential new centers, and the variability in carbon-trading prices. To manage the complexity of these objectives and inherent uncertainties, we introduce a flexible chance-constrained programming model for the cold chain system (FCCP-CCS). An FCCP-CCS programming model is developed to address the multifaceted goals and various uncertainties. The effectiveness of this model is validated through experimental analysis using real-world data from a major city’s cold chain system. The findings of this study reveal several key insights: (1) The levels of confidence and satisfaction significantly impact system optimization, with higher levels leading to increased consumption. (2) Customer demand variations would determine the transportation and the potential new centers in the system. (3) The surroundings of a distribution center partly indicate its service quality. (4) Governmental adjustments in carbon-trading prices can effectively enhance the overall sustainability of the urban cold chain system. This research highlights the importance of optimization in designing and managing urban cold chain systems, particularly in environmental sustainability. Full article

Public transportation wait times are a crucial factor influencing users’ perception of the system’s efficiency, their satisfaction, and their willingness to continue using these services. This study analyzes long wait times in public transportation and identifies the most affected users in the Metropolitan Area of Valparaíso, Chile. Using data from the Gran Valparaíso Mobility and Transportation Survey conducted by the Transportation Planning Secretariat (SECTRA) in 2014, only public transportation trips were selected, resulting in a dataset of 17,951 records. Exploratory data analysis techniques and Artificial Intelligence algorithms, such as DBSCAN clustering, were applied, as well as Moran’s Index for spatial autocorrelation, in order to identify patterns and groups of users experiencing prolonged wait times. The results show that certain demographic groups and specific geographic areas face longer wait times, negatively impacting equity and accessibility within the public transportation system. This study provides insights for improving transportation planning by identifying patterns and user groups that experience extended wait times, which can guide decisions to enhance user satisfaction and promote the use of public transportation. Full article

Budapest and its metropolitan area serve as a key railway hub both within Hungary and across Europe, intersected by multiple European rail corridors and characterized by substantial suburban traffic driven by daily commuters from surrounding areas. The Budapest agglomeration is served by 11 rail lines to Budapest managed by the MÁV Group Company (MÁV: Magyar Államvasutak Co., Budapest, Hungary) is a railway company owned by the Hungarian state). The majority of these are high-capacity, mostly double-track electrified main lines, which play a major role in long-distance and international transport. The main goal of the MÁV Group Company is the continuous development of the quality of passenger transport in Hungary and Europe, quality improvement in passenger comfort, sales, and passenger information systems, and the introduction of up-to-date, environmentally friendly means and solutions. Infrastructure plays a decisive role in the development and transformation of the country and its regions, municipalities, and settlement systems. The development of transport infrastructure not only dynamically transforms and shapes spatial structures but also initiates processes of internal differentiation. In our study, statistical analysis of municipalities and rail-based public transport confirmed a positive correlation between the modernization of transport infrastructure and selected demographic indicators. Full article

Concrete demolition waste represents a critical bottleneck in achieving a circular economy for the construction sector. This study develops a system-dynamics model that couples material flows with economic and logistical feedback to quantify how cost structures affect concrete recycling in the Sydney (Australia) metropolitan area. The model is calibrated with (i) official New South Wales 2020–2021 construction-and-demolition waste statistics, (ii) concrete consumption data scaled from state infrastructure reports, and (iii) parameters elicited from structured interviews with recycling contractors and plant operators. Scenario analysis systematically varies recycling-plant fees, landfill levies, and transport costs to trace their nonlinear impacts on three core performance metrics: recycling rate, cumulative landfill mass, and virgin gravel extraction. Results reveal distinct cost tipping points: a 10% rise in landfill-logistics costs or a 25% drop in recycling logistics costs shifts more than 95% of concrete waste into the recycling stream, cutting landfill volumes by up to 47% and reducing virgin aggregate demand by 5%. Conversely, easing landfill costs by 25% reverses these gains, driving landfill dependency above 99% and increasing gravel extraction by 39%. These findings demonstrate that carefully calibrated economic levers can override logistical inefficiencies and accelerate circular construction outcomes. The system-dynamics framework offers policymakers and industry stakeholders a decision-support tool for setting landfill levies, recycling subsidies, and infrastructure investments that jointly minimize waste and conserve natural resources. Full article

In recent years, with the rapid socioeconomic development of Wuxi City, the frequent occurrence of severe air pollution events has attracted widespread attention from both the local government and the public. Based on the real-time monitoring data of criteria pollutants and GDAS (Global Data Assimilation System) reanalysis data, the spatiotemporal variation patterns, meteorological influences, and potential sources of major air pollutants in Wuxi across different seasons during 2019 (pre-COVID-19) and 2023 (post-COVID-19 restrictions) are investigated using the Pearson correlation coefficient, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models. The results demonstrate that the annual mean PM_2.5 concentration in Wuxi decreased significantly from 39.6 μg/m³ in 2019 to 29.3 μg/m³ in 2023, whereas the annual mean 8h O₃ concentration remained persistently elevated, with comparable levels of 104.6 μg/m³ and 105.0 μg/m³ in 2019 and 2023, respectively. The O₃ and particulate matter (PM) remain the most prominent air pollutants in Wuxi’s ambient air quality. The hourly mass concentrations of criteria pollutants, except O₃, exhibited characteristic bimodal distributions, with peak concentrations occurring post-rush hour during morning and evening commute periods. In contrast, O₃ displayed a distinct unimodal diurnal pattern, peaking between 15:00 and 16:00 local time. The spatial distribution patterns revealed significantly elevated concentrations of all monitored species, excluding O₃, in the central urban zone, compared to the northern Taihu Lake region. The statistical analysis revealed significant correlations among PM concentrations and other air pollutants. Additionally, meteorological parameters exerted substantial influences on pollutant concentrations. The PSCF and CWT analyses revealed distinct seasonal variations in the potential source regions of atmospheric pollutants in Wuxi. In spring, the Suzhou–Wuxi–Changzhou metropolitan cluster and northern Zhejiang Province were identified as significant contributors to PM_2.5 and O₃ pollution in Wuxi. The potential source regions of O₃ are predominantly distributed across the Taihu Lake-rim cities during summer, while the eastern urban agglomeration adjacent to Wuxi serves as major potential source areas for O₃ in autumn. In winter, the prevailing northerly winds facilitate southward PM_2.5 transport from central-northern Jiangsu, characterized by high emissions (e.g., industrial activities), identifying this region as a key potential source contribution area for Wuxi’s aerosol pollution. The current air pollution status in Wuxi City underscores the imperative for implementing more stringent and efficacious intervention strategies to ameliorate air quality. Full article

This study investigates the relationship between intercity transportation accessibility and network centrality across South Korea by integrating Global Positioning System (GPS)-based mobility data with graph-theoretic centrality measures, including degree, PageRank, local clustering coefficient, harmonic, Katz, and information centrality. Employing both statistical modeling and machine learning techniques, this analysis uncovers key structural patterns and interaction effects within the national mobility network. The findings yield several important insights. First, the Seoul Metropolitan Area emerges as the dominant mobility hub, with Busan, Daegu, and Daejeon functioning as secondary centers, reflecting a polycentric urban configuration. Second, intermediary transfer hubs—despite having lower direct connectivity—substantially enhance overall network efficiency and interregional mobility. Third, transportation accessibility, particularly in relation to regional transit and highway infrastructure, exhibits a significant association with centrality measures and strong feature importance, identifying these modes as primary determinants of spatial connectivity. Fourth, the impact of accessibility on centrality is characterized by nonlinear relationships and threshold effects. By elucidating the complex interplay between mobility infrastructure and spatial network dynamics, this study contributes to a more comprehensive understanding of regional connectivity and network centrality and offers policy-relevant insights for future transportation planning. Full article