Search Results (255)

With the increasing frequency of precipitation events under global warming, understanding rainfall-induced disruptions to urban mobility has become increasingly important. While prior studies primarily focus on road traffic, the lagged and threshold effects of rainfall on urban rail transit (URT) passenger flow remain insufficiently explored. This study analyzes 109 days of automatic fare collection data from Tianhe District, Guangzhou, in combination with hourly meteorological records and station-level built environment attributes. A rainfall threshold-aware gradient boosting framework is proposed to capture nonlinear response regimes, and an explainable learning approach is used to quantify the relative importance of rainfall, temporal factors, and built environment characteristics. The proposed framework outperforms the baseline model, with the root mean squared error (RMSE) and mean absolute error (MAE) reduced by over 5.38% and 5.93%, respectively. Results further indicate that lagged rainfall intensity exerts the strongest influence on passenger flow variation, with impact magnitudes varying systematically across station types. These findings enhance understanding of the nonlinear, time-dependent effects of rainfall on URT demand and provide practical guidance for passenger flow management and operational planning under rainfall conditions. Full article

Traffic flow anomalies represent significant deviations from normal traffic behavior and disrupt the smooth operation of transportation systems. These may appear as unusually high or low traffic volumes compared to historical trends. Unexpectedly high volume can lead to congestion exceeding usual capacity, while unusually low volume might indicate incidents like road closures, or malfunctioning traffic signals. Identifying and understanding both types of anomalies is crucial for effective traffic management. This paper presents a clustering based approach for enhanced characterization of anamolies in traffic flows. Anomalies in traffic patterns are determined using three anomaly detection techniques: Elliptic Envelope, Isolation Forest, and Local Outlier Factor. These anomalies were newly detected in this work on the Montréal dataset after preprocessing, rather than directly reused from earlier studies. These methods were applied to a dataset that had been pre-processed using windowing techniques with different configuration settings to enhance the detection process. Then, to leverage the detected anomalies, we utilized clustering algorithms, specifically k-means and hierarchical clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. Full article

Urban floods frequently occur in Southeast Asian cities, causing extensive road disruptions and a significant decline in overall urban mobility. To effectively adapt to such conditions, it is crucial to understand how residents modify their travel behavior and daily activities during flood events. This study investigates these behavioral changes by comparing individual travel behaviors and activities under normal and flooding conditions, based on an Activity Diary Survey conducted in Kudus, Indonesia. The comparative analysis reveals that during floods, individuals tend to reduce non-essential activities and limit travel to essential purposes such as work and education. The findings from chi-square tests and applying the RF (random forest) model indicate that socioeconomic characteristics—particularly age, license, income, and level of flood—significantly influence the likelihood of behavioral change. These results highlight that flood-induced disruptions in mobility are not only physical but also socially differentiated, reflecting disparities in vulnerability and adaptive capacity. Full article

Brake wear particles (BWPs) represent a major source of non-exhaust particulate matter from road traffic, contributing substantially to human exposure, particularly in urban environments. While traditionally associated with coarse and fine fractions, mounting evidence shows that brake systems emit large quantities of ultrafine particles (UFPs; <100 nm), which dominate number concentrations despite contributing little to mass. This paper synthesizes current knowledge on BWP formation mechanisms, physicochemical characteristics, environmental behavior, and toxicological effects, with a specific emphasis on UFPs. Mechanical friction and high-temperature degradation of pad and disc materials generate nanoscale primary particles that rapidly agglomerate yet retain ultrafine structural features. Reported real-world and laboratory number concentrations commonly range from 10³ to over 10⁶ particles/cm³, with diameters between 10 and 100 nm, rising sharply during intensive braking. Toxicological studies consistently demonstrate that UFP-rich and metal-laden BWPs, particularly those containing Fe, Cu, Mn, Cd, and Sb compounds, induce oxidative stress, inflammation, mitochondrial dysfunction, genotoxicity, and epithelial barrier disruption in human lung and immune cells. Ecotoxicological studies further reveal adverse impacts across aquatic organisms, plants, soil invertebrates, and mammals, with evidence of environmental persistence and food-chain transfer. Despite these findings, current regulatory frameworks address only the mass of particulate matter from brakes and omit UFP number-based limits, leaving a major gap in emission control. Full article

Supply chain disruptions and climate shocks have exposed the fragility of food systems, highlighting the urgency of reconnecting urban areas with local food production through spatial planning. This study develops a regional-scale ecosystem service (ES)-based foodshed assessment framework, integrating agricultural capacity, ecological functionality, and infrastructure, specifically roads, food industries, and markets. The framework combines the Metropolitan Foodshed and Self-Sufficiency Scenario (MFSS) model with stakeholder-prioritized integrated ES mapping and Geographic Information System (GIS)-based multi-criteria suitability analysis. Applied to Istanbul and the Marmara Region, the assessment focuses on cereals/legumes, vegetables, and fruits/spices under four scenarios projected to 2033. Results show that integrating ESs increases the area classified as suitable by 26%, while infrastructure constraints reduce it to 9%, reflecting the spatial trade-offs between ecological potential and accessibility. Istanbul, with limited agricultural land, achieves self-sufficiency levels below 10% in all scenarios, highlighting its structural dependency. Eliminating food loss and waste reduces regional land demand by 23%. The framework offers policy-relevant insights for conservation, ecological restoration, and decentralized food system development. It remains open to further enhancement through the inclusion of livestock-based systems, updated land cover data, and climate projections, factors essential for assessing long-term resilience. Overall, the ES-based assessment can support food- and ecosystem-sensitive spatial planning in metropolitan regions. Full article

Wildfires increasingly threaten the operation and stability of regional socio-economic systems, where infrastructure, population, and environmental conditions are tightly interconnected. To enhance operational efficiency and strengthen community resilience, this study develops an integrated optimization framework for wildfire evacuation system design based on mixed-integer programming. The model simultaneously determines the locations of primary and secondary shelters and establishes both main and backup evacuation linkages, forming a dual-stage structure that ensures continuous accessibility even under disrupted conditions such as road blockages or fire spread. Wildfire risk indices derived from topographic and environmental data are incorporated to support risk-aware and balanced shelter allocation. A case study of Uiryeong County, South Korea, demonstrates that the proposed framework effectively improves evacuation efficiency and system reliability, producing spatially coherent and adaptive evacuation plans under diverse disruption scenarios. The findings highlight how operation optimization can enhance socio-economic system resilience and sustainability when facing large-scale environmental disruptions. Full article

The increasing frequency and intensity of extreme disasters, exacerbated by climate change, pose significant challenges to sustainable development by disrupting critical infrastructure and hampering relief efforts. Enhancing disaster resilience—a core objective of sustainable development—requires integrated approaches that simultaneously address infrastructure restoration and efficient resource allocation. This study proposes a sustainable optimization framework for post-disaster response, integrating road rehabilitation decisions with emergency logistics planning within a three-tier supply chain network. We develop a mathematical model that synergistically optimizes repair crew scheduling, depot location, and vehicle routing, with the objective of maximizing a comprehensive satisfaction index that balances timely delivery (time satisfaction) and fulfillment of material needs (demand satisfaction). This integrated approach directly contributes to sustainable disaster management by ensuring more reliable and equitable access to vital resources in affected communities. A tailored variable neighborhood search algorithm is designed to solve the model efficiently, as demonstrated through large-scale numerical experiments. Our findings highlight several policy-relevant insights for sustainable emergency planning: adequate budgeting is crucial for uninterrupted relief operations; strategic investments in rapid road repair capabilities or vehicle fleets significantly enhance system efficiency; and prioritizing time satisfaction (rapid response) yields greater overall benefits than merely increasing delivered quantities. Furthermore, restoring critical road infrastructure is shown to mitigate transportation uncertainties, thereby strengthening the resilience of the entire relief system. This work provides a quantifiable methodology and practical decision support tools for building more sustainable and resilient communities in the face of disasters. Full article

To reduce volatile organic compounds (VOCs) emissions from rubber powder/SBS modified asphalt under high temperatures, the key road performance characteristics of asphalt modified with different proportions of rubber powder/SBS were investigated. The optimal rubber powder proportion was determined. The VOCs emission behavior of rubber powder/SBS modified asphalt before and after desulfurization was compared and analyzed using headspace gas chromatography–mass spectrometry (GC-MS). The VOCs emission mechanisms were revealed through microstructural testing. The results showed that the rubber powder/SBS modified asphalt exhibited good road performance with the optimal rubber powder content of 15%. As more rubber powder was added, the total VOCs emissions increased. Desulfurized rubber powder/SBS modified asphalt demonstrated superior performance in controlling harmful VOCs emissions. Undesulfurized rubber powder/SBS modified asphalt released more complex and toxic components compared to desulfurized rubber powder/SBS modified asphalt. The content of xylene was significantly higher than desulfurized rubber powder. Infrared spectroscopy analysis further validated the GC-MS results. Consistency in functional group changes was shown by both methods. Scanning electron microscopy revealed that the original cross-linked network of the adhesive powder was disrupted by desulfurization treatment. Interfacial activity and dispersion of the particles were enhanced. This led to the establishment of a more stable structural system and a reduction in VOCs emissions. Therefore, road performance was ensured, and VOCs emissions were significantly reduced by desulfurized rubber powder/SBS modified asphalt. This expands the utilization rate of waste rubber powder and reduces its environmental pollution. Full article

Global Navigation Satellite Systems (GNSSs) play a critical role in ensuring the safety of modern transportation across all domains, including aviation, road, rail, and maritime navigation. However, recent years have seen a significant increase in radio frequency interference, including signal masking (jamming) and data deception (spoofing) attacks against GNSSs. These threats can severely compromise human safety, disrupt logistics chains, and undermine essential public services. This study offers a structured holistic overview of the most common forms and impacts of GNSS interference. It also presents practical, resilient solutions to reduce vulnerabilities. Both technological (e.g., redundancy, filtering, alternative navigation) and organizational (e.g., regulation, training, risk assessment) strategies are discussed. The findings highlight that building GNSS resilience is not optional—it is necessary to protect transportation systems that rely on satellite navigation. The summary may be of particular interest to legislators, transport authorities, logistics operators, and policymakers. Full article

The latest climate change predictions indicate that the sea level will accelerate in the coming decades as a direct consequence of global warming. This is expected to seriously threaten low-lying coastal areas worldwide, resulting in severe coastal flooding with significant socio-economic impacts, leading to the loss of coastal settlements, exploitable land, and natural ecosystems. The main objective of this study is to provide a first-order preliminary estimation of potential inundation extents along the northern coastline of the Amvrakikos Gulf, a deltaic complex formed by the Arachthos, Louros, and Vouvos rivers in Western Greece, resulting from long-term sea-level rise induced by climate change, using the integrated Bathtub and Hydraulic Connectivity (HC) inundation method. A 2 m resolution Digital Elevation Model (DEM) was used, along with local long-term sea-level projections, for the years 2050 and 2100. Additionally, subsidence rates due to the compaction of deltaic sediments were taken into account. To assess the area’s proneness to inundation caused or enhanced by sea-level rise, the extent of each land cover type, the Natura 2000 Network protected area, the settlements, the total length of the road network, and the cultural assets located within the inundation zones under each climate change scenario were considered. The analysis revealed that under the optimistic SSP1-1.9 scenario of the Intergovernmental Panel on Climate Change (IPCC), areas of 40.81 km² (min 20.34 km², max 63.55 km²) and 69.10 km² (min 41.75 km², max 88.02 km²) could potentially be inundated by 2050 and 2100, respectively. Under the pessimistic SSP5-8.5 scenario, the inundation zone expands to 42.56 km² (min 37.05 km², max 66.31 km²) by 2050 and 84.55 km² (min 67.54 km², max 116.86 km²) by 2100, affecting a significant portion of ecologically valuable wetlands and water bodies within the Natura 2000 protected area. Specifically, the inundated Natura 2000 area is projected to range from 37.77 km² (min 20.30 km², max 46.82 km²) by 2050 to 50.74 km² (min 38.71 km², max 62.84 km²) by 2100 under the SSP1-1.9 scenario, and from 39.34 km² (min 34.53 km², max 49.09 km²) by 2050 to 60.48 km² (min 49.73 km², max 82.5 km²) by 2100 under the SSP5-8.5 scenario. Four settlements with a total population of approximately 800 people, as well as 32 economic facilities most of which operate in the secondary and tertiary sectors and are small to medium-sized economic units, such as olive mills, farms, gas stations, spare parts stores, construction companies, and food service establishments, are expected to experience significant exposure to coastal flooding and operational disruptions in the near future due to sea-level rise. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

To achieve a scientific and efficient emergency response, a dynamic route-planning strategy for emergency vehicles based on government–enterprise collaboration was studied. Firstly, a hybrid evaluation approach was developed, integrating the Analytic Hierarchy Process, Entropy Weight Method, and Gray Relation Analysis-TOPSIS to quantitatively assess the urgency of demands at disaster sites. Secondly, a government–enterprise coordinated route-planning strategy was designed, leveraging the government’s strong mobilizing capabilities and enterprises’ flexible operational mechanisms. Thirdly, to optimize scheduling efficiency, a dynamic route-planning model was proposed, incorporating multiple distribution conditions to minimize scheduling time, delay penalties, and unmet demand rates. A two-stage cellular genetic algorithm was employed to address realistic constraints such as demand splitting, soft time windows, open scheduling, and differentiated services. Numerical simulations of potential flooding in Hunan Province revealed that the collaborative strategy significantly improved performance: the demand satisfaction rate rose from 70.1% (government-led) to 92.3%, while the material transportation time per unit decreased by 23.6% (from 1.61 to 1.23 min/unit). Vehicle path characteristics varied under different operational behaviors, aligning with theoretical expectations. Even under sudden road disruptions, the model maintained a 98% demand satisfaction rate with only a negligible 0.076% increase in system loss. This research fills the gaps in previous studies by comprehensively addressing multiple factors in emergency vehicle route planning, offering a practical and efficient solution for post-disaster emergency response. Full article

Rainfall-induced cut-slope failures are one of the main causes of traffic disruptions in road networks, consuming 30–50% of annual road maintenance budgets. Therefore, it is crucial to analyze how traffic disruptions, resulting from cut-slope failures, impact the overall operation of road networks. In addition, as climate change alters the precipitation patterns, the frequency of these phenomena is expected to increase. For these reasons, it is essential to develop a methodology, from a risk perspective, to understand and assess how cut-slope failures impact the normal operation of road networks. This article introduces a methodology to assess the risk of traffic disruption caused by rainfall-induced cut-slope failure, in terms of Origin–Destination travel time increases. The methodology comprises three stages: (1) modeling the rainfall hazard, (2) estimating the road network’s vulnerability to slope instability, and (3) quantifying risk through resulting travel time increases. A case study was performed on a road network highly vulnerable to cut-slope failure in the Biobío Region of southern Chile. The analysis using the GIS-based software revealed that rainfalls lasting more than 12 h increase average travel times by 20%, with maximum increases of about 40% for 24 h rainfalls, affecting travel between the main cities in the Biobio region and the Concepción metropolitan area. These results may be critical for decision-makers to identify highly exposed and vulnerable road sections in order to recommend effective mitigation strategies to reduce the risk of cut slope failures. Full article

The Belt and Road Initiative (BRI) represents one of the world’s most ambitious transnational infrastructure and investment programs, but its implementation faces considerable external risks. Specifically, these risks include geopolitical instability, regulatory disparities, socio-cultural conflicts, and economic volatility, which threaten project continuity, economic viability, and sustainability of the BRI framework. Consequently, effective risk recognition and prediction has become crucial for mitigating disruptions and supporting evidence-based policy formulation. What should be noticed is that existing risk management frameworks lack specialized, dynamically adaptive indicator systems capable of forecasting external risks specific to international engineering projects under the BRI. They tend to rely on static and traditional methods, which are ill-equipped to handle the dynamic and nonlinear nature of these transnational challenges. To address this gap, we have developed a machine learning-based early warning system. Drawing on a comprehensive dataset of 31 risk indicators across 155 BRI countries from 2013 to 2022, we constructed a stacked ensemble model optimized via Grid Search. The resulting ensemble model demonstrated exceptional predictive performance, achieving an R² value of 0.966 and outperforming all baseline methods significantly. By introducing a data-driven early-warning framework, our study contributes to more resilient infrastructure planning and improved risk governance mechanisms in the context of transnational cooperation initiatives. Full article