Search Results (1,493)

Inland waterway locks are critical navigation infrastructures in waterborne transport systems, and their operational reliability is closely associated with the resilience and service capacity of regional inland waterway networks. As lockage demand continues to increase, reservation scheduling has emerged as an important institutional and operational instrument for improving traffic organization, alleviating congestion, and enhancing the adaptive capacity of lock operations. However, the implementation of reservation-based rules may reshape lockage resource allocation, priority mechanisms, vessel arrival patterns, and stakeholder benefit structures, thereby generating social risks that cannot be adequately captured by conventional engineering safety assessment methods. To address this issue, this study develops a stakeholder-informed dynamic social risk assessment framework for inland waterway lock reservation scheduling. First, a hybrid scheduling mode combining priority reservation and declaration-based queuing is conceptualized, and its operational process is characterized through lockage resource allocation, vessel reservation/declaration, coordinated water-area control, anchorage and safety inspection, and sequenced vessel lockage. Second, drawing on the social amplification of risk framework, a social risk assessment indicator system is constructed from four dimensions: legality, rationality, feasibility, and controllability. Third, a Dynamic Weighting Model is introduced to adaptively update indicator weights according to changes in scheduling rules and operational states, while a Two-Dimensional Cloud Model is developed to jointly represent risk probability and risk consequence under uncertainty. The proposed approach is validated using the reservation scheduling scenario of the Three Gorges locks. The results indicate that the overall social risk is classified as level 2, corresponding to a moderately low risk level, suggesting that the reservation scheduling mode is generally compatible with lock operation and navigation management requirements. Among the four dimensions, controllability exhibits the highest relative risk, indicating that greater attention should be paid to safety management, reservation compliance, public opinion guidance, and emergency response. The proposed framework provides a systematic and visual decision-support tool for risk identification, risk grading, and scheme optimization in inland waterway lock reservation scheduling. Full article

Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” pattern for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment. Full article

The National Electric Vehicle Infrastructure (NEVI) Program is a major federal initiative to expand electric vehicle (EV) charging infrastructure and support transportation electrification in the United States. This study examines the evolution of NEVI implementation across New York, New Jersey, and Pennsylvania between 2022 and 2026. A qualitative comparative longitudinal approach was used to analyze 23 official documents, including NEVI deployment plans, annual implementation updates, Federal Highway Administration guidance, and Joint Office of Energy and Transportation resources. The findings show that implementation evolved beyond compliance with the Alternative Fuel Corridor toward broader transportation electrification, characterized by adaptive governance, infrastructure scalability, and operational resilience. New York demonstrated the most advanced implementation through extensive interagency coordination, infrastructure integration, and long-term planning. New Jersey emphasized metropolitan charging accessibility, adaptive planning, and alignment with statewide zero-emission vehicle objectives. Pennsylvania followed a more gradual implementation trajectory shaped by phased deployment, regional accessibility priorities, and procurement-related challenges. The study demonstrates that implementation trajectories differed despite a common federal framework and contributes to the literature by providing a comparative longitudinal perspective on how governance and institutional adaptation influence large-scale EV infrastructure deployment. Full article

Gallium (Ga)-based liquid metal (LM) composites, particularly those based on eutectic gallium–indium (EGaIn) and related alloys, have emerged as a promising materials platform for soft and deformable energy storage owing to their unique combination of metallic conductivity, fluidic deformability, and adaptive interfaces. Despite rapid advances in LM-enabled devices, binders remain insufficiently understood and are still commonly regarded as passive structural components. Here, we present a comprehensive binder-centered perspective for LM composites, establishing the binder as a key regulator of electro-chemo-mechanical coupling, interfacial stability, transport behavior, and processability in soft energy systems. We show that tailored binder chemistries in Ga-based LM systems—including stretchable batteries, printable conductors, and soft electrochemical devices—govern LM droplet dispersion, suppress coalescence and leakage, and preserve conductive percolation under large deformation, while enabling room-temperature fabrication and printability through rheological regulation and interfacial wetting. Beyond mechanical confinement, emerging binder functionalities—including dynamic bonding, supramolecular interactions, ionically conductive networks, and reversible polymer architectures—enable self-healing interfaces, adaptive transport pathways, and robust adhesion in deformable devices. By integrating recent advances in stretchable batteries, flexible supercapacitors, printable electronics, and multifunctional soft energy systems, we establish a unified multiscale framework linking binder molecular design to device-level electrochemical and mechanical performance. We further discuss sustainability and manufacturing considerations, including recyclable polymer networks, low-temperature fabrication, and scalable processing strategies. Finally, we outline current challenges and future opportunities toward programmable binder systems with tunable viscoelasticity, interfacial reactivity, and adaptive functionality. This Review establishes binder-centered engineering as a key pathway for transforming LM composites from proof-of-concept materials into resilient, manufacturable, and multifunctional soft energy technologies for wearable, stretchable, and biointegrated electronics. Full article

Greenhouse gas emissions and air pollution are closely interconnected environmental challenges with major implications for human health and global sustainability. Many of the activities that drive climate change also release pollutants such as nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter, which directly affect air quality and population health. This review synthesises current evidence on the main sources of greenhouse gas emissions and atmospheric pollutants, the atmospheric processes that influence air quality, and the epidemiological evidence linking air pollution exposure to adverse health outcomes. The paper also discusses the public health co-benefits of climate mitigation strategies, including the transition to cleaner energy systems, sustainable transport policies, and urban environmental interventions. Finally, the review places air pollution and climate change within the broader framework of human security, highlighting their implications for health security, environmental stability, food systems, and economic resilience. By integrating perspectives from environmental epidemiology, public health, and global environmental governance, this review provides a multidisciplinary overview of the links between greenhouse gas emissions, air quality, and human well-being, and underscores the importance of coordinated policy responses to address these interconnected challenges. Full article

With the increasing frequency of extreme weather events, the vulnerability of urban power-transportation systems in severe dust storm conditions has become increasingly apparent. Addressing the shortcomings of existing research regarding the quantitative assessment and enhancement of system resilience, this paper proposes a set of strategies and methods for evaluating and improving the resilience of urban power-transportation systems under severe dust storm conditions, taking mobile energy storage into account. The study first establishes a multidimensional failure probability model for severe dust storm conditions: on the power grid side, it comprehensively considers fluctuations in renewable energy output, wind speed variations, and line insulation performance to propose a probabilistic failure model that accounts for the sand accumulation effect; on the transportation side, it considers road visibility and traffic flow to propose an improved BPR traffic flow model, using the Floyd algorithm to plan MES travel routes. Fault scenarios are generated using the Monte Carlo algorithm, and multidimensional system performance metrics for the power grid–road network-coupled nodes are established. A quantification method for resilience metrics applicable to urban power-transportation systems is proposed based on the ΦΛEΠ resilience index. Furthermore, a multi-objective, multi-stage resilience enhancement strategy for urban power-transportation systems that incorporates mobile energy storage is proposed using the NSGA-II algorithm. Finally, the effectiveness of the proposed optimization strategy was verified through coupled simulation cases using the IEEE-33 node test system and the Sioux Falls network. The results demonstrate that the proposed optimization strategy can significantly enhance system resilience under different optimization objectives. Full article

Multimodal transport plays an irreplaceable role in international trade due to its cost and efficiency advantages. However, optimizing multimodal transport paths that simultaneously consider economic costs, carbon emissions, demand uncertainty, and traffic congestion remains a critical challenge. This paper establishes a scenario-based multi-objective optimization model to minimize total transportation costs and carbon emissions under uncertain demand and road congestion. To address this complex combinatorial problem, we propose LMSSA, an improved multi-objective salp swarm algorithm that integrates Bernoulli chaotic mapping, adaptive parameter adjustment, and a co-directional leader–follower update strategy. These enhancements significantly improve the balance between global exploration and local exploitation, overcoming premature convergence common in traditional salp swarm algorithms. The algorithm’s effectiveness is validated through extensive experiments on 50 instances of varying scales (8 to 100 nodes) and a real-world case study of multimodal transport in northern China. Results demonstrate that LMSSA outperforms the standard multi-objective salp swarm algorithm in convergence speed, solution quality, and robustness, providing enterprises with more economical, low-carbon, and resilient transportation decisions under uncertain and congested conditions. Full article

The rapid digitalization of transportation systems has expanded the cyberattack surface of critical infrastructure while exposing significant shortages in transportation-specific cybersecurity workforce capacity. Existing workforce studies rarely integrate transportation-sector workforce development, organizational preparedness, operational technology (OT) challenges, and interdisciplinary collaboration within a unified analytical framework. In response to this gap, this study employs a mixed-methods approach that integrates Term Frequency–Inverse Document Frequency (TF-IDF) text analysis of publicly available workforce development documents, qualitative interviews with academics, policymakers, and transportation cybersecurity professionals, and organizational survey analysis to examine workforce development challenges across transportation subsectors. The findings reveal persistent information technology (IT) and operational technology (OT) competency gaps, curriculum–industry misalignment, organizational staffing shortages, disparities in cybersecurity preparedness, and the critical importance of experiential learning and dedicated cybersecurity staffing. The study further emphasizes the need for interdisciplinary education, organizational capacity building, and sustained collaboration among academia, government, and industry to strengthen the transportation cybersecurity workforce’s readiness and resilience. Collectively, the findings position transportation cybersecurity workforce development as a systems-coordination challenge that requires long-term strategic investment, cross-sector collaboration, and workforce innovation to enhance the security, resilience, and operational continuity of transportation systems. Full article

Inbound tourism has become an important indicator of destination openness, service capacity, cultural communication, and sustainable governance. However, existing evaluations often separate visitor experience, destination competitiveness, and sustainability, making it difficult to diagnose how service quality supports long-term competitiveness. This study develops a sustainability-oriented framework for evaluating inbound tourism service quality in 10 representative Chinese cities. Nineteen indicators are organized into four dimensions: basic service provision, cultural and experiential perception, safety and emergency response, and sustainable and resilient development. A TIFN-AHP-TOPSIS model is used to integrate official statistics, public tourism information, online-review evidence, and expert judgments while retaining uncertainty and hesitation in qualitative assessments. The results show that Shanghai, Beijing, and Hangzhou form the leading tier; Shenzhen, Chengdu, Guangzhou, Sanya, and Xiamen form the balanced tier; and Xi’an and Chongqing form the potential tier. Robustness checks based on risk-preference adjustment, entropy-weighted TOPSIS, grey relational TOPSIS, and perception-indicator perturbation confirm the stability of the tier classification. The findings suggest that inbound tourism competitiveness depends not only on transport access and reception capacity but also on cultural interpretation, digital convenience, safety governance, ecological quality, and resilience. The framework provides a diagnostic tool for improving sustainable destination competitiveness. Full article

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays. Full article

Extreme precipitation and compound hazards can increase the risk of inundation and accidental release of untreated effluent from water treatment facilities, with potential downstream impacts within a short emergency-response window. Few studies have linked site-scale surface-runoff behavior, feasible emergency-response scenarios, and resilience-based decision support for critical water infrastructure. This study presents a GIS-based scenario-comparison framework that couples high-resolution surface-runoff simulation with an AHP-informed resilience interpretation to evaluate untreated effluent runoff and temporary flood-defense strategies at a water treatment plant in Jeollabuk-do, South Korea. A 1 m digital elevation model derived from drone-based LiDAR data was used in ArcGIS Pro to simulate two-dimensional unsteady surface-runoff propagation, producing water-depth and flow-velocity fields at 30 s intervals over 20 min. Three scenarios were compared under identical topographic, release, and hydraulic assumptions, no response, primary defense-line deployment, and secondary defense-line deployment, adding a 335 m barrier along the downstream road. Under the no-response scenario, released water reached the river after approximately 6 min, with a cumulative river inflow of 329.27 m³. The primary defense line reduced cumulative river inflow by 16.8%, and the secondary defense line by 78.2%, while delaying river arrival to 8 min and 30 s. An approximate surface-water balance and time-series analysis showed that the defense lines primarily redistribute water into temporary upstream storage rather than eliminate it. The simulation-derived indicators were linked to four resilience components whose relative importance was estimated using the Analytic Hierarchy Process (AHP) from 205 expert and practitioner responses, which identified recovery speed as the highest-priority component; the weighted normalized indicators are summarized as a transparent scenario-level composite resilience indicator that increases from the no-response to the primary and secondary defense-line scenarios. Because the stormwater drainage network, pollutant transport, and operational deployment uncertainties were not explicitly modeled, the results should be interpreted as a comparative assessment of water-volume transport risk rather than a deterministic prediction of inundation or pollution impact. Within these stated assumptions, the results indicate that a strategically placed secondary defense line can substantially reduce downstream river inflow and secure additional response time, providing preliminary decision support for disaster-risk reduction and emergency-response planning at critical water infrastructure. Full article

Hybrid Energy Storage Systems (HESSs) have emerged as an inevitable solution in modern power systems and transport electrification. An HESS combines two or more complementary storage technologies—such as Batteries (BTs) with Supercapacitors (SCs), or BTs with thermal or mechanical energy storage, etc., to leverage their virtues. The robustness of HESS configurations is of utmost importance for exploring failure analysis and resilience approaches in BT-SC-based HESSs, which are crucial for long-term reliability, safety, and contributions towards future decarbonization goals. Hence, based on this motivation, this work focuses on the study of conventional and advanced HESS configurations, together with a method of configuration selection. Subsequently, the review aims to obtain a systematic identification, characterization, and understanding of the reasons behind HESS failures. This paper thus defines what HESS failures are and their possible mitigations, discussing many state-of-the-art research studies that may help researchers in finding correct and updated literature content concerning this research area. Finally, future trends and developments in BT-SC-based HESSs are discussed. Full article

In Space–Air–Ground Integrated Networks (SAGINs), the inherent high bit error rate (BER) and prolonged propagation latency of satellite links, compounded by the highly dynamic topologies and multi-hop nature of Unmanned Aerial Vehicle (UAV) networks, present severe bottlenecks to end-to-end transport performance. To mitigate performance degradation within these heterogeneously converged SAGIN-UAV architectures, this paper proposes a SAGIN-enabled Adaptive End-to-End Congestion Control scheme. By exploiting the distinct transmission characteristics of long-delay, high-BER satellite links alongside terrestrial mobile multi-hop UAV networks, the Proposed Scheme optimizes data injection during the slow-start phase and introduces a high-precision loss differentiation mechanism during the congestion avoidance phase. This framework accurately distinguishes non-congestive losses (e.g., channel errors or topology switching induced by UAV mobility) from genuine buffer overflows. The simulation results demonstrate that the proposed adaptive scheme significantly reduces queuing delays at UAV nodes, accelerates transmission efficiency across multi-hop terminals, and enhances data throughput in high-latency environments. Ultimately, this scheme offers a resilient solution for optimizing end-to-end transport control and maximizing the overall transmission capability of SAGIN-enabled UAV networks. Full article

The virtually coupled train set (VCTS) system is a promising urban rail transit paradigm that replaces physical couplers with train-to-train (T2T) wireless communication, enabling dynamic marshaling to achieve the precise matching of transportation demand and resources. However, existing VCTS control strategies either assume perfect leader state availability, rely on continuous communication, or lack guaranteed transient/steady-state performance under Denial-of-Service (DoS) attacks. To address these critical limitations, this paper proposes a unified finite-time resilient event-triggered cooperative control framework for VCTSs against malicious DoS attacks. The proposed framework integrates three synergistic components: a distributed finite-time leader state estimator to reconstruct leader information under intermittent communication interruptions, a prescribed performance finite-time controller to bound tracking error fluctuations and accelerate convergence, and an adaptive event-triggered communication protocol to reduce controller update frequency. The closed-loop system stability, finite-time convergence, and prescribed performance guarantees are rigorously proven via Lyapunov analysis, and Zeno behavior is strictly excluded. Extensive comparative simulations demonstrate that the proposed framework outperforms representative state-of-the-art methods in terms of tracking accuracy, attack resilience, and communication efficiency, achieving a significance reduction of approximately 70% in controller update frequency while maintaining system stability under the considered DoS attack scenarios. Full article

Sustainable and resilient road infrastructure requires the early identification of pavement deterioration mechanisms that emerge under complex urban traffic conditions, particularly at signalized intersections where repeated stop–go operations, queue persistence, and lane-wise freight concentration generate highly nonuniform structural loading. However, most existing intelligent transportation studies emphasize crash prediction, traffic-state estimation, or mobility optimization, while the infrastructure-performance consequences of freight-dominant interrupted flow remain insufficiently addressed. To support proactive pavement management and resilient urban road operation, this study proposes a traffic simulation-driven deep learning framework for predicting lane-level pavement deterioration under freight loading and stop–go urban traffic conditions. A high-resolution PTV Vissim 2024 microscopic simulation environment was developed for a four-leg signalized urban intersection, and a structured multi-scenario design was used to generate progressively increasing operational stress regimes, ranging from baseline flow to freight-dominant oversaturated operation. The resulting lane-wise dataset integrates direct traffic variables with pavement-oriented descriptors, including the Lane Freight Loading Index (LFLI), Stop–Go Severity Index (SGSI), ESAL proxy, queue persistence, and Loading Asymmetry Index (LAI). To learn the complex relationship between traffic operation and infrastructure degradation, a new Freight-Aware Lane Interaction Transformer Network (FLIT-Net) is introduced. The proposed model combines feature embedding, lane-interaction self-attention, freight-aware gating, residual refinement, and multi-task regression to jointly predict rutting risk, fatigue-cracking risk, and the Pavement Deterioration Index (PDI). Experimental results show that FLIT-Net outperforms MLP, CNN, LSTM, Bi-LSTM, and generic Transformer baselines, achieving RMSE/MAE/$R^2$ values of 0.041/0.032/0.9687 for rutting risk, 0.044/0.034/0.9635 for fatigue-cracking risk, and 0.031/0.024/0.9824 for PDI. Sensitivity and scenario-wise analyses further confirm that deterioration increases monotonically with freight intensity, stop–go severity, and queue persistence, highlighting the importance of lane-resolved deterioration intelligence for sustainable maintenance prioritization. The proposed framework bridges traffic microsimulation, pavement-oriented feature engineering, and freight-aware deep learning, providing a decision-support basis for improving the performance, safety, and resilience of urban pavement infrastructure. Full article