Search Results (86)

Sustainable urban logistics requires infrastructure that can support routine low-carbon freight delivery while maintaining emergency supply capacity under disruptions. However, existing underground logistics system studies mainly focus on routine freight efficiency and network feasibility, whereas emergency logistics research is largely based on surface transport systems. Limited attention has been paid to the integrated design and operational validation of dual-use underground logistics networks under uncertain routine and emergency demand. To address this gap, this study proposes a dual-use underground logistics system (DULS) framework that combines robust layout optimization with dynamic simulation. A multi-echelon network consisting of supply centers, primary nodes, secondary nodes, and demand points is constructed. Candidate primary nodes are screened using an entropy-weighted TOPSIS method, and a Wasserstein-based distributionally robust optimization model is formulated to jointly determine node location, resource allocation, and freight paths under demand uncertainty. A hybrid heuristic is developed to solve the model, and an AnyLogic-based discrete-event simulation model is used to evaluate operational performance under different demand-generation patterns and train operation strategies. In the Nanjing case, the optimized DULS includes 19 primary nodes and 72 secondary nodes, achieves an emergency-demand fulfillment rate of 84.84%, and keeps the average end-to-end emergency supply time within 4 h. Cross-station operation performs better than the all-stop mode in both transport time and deprivation cost. An ex-post operational emission comparison further indicates that the DULS can reduce road-based freight emissions by 60.20% under routine operations. The proposed framework provides methodological support for planning sustainable dual-use underground logistics infrastructure serving both routine freight delivery and emergency supply. Full article

Air pollution associated with public transport systems constitutes a critical yet highly heterogeneous component of urban exposure and represents an important challenge for sustainable urban mobility and environmental health governance. Commuters and transport workers are frequently subjected to pollutant concentrations that exceed those reported by ambient background monitoring networks. This review provides a comprehensive synthesis of the global scientific literature on air quality in public transport microenvironments—including buses, bus stops, terminals, and underground stations—through a multidimensional analytical framework that considers climatic classification, socio-economic context, meteorological drivers, transport microenvironment typology, sampling strategies, analytical techniques, and exposure metrics. A large body of peer-reviewed studies published worldwide was examined to identify dominant patterns, methodological trends, and persistent knowledge gaps. Across regions, the evidence consistently reports elevated concentrations of particulate matter (PM_2.5, PM₁₀, and ultrafine particles) and traffic-related gaseous pollutants, particularly within confined or poorly ventilated environments and during peak traffic periods. Marked geographical, climatic, and socio-economic imbalances are evident, with most studies conducted in temperate and tropical climates and in countries with very high or high Human Development Index, whereas arid, continental, and low-HDI regions remain substantially underrepresented. From a methodological perspective, the literature is dominated by short- to intermediate-term monitoring campaigns relying on active sampling, mobile measurements, and increasingly calibrated low-cost sensors, while long-term stationary observations and standardized integrative monitoring frameworks remain scarce. Although advanced analytical approaches—such as chemical characterization, environmental magnetism, receptor modeling, computational fluid dynamics, and inhaled dose assessment—are increasingly applied, their systematic integration remains limited. Overall, this review reveals persistent methodological, geographical, and conceptual gaps and highlights the urgent need for standardized, interdisciplinary, and long-term monitoring strategies to improve exposure assessment and support evidence-based mitigation policies and sustainable urban transport planning aimed at reducing health risks associated with public transport-related air pollution. Full article

Traffic flow in long underground expressways is expected to exhibit amplified spatiotemporal variability due to confined geometry, longitudinal gradients, limited recovery space, and heterogeneous vehicle interactions. As these facilities remain at the planning stage, empirical field data are unavailable, necessitating simulation-based methodological development. Conventional performance indicators (average speed) primarily reflect macroscopic deterioration after congestion has materialized and are therefore insufficient for capturing early variability transitions. This study proposes a composite Spatiotemporal Variability Metric (STVM) designed to quantify instability-related variability dynamics and enable early congestion detection in confined expressway environments. The metric structure was established through the synthesis of prior traffic flow instability research and systematic evaluation of 72 predesigned microscopic simulation scenarios representing diverse geometric and operational conditions. STVM integrates six mechanism-informed components: short-term speed and density fluctuations, heavy-vehicle proportion, sectional saturation level, ramp interference intensity, and exit discharge efficiency. Comparative analyses against average speed demonstrated that variability escalation measured by STVM consistently precedes observable speed degradation by 5–20 min. Internal contribution analyses using correlation, regression, and random forest modeling further confirmed the dominant structural roles of fluctuation- and saturation-related components in governing variability escalation. These findings confirm the usefulness of the STVM in analyzing transition dynamics and supporting real-time ITS-based monitoring in confined expressway systems. Full article

Pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. Quantitative and early detection of defects avoids costly consequences. Due to low cost of water, high-profile accidents, and economic downturns, the research and development of nondestructive evaluation (NDE) and structural health monitoring (SHM) technologies for freshwater mains and urban water networks have received less attention with respect to the gas and oil industries. Moreover, the technical challenges associated with the practical deployment of monitoring systems and the fact that most water pipelines are buried underground demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper reviews the most prominent NDE/SHM technologies for freshwater pipes. The challenges that said infrastructures pose, as well as the methodologies that can be translated into SHM approaches, are highlighted. The scope of this review is to provide a holistic view of the physical principles, the success, and the technological challenges associated with the inspection and monitoring of freshwater pipelines. Full article

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization and the expansion of e-commerce. In this regard, underground or enclosed corridor-based tube-based freight transit systems have surfaced as a viable smart infrastructure option for automated and low-impact commodities delivery. Methods: This study adopts an analytical literature review complemented by a structured case study analysis to examine the potential role of tube-based freight transport systems in future urban logistics. Key technological concepts, including pneumatic tubes, automated capsule transport, and integration with digital platforms, the Physical Internet, and smart city management systems, are examined through a structured analytical review of the literature. Results: The outcome of the reviewed studies indicates that tube-based systems can contribute to congestion alleviation, emission reduction, and improved delivery reliability by shifting selected freight flows away from surface transport networks. However, governance frameworks, infrastructure integration, and institutional coordination mechanisms continue to have a significant impact on claimed performance outcomes. Conclusions: Tube-based freight systems represent a promising but conditional pathway toward smarter and more sustainable urban logistics. Their large-scale deployment is forced by high capital costs, standardization challenges, regulatory uncertainty, and social acceptance issues. Coordinated investment plans, encouraging legal frameworks, and integrated urban planning techniques in line with smart city goals are needed to overcome these obstacles. Full article

Underground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks. Full article

Against the backdrop of global climate change, the rising frequency and intensity of extreme weather events pose severe challenges to urban transport and commercial systems. As a core capacity for managing uncertainty and risk, urban resilience requires infrastructure to resist shocks, recover rapidly, and adaptively evolve. From a resilience perspective, this study develops a comprehensive evaluation system for spatial accessibility between subway stations and commercial complexes, operationalized by 21 indicators across five dimensions: Connectivity, Redundancy, Robustness, Dynamic adaptability, and Comfort. Spatial accessibility is simulated and measured using sDNA spatial network analysis, while an in-depth questionnaire survey collects, feeds back, and validates users’ subjective perceptions. By constructing a dual evaluation model that integrates spatial configuration and behavioral psychology, we find that the integrated development of subway stations and commercial complexes can maintain stable functional performance and sustained vitality under complex urban conditions by optimizing connectivity, enhancing redundancy, and improving adaptability. This is manifested in the expansion of residents’ pedestrian networks and the spillover of social service functions. In parallel, underground spaces can be transformed into resilient infrastructure to enhance civil air defense performance and provide diversified evacuation routes. The findings offer theoretical support and practical guidance for the construction of resilient cities. Full article

Characterized by zero-carbon, congestion-free, and high-capacity features, the utilization of metro systems for collaborative passenger-and-freight transport (the metro-based underground logistics system, M-ULS) has been recognized as a favorable alternative to facilitate automated freight transport in future megacities. This article constructs a three-echelon M-ULS network and establishes a multi-objective bilevel programming model, considering the interests of both government investment departments and transport enterprises. The overall goal of the study is to establish a transportation network with the lowest construction cost, lowest operating cost, and highest facility utilization rate, taking into account factors such as population density, transportation conditions, land resources, logistics demand, and metro station location, under given cost parameters and demand conditions. The upper-level model takes government investment as the main body and aims to minimize the total cost, establishing an optimization model for location selection allocation paths with capacity constraints; the lower-level model aims to minimize the generalized cost for freight enterprises by simulating the competition between traditional transportation and the M-ULS mode. In addition, a bi-level programming model solving framework was established, and a multi-stage precise heuristic hybrid algorithm based on adaptive immune clone selection algorithm (AICSA) and improved plant growth simulation algorithm (IPGSA) is designed for the upper-level model. Finally, taking the central urban area of Beijing as an example, four network scales are set up for numerical simulation research to verify the reliability and superiority of the model and algorithm. By analyzing and setting key indicators, an optimal network configuration scheme is proposed, providing a feasible path for cities to improve logistics efficiency and reduce the impact of logistics externalities under limited land resources, further strengthening the strategic role of subway logistics systems in urban sustainable development. Full article

With the rapid expansion of urban transportation networks, new metro tunnels frequently cut through existing structures’ diaphragm walls by using the shield machine. Such intrusions induce dynamic disturbances that pose significant risks to adjacent structures. This study employs Suzhou Metro Line 8 as a case study to evaluate the safety of existing metro stations during shield tunneling, specifically examining deformation characteristics induced by varying tunneling parameters. A three-dimensional numerical model is developed to assess structural responses, with simulation accuracy rigorously validated against field measurements. Results reveal that the transverse influence zone of the base slab extends approximately 2.5 times the tunnel diameter. Diaphragm wall exhibits horizontal deformation opposite the tunneling direction, while the maximum lateral deformation of adjacent station walls reaches 2.49 mm. Concurrently, a slight uplift manifests at the base slab center with a peak value of 2.54 mm. All obtained structural deformations remain well below the permission value of 5 mm, with observed maxima constituting only 50% of this safety threshold. This substantial deformation margin significantly mitigates construction hazards, promoting the sustainable development of underground space. Full article

Nonlinear seepage behavior within rock fractures represents a critical and actively researched challenge in underground engineering, energy exploitation, and environmental sciences. Through the integration of nonlinear seepage theory with coupled numerical simulations of fracture flow and matrix flow, this study systematically investigates the synergistic mechanisms governing the influence of filling particles, tortuous fractures, and porous matrices on fluid transport within fracture–porous matrix seepage systems. Key findings reveal that: (1) Horizontal fractures continuously receive fluid influx from the surrounding porous matrix, where the flow field maintains remarkable symmetry, with a critical matrix height-to-fracture aperture ratio regulating streamline divergence and convergence at the fracture outlet; (2) The flow field within horizontal fractures undergoes substantial transformation when the Reynolds number exceeds a critical threshold, while maintaining stable flow patterns and -ΔP-Q relationships below this value, demonstrating a distinct inertial-controlled flow regime transition; (3) Tortuous fracture geometries induce localized vortex formation and significant velocity fluctuations, particularly in the front and rear dip-angle zones, substantially enhancing fluid exchange efficiency compared to horizontal configurations; (4) The volumetric flow rate exhibits a non-monotonic relationship with inclination angle, peaking at approximately 36°, while a synergistic effect between fracture inclination and infill particle diameter systematically modulates pressure-drop-flow-rate relationships, with a critical d/h = 0.5 threshold distinguishing fundamentally different flow behaviors. These findings provide quantitative criteria for predicting nonlinear seepage in practical engineering scenarios involving complex fracture networks and filling materials, offering significant implications for risk assessment and drainage design in deep underground projects. Full article

In high-density urban areas where ecological protection constraints are increasingly stringent, transportation infrastructure layout must balance service efficiency and environmental preservation. From an ecological-prioritization perspective, this study proposes a three-stage multi-objective optimization strategy for siting underground smart parking facilities using the NSGA-III algorithm, with Haidian District, Beijing, as a case study. First, spatial identification and screening are conducted using GIS, integrating urban fringe-space extraction with POI, AOI, population, and transportation network data to determine candidate locations. Second, a multi-objective model is constructed to minimize green space occupation, walking distance, and construction cost while maximizing service coverage, and is solved with NSGA-III. Third, under the ecological-prioritization strategy, the solution with the lowest land occupation is selected, and marginal benefit analysis is applied to identify the optimal trade-off between ecological and economic objectives, forming a flexible decision-making framework. The findings show that several feasible schemes can achieve zero green-space occupation while maintaining high service coverage, and marginal benefit analysis identifies a cost-effective solution serving about 20,000 residents with an investment of 7 billion CNY. These results confirm that ecological protection and urban service efficiency can be reconciled through quantitative optimization, offering practical guidance for sustainable infrastructure planning. The proposed methodology integrates spatial analysis, multi-objective optimization, and post-Pareto analysis into a unified framework, addressing diverse infrastructure planning problems with conflicting objectives and ecological constraints. It offers both theoretical significance and practical applicability, supporting sustainable urban development under multiple scenarios. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

Auxiliary transport vehicles are essential components of the underground mine auxiliary transportation system, primarily used for tasks such as personnel and material transportation. However, the underground environment is complex, and unstructured roads exhibit significant randomness. Traditional passive hydropneumatic suspension systems struggle to strike a balance between ride comfort and stability, resulting in insufficient adaptability of auxiliary transport vehicles in such challenging underground conditions. To address this issue, this paper proposes a multi-mode hydropneumatic suspension control strategy based on the identification of road surface grades in underground mines. The strategy dynamically adjusts the controller’s parameters in real time according to the identified road surface grades, thereby enhancing vehicle adaptability in complex environments. First, the overall framework of the active suspension control system is constructed, and models of the hydropneumatic spring, vehicle dynamics, and road surface are developed. Then, a road surface grade identification method based on Long Short-Term Memory networks is proposed. Finally, a fuzzy-logic-based sliding mode controller is designed to dynamically map the road surface grade information to the controller’s parameters. Three control objectives are set for different road grades, and the multi-objective optimization of the sliding mode’s surface coefficients and fuzzy-logic-based rule parameters is performed using the Hiking Optimization Algorithm. This approach enables the adaptive adjustment of the suspension system under various road conditions. The simulations indicate that when contrasted with conventional inactive hydropneumatic suspensions, the proposed method reduces the sprung mass’s acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively, at a speed of 10 km/h. This significant reduction in the vibrational response validates the potential application of the proposed method in underground mine environments. Full article

As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes. Full article

Repeated mining under insufficiently collapsed gobs is a complex process in underground mining and is associated with safety hazards such as ground collapse and subsidence. The effect of layer spacing on the fracture network evolution and fluid transport mechanisms in rock strata during this process has not been systematically studied. In this work, the discrete element method was employed to analyze the fracture development and seepage evolution of surrounding rocks in the Nanliang coal mine across varying layer spacings (5, 20, 35, 50, and 65 m). A systematic evaluation of the rock mass integrity was conducted through damage coefficient quantification. The key findings revealed that an increase in the layer spacing progressively reduced the damage coefficients in both the overburden strata above the goaf and in the interlayer formations ahead of the working face, accompanied by reduced fracture propagation intensity. Shear failure mechanisms dominated throughout the mining process. Fractal characteristics of the fractures intensified with the advance of the working face, while the hydraulic conductivity and interstitial pressure in the interlayer strata exhibited declining trends with reduced attenuation rates. Our findings provide critical insights for ensuring the safety and improving the efficiency of repeated mining under insufficiently collapsed gobs. Full article