Search Results (336)

Urban underground infrastructures are highly vulnerable to intense rainfall events, particularly access stairs, where preferential runoff paths and the most probable evacuation routes can conflict. This study presents a pluvial flood hazard assessment for underground access stairs in the Barcelona Metropolitan Area Metro network. It integrates the EU ICARIA project modeling framework and the hazard assessment criteria based on hydraulic parameters identified by the Spanish national research project FAVOUR. Both current and future climate change rainfall scenarios are considered. The results showed that out of 415 underground access points, 27 face a high risk of floods, while 35 more have potentially high-risk conditions. These figures could rise to 38 (40% increase) and 47 (74% increase) respectively by the end of the century since climate change is projected to increase rainfall intensity and frequency. By quantifying hazard levels across the network, this study allows the identification of points of the infrastructure where hazard conditions can be more critical. Furthermore, the results presented could potentially support targeted adaptation strategies such as entrance retrofitting, improved drainage design, and emergency planning to develop resilient and sustainable cities. The proposed methodology demonstrates how ICARIA’s modeling framework can effectively evaluate and anticipate flood hazards in complex urban environments at the asset level. Full article

The emergence of Generative Artificial Intelligence (GenAI), capable of interpreting and reasoning with human language, has catalyzed a paradigm shift across various societal sectors. Within the tourism industry, GenAI is increasingly utilized to facilitate personalized itinerary planning, destination recommendations, and the provision of optimal route information. This study evaluates the reliability of GenAI in identifying the nearest metro station within a walking distance from tourist attractions in Busan, South Korea. Furthermore, it aims to empirically verify the determinants influencing the correctness of AI-generated responses compared to network-based shortest-path analyses. The empirical results demonstrate that Google’s Gemini 3 Pro model achieved superior performance, recording an accuracy rate of 65.0%. Regression analysis revealed that for both Gemini and GPT models, the volume of news articles associated with an attraction—representing media visibility—significantly increased the likelihood of accurate information provision. Notably, the Gemini model exhibited distinct sensitivity to geographic factors and text similarity metrics, suggesting a difference in how it processes spatial context compared to other models. Consequently, this study underscores the importance of high-quality AI-generated tourism data and offers significant contributions to the advancement of sophisticated personalized travel planning systems and GeoAI research focused on spatial problem-solving. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

Intra-urban commuting plays a fundamental role in shaping urban spatial structure and daily mobility patterns. Existing studies have largely explained commuting flows using attribute-based or distance-centred approaches. Such approaches overlook the interdependent and relational nature of commuting within complex urban systems. This study constructs a subdistrict-level commuting network using anonymised mobile phone signalling data from Hangzhou, China, and a valued exponential random graph model (valued ERGM) to examine how commuting flows are generated through the interaction of network self-organization, local job-housing conditions, and multi-dimensional proximity. The results reveal strong endogenous dependence exemplified by reciprocal commuting ties. Employment agglomeration and public rental housing provision are associated with stronger integration of subdistricts within the commuting network, while high housing prices and certain residential amenities are associated with reduced inter-subdistrict commuting. Beyond geographic distance, metro connectivity, administrative affiliation, and social interaction are significantly associated with commuting flows. This study advances a relational explanation of intra-urban commuting and demonstrates the methodological value of valued ERGMs for analysing weighted urban flow networks. The findings have implications for integrated transport, housing, and governance strategies, particularly transit-oriented development, cross-jurisdictional coordination, and the strategic siting of affordable housing, aimed at promoting more locally embedded and sustainable urban mobility. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

The object of this research is resource management and energy consumption processes in optical fiber communication networks with access–metro–core architectures. The study addresses the problem that conventional static and semi-dynamic control methods are unable to simultaneously ensure energy efficiency and QoS stability under conditions of exponentially growing and highly variable traffic. To solve this problem, an AI-based integrated control model was developed that combines traffic prediction, dynamic resource allocation, spectrum management, and power optimization within a unified framework. Traffic prediction is performed using LSTM–BiRNN neural networks (1.2–1.8 million parameters, 300–500 thousand records), while control decisions are generated by an Actor–Critic reinforcement learning algorithm. Simulation results obtained in the Python 3.12 and OptiSystem 17.0 environments demonstrate that, in the Access segment (1–10 Gb/s), latency is stabilized within 1–10 ms; in the Metro segment (40–120 Gb/s), energy consumption is reduced by 18–27%; and in the Core segment (400–1000 Gb/s), the efficiency of RSA algorithms increases by 22–35%. When the EDFA output power is maintained within +17 to +23 dBm, amplifier power consumption decreases by 10–15%, resulting in overall network energy savings of 20–40%. The obtained results are explained by the synergy of accurate traffic prediction provided by the LSTM–BiRNN model and proactive real-time decision-making enabled by the Actor–Critic algorithm. The distinctive feature of the proposed approach is the simultaneous optimization of energy efficiency and QoS across all access, metro, and core segments within a single integrated architecture. The results can be practically applied in the design and modernization of optical fiber communication networks, as well as in the deployment of energy-efficient intelligent network management systems. Full article

Mexico City’s Metro Line 7 is a critical north–south artery within one of the world’s largest metro systems, yet it suffers from persistent operational inefficiencies, including chronic overcrowding and extended passenger travel times. This research employed a data-driven discrete-event simulation model built in SIMIO to analyze the passenger dynamics of Line 7. The model was grounded in a comprehensive dataset of approximately 280,000 daily passengers over one year. Key innovations included modeling station-specific passenger arrivals as non-stationary Poisson processes with time-varying rates calculated at 15-min intervals and incorporating empirically derived walking times within stations. The simulation framework replicated the system’s operational logic, including train movements, passenger boarding and alighting, and complex transfer behaviors at interchange stations, while accounting for the influence of the broader metro network on Line 7’s passenger flows. The simulation results, derived from 100 replications, quantified severe systemic inefficiencies. The average total travel time for a passenger using Line 7 was 81.17 min. However, the ideal in-motion travel time was calculated to be only 53 min, revealing that passengers spend a disproportionate amount of time waiting. This yielded a travel time efficiency of just 65.3%. The model identified specific bottlenecks at key transfer stations like Tacubaya and San Pedro de Los Pinos, where platform utilization reaches full capacity, directly causing the excessive queuing times that degrade the overall passenger experience. This study demonstrated that the primary issue is not the speed of trains but the systemic inability to manage passenger flow during peak demand, leading to critical capacity shortfalls at specific stations. The simulation provides a quantitative tool for diagnosing these inefficiencies and offers a robust platform for prototyping and evaluating strategic interventions, such as optimized timetables and resource allocation, before costly real-world implementation. Full article

The continuous growth of traffic in metro networks is increasing the need for cost-effective, scalable, and power-efficient optical solutions. Filterless optical networks (FONs) have emerged as a promising architecture for metro-aggregation and metro-access domains, thanks to their low complexity and reliance on passive optical components. However, their inherent broadcast nature introduces key challenges, including spectrum waste, limited power equalization, and significant noise accumulation, particularly when coherent pluggable transceivers are employed. This work provides a detailed assessment of FON performance using state-of-the-art multi-source agreement (MSA)-compliant coherent modules, evaluating both point-to-point (P2P) and digital subcarrier multiplexing (DSCM)-based point-to-multipoint (P2MP) architectures. A novel optical amplifier (OA) optimization algorithm is proposed to balance expressed and added signal power in FON, accounting for optical power saturation effects and optical performance degradation due to limited power at the receiver input. The analysis highlights the substantial impact of transmitter out-of-band (OB) noise in FONs and its detrimental accumulation during multi-channel colorless aggregation, which can limit network capacity. In scenarios with lower capacity requirements, P2MP architectures demonstrate superior performance, benefiting from reduced insertion loss and lower OB noise accumulation while offering enhanced scalability compared with P2P solutions. Overall, the study highlights that FONs combined with coherent pluggables can support cost-efficient and scalable metro solutions, provided that OB noise, power imbalance, and amplifier operation are properly addressed through optimized design strategies. Full article

To investigate the regulatory effects of emotional states on the evolution of fatigue in metro drivers, this study conducts an experimental investigation based on an urban rail transit driving simulation platform. A total of 21 participants complete a 90 min simulated driving task, during which electroencephalogram (EEG) and electrocardiogram (ECG) signals are synchronously collected from drivers for fatigue assessment and emotion recognition, respectively. An emotion recognition model based on a multi-scale convolutional neural network (MSCNN) combined with an attention mechanism is constructed. The proposed model uses ECG signals to classify three emotional states—neutral, positive, and negative—where the neutral state is defined as an emotionally undefined baseline that is neither positive nor negative. The model achieves a classification accuracy of 86.96% on the DREAMER dataset. By temporally aligning the emotion recognition results with EEG frequency-domain fatigue indicators, the results show that fatigue exhibits the highest growth and largest fluctuation in amplitude under negative emotions, demonstrating a pronounced fatigue-accelerating effect. Under positive emotions, fatigue decreases considerably and has smaller fluctuations, indicating a certain buffering and restorative effect. In contrast, the neutral emotional state exhibits intermediate and transitional fatigue characteristics. This study innovatively integrates ECG-based emotion recognition with EEG-based fatigue assessment to reveal the mechanisms based on which emotions influence fatigue in metro driving tasks from a physiological perspective. This work provides a basis for emotion-aware fatigue monitoring and safety intervention strategies. Full article

Real-time route guidance during disruptions in urban rail transit systems requires rapidly providing effective strategies that simultaneously alleviate congestion and account for passengers’ travel time. This study proposes an optimization framework that considers travel time, congestion perception time, and information costs, incorporating a Logit choice model with information bias to reflect passengers’ behavioral responses under disruptions. A bi-level simulation evaluation mechanism is employed to rapidly evaluate the objective functions under different guidance strategies, where a Physically Consistent Incremental Simulator, based on differential computation, achieves a 599-fold speedup while maintaining high fidelity with full-scale simulations (Pearson correlation > 0.96). A hybrid algorithm combining the Gray Wolf Optimizer and Adaptive Large Neighborhood Search is developed to solve the origin–destination level route guidance optimization problem. The algorithm embeds domain knowledge-based “destroy and repair” operators with a sequential repair mechanism to enable fast global search and precise local refinement. Case study results demonstrate that the framework reduces severely congested sections by 36%, shortens average travel time by 7.16 min, and improves solution quality by 12–30% over baseline algorithms. These findings confirm the practical applicability of integrating intelligent optimization with high-efficiency simulation for emergency route guidance in large-scale metro networks. Full article

Climate-induced natural hazards are increasingly disrupting metro operations in megacities, necessitating robust and generalizable frameworks for system-wide resilience. While current studies often treat infrastructure degradation, operational adjustment, and passenger flow redistribution as separate problems, this study proposes a resilience-oriented decision framework that couples these universal processes together to address diverse disruptive events. Taking extreme rainfall as a critical representative scenario, a multi-objective recovery optimization model is developed to jointly optimize repair resource cost and average section saturation. Resilience is quantified through the demand satisfaction ratio over the disruption–recovery process, ensuring the framework’s applicability across different hazard types. A case study of the Shanghai metro system under a real extreme rainfall event demonstrates the model’s efficacy in capturing complex system dynamics. Results show a clear Pareto trade-off between repair resource cost and average section saturation, while increasing service capacity on adjacent lines improves the Pareto frontier. Prioritizing repairs on lines with the fewest damaged sections effectively reduces network saturation by restoring corridor throughput. The resilience curve proves that higher repair resources not only shorten recovery time but also raise the minimum demand satisfaction ratio. These findings provide a scalable methodology for designing resilient metro recovery strategies under various climate-related disruptions globally. 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

Taking the rail transit transfer stations in Qingyang, Wuhou, and Chenghua Districts of Chengdu as the research objects, this study constructs a static-dynamic coupled analytical framework by integrating the input-oriented three-stage super-efficiency SBM model and the output-oriented Malmquist-Luenberger (ML) index to systematically evaluate rail transit transfer efficiency. The findings reveal that the transfer efficiency of Chengdu Metro exhibited a fluctuating growth pattern from 2017 to 2023, with significant variations corresponding to periods of network expansion and operational adjustments. Improvements in technical efficiency and management optimization have been key drivers of overall efficiency gains. The three-stage super-efficiency SBM model effectively filters out the impacts of environmental variables and random noise, uncovering inter-station efficiency disparities and resource redundancy issues. Decomposition of the ML index indicates that both technical efficiency and technological progress jointly drive total factor productivity (TFP) changes. On average, technical efficiency has been the more stable and prominent contributor to productivity growth. However, the reasons for TFP declines at certain stations are varied; some under-performed due to lagging technological progress, while others faced constraints in technical or scale efficiencies. The study confirms that the synergistic application of the three-stage model and the ML index can accurately identify bottlenecks and provide theoretical support and practical pathways for optimizing resource allocation and dynamic management in urban rail transit systems. Findings and methods from Chengdu’s practice provide a replicable paradigm for evaluating, planning and optimizing rail transit transfer hubs in Chinese cities at different development stages, and offer empirical references for advancing urban public transport and sustainable development of comprehensive transportation systems. Full article

The pantograph–catenary system is a critical component of the traction power supply network. Due to hard points on the overhead contact line and vibrations of the pantograph, pantograph–catenary separation may occur, leading to offline DC arc events. To investigate the characteristics of DC arcs generated during pantograph–catenary separation in metro systems, this study constructs a laboratory platform that simulates the offline process and analyzes the electrical characteristics, optical intensity, and arc-burn duration under different electrode separation conditions. First, a DC pantograph–catenary offline arc simulation platform is developed using a contact wire, a carbon-strip pantograph slider, and a linear motor, enabling slider movement in both horizontal and vertical directions. Second, offline discharge experiments are conducted to compare the discharge process and electrical arc characteristics with and without horizontal slider motion. Finally, arc luminosity and burn duration are measured under various electrode separation configurations, and the influence of voltage level, current level, and electrode material is examined. Experimental results reveal a significant polarity effect, where the arc burn duration is notably longer when the contact wire serves as the cathode than when the carbon slider serves as the cathode. At the instant of separation, the high electric field intensity within the micro-gap triggers pronounced “peak phenomena” in both arc resistance and power, accompanied by abrupt voltage surges and transient current dips. Furthermore, the introduction of horizontal motion modulates the arcing process, causing the stable arcing voltage to follow a distinctive trend of a slow increase followed by a gradual decrease, which differs from static separation characteristics. Finally, this study demonstrates that voltage levels exert a more dominant influence on arc luminosity and duration than current levels, while the maintenance voltage of the arc channel remains significantly lower than the air breakdown voltage. Full article

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners. Full article