Search Results (583)

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article

Rail transit station areas are high-volume public spaces where pedestrian efficiency directly affects the operational quality, equity, and sustainability of public transport systems. However, temporarily mobility-impaired (TMI) pedestrians, such as people carrying luggage or pushing strollers, are often overlooked in station-area pedestrian design. This study quantifies walking-efficiency attenuation among TMI groups and identifies key micro-spatial factors influencing their mobility. Based on 96 typical paths around metro stations in Xiamen, China, real-world walking experiments were conducted with 566 volunteers, producing 1152 valid observations. A Random Forest model was used to predict walking efficiency under different spatial attributes and assess factor importance. The results show that TMI pedestrians walk significantly slower than unimpaired pedestrians and can become a major bottleneck in station-area circulation. Stroller users are most affected by ramp shape, while luggage carriers are particularly sensitive to path width. Partial dependence analysis indicates that a path width of 4.2–4.7 m and a ramp shape factor of 0.2–0.35 support higher efficiency and equity. The findings provide quantitative evidence for universal design and offer practical guidance for sustainable, inclusive, and people-centered transit-oriented development. Full article

Addressing bidirectional section disruptions in urban rail transit, this paper proposes a fault recovery strategy that explicitly incorporates the constraints imposed by train operation intersections (i.e., turn-back stations). To achieve this goal, this study develops a resilience-oriented recovery framework that captures operational dependencies associated with turn-back sections, compares recovery outcomes under three designed failure scenarios with and without train operation adjustment, and evaluates how variations in the number of repair teams affect resilience loss, recovery time, and repair priorities. Using the Chengdu Metro network as a case study, the results show that, compared with strategies that do not consider turn-back operation adjustment, the proposed method reduces resilience loss by 17.9%, 16.0%, and 38.6% across the three scenarios. The results also indicate that increasing the number of repair teams shortens total recovery time and reduces resilience loss, although the marginal improvement gradually decreases. For example, in Scenario 1, resilience loss decreases from 2.3% to 0.7%, while total recovery time is reduced from 13.4 to 3.4. The main contribution of this study is the integration of turn-back-section dependency and repair-team constraints into a unified resilience-based recovery framework, which may serve as a reference for post-disruption recovery planning in urban rail transit systems. Full article

Metro Computer Interlocking Systems are crucial for ensuring the safe and efficient operation of rail transit. However, existing vision-based signal detection methods face challenges including small target sizes, high target density, low image resolution, and the need for deployment on resource-constrained devices. To address these issues, this paper proposes a two-stage lightweight signal detection framework for Metro Computer Interlocking Systems. First, based on YOLOv8, a small object detection layer together with feature fusion modules is introduced to form the YOLOv8-SFF architecture. A Scale Sequence Feature Fusion (SSFF) module is added to adjust the resolution of feature maps and retain critical fine-grained information, enhancing the detection of small visual signals. A Triple Feature Encoding (TFE) module is designed to enhance the recognition of dense small signals while replacing some traditional feature concatenation and upsampling operations, yielding a more compact network. Second, to enable practical deployment, a joint optimization strategy combining Layer-Adaptive Magnitude-based Pruning (LAMP) and Channel-wise Knowledge Distillation (CWD) is applied, in which the unpruned YOLOv8-SFF serves as the teacher and the pruned model serves as the student. In addition, an automatic annotation subsystem based on digital image processing is developed, leveraging color and morphological features to generate high-quality labels. Experimental results show that YOLOv8-SFF achieves a mean average precision (mAP@50) of 98.7%, improving mAP@50 by 11.3 percentage points and recall by 22.1 percentage points over the original YOLOv8. After joint pruning and distillation, the final compact model retains 98.0% mAP while reducing the parameter count by 86.2% and the model size to 1.1 MB, making it well suited for real-time deployment in resource-constrained metro dispatching systems. Full article

Taking the over-track development project of a metro depot in Chongqing as the engineering background, this study investigates the socket-type disc-lock full-hall scaffold system beneath the long-span transfer beam of Tower 9. A finite element model was established using MIDAS Civil to analyze the stress distribution and deformation characteristics of the scaffold system under construction loads, and the model was validated through field monitoring. On this basis, a parametric analysis was conducted to investigate the effects of erection height, step spacing of vertical standards, spacing between vertical standards, sweeping rod height, and joint stiffness on the overall stability of the scaffold system. A fitted analytical model for the buckling eigenvalue was further established. The results show that the scaffold system was mainly subjected to compression during construction. The measured maximum compressive stress of the vertical standards was 90.92 MPa, with an error of 12.50% compared with the finite element result of 80.82 MPa. The measured maximum tensile stress was 22.37 MPa, which was close to the calculated value of 21.96 MPa. The measured maximum average cumulative vertical displacement of the scaffold was 1.69 mm, which did not exceed the allowable deformation range during construction. The parametric analysis indicates that increases in erection height, step spacing of vertical standards, spacing between vertical standards, and sweeping rod height reduce the overall stability of the scaffold system, among which the step spacing of vertical standards has the most significant influence. In contrast, increasing joint stiffness is beneficial for enhancing the stability reserve. In this study, the overall stability of the scaffold system is characterized by the buckling eigenvalue obtained from linear eigenvalue buckling analysis. These findings can provide a reference for parameter selection, scheme comparison, and construction control of similar disc-lock high-formwork support systems for heavily loaded transfer beams. However, the conclusions of this study are mainly based on linear eigenvalue buckling analysis and single-factor parametric investigation, without further consideration of material nonlinearity and multi-parameter interaction effects. Full article

With the increasing development in urban underground spaces towards greater depth, scale, and complexity, the prediction and control of deformations in deep excavation engineering have become critical challenges in geotechnical engineering. This study investigates the ultra-deep excavation of the Tianjin Metro Line 8 Liulitai Station, analyzing the deformation characteristics of the retaining structure during top-down construction in soft soil based on field monitoring data. The results reveal a typical “bulging” pattern in the horizontal displacement of the diaphragm wall, which accumulates progressively with excavation depth. To enhance deformation prediction accuracy, a self-developed beam-plate finite element method (BPFEM) platform, implemented in Python (version 3.11.9), is introduced. The platform integrates code-specified analytical methods and the incremental approach to simulate the internal forces and deformations of the support system with high precision. By incorporating a dual-parameter back-analysis technique—adjusting both the horizontal subgrade reaction modulus and active earth pressure—the numerical model achieves significantly improved agreement with monitoring data. The proposed method demonstrates strong predictive capability, with a maximum error of only 4.4% in subsequent construction stages, confirming its feasibility and reliability for deformation forecasting in top-down deep excavations. The BPFEM framework and parameter inversion strategy presented herein provide an effective technical basis for intelligent prediction and dynamic control in deep excavation projects under complex geological conditions. Full article

This study investigates associations between socioeconomic and travel variables among users of the São Paulo metro, focusing on travels made for work and study purposes, which are expected to reflect regular commuting patterns, and identifies the main variables associated with mobility characteristics within this group. Using data from the 2017 Origin–Destination Survey conducted by the São Paulo Metro Company, a set of 10,522 respondents was analyzed. The statistical analysis employed Pearson correlation, factor analysis of mixed data (FAMD), and multiple linear regression. The findings indicate that both socioeconomic and travel variables were significantly associated with mobility characteristics among metro system users in the Metropolitan Region of São Paulo (RMSP). The main variables associated with these mobility characteristics were the distance between origin and destination, the distances to the respective stations, travel duration, age, study status, employment status, education level, Brazilian Criteria score, and number of vehicles. Based on the FAMD, these variables were organized into multiple dimensions that could be descriptively grouped into three main groups of information: travel burden and spatial accessibility; life-stage and educational/occupational profile; and life-stage and socioeconomic position. The socioeconomic composition of consistent metro users predominantly includes middle and middle-lower economic classes, with lower economic class, lower household income, and lower education levels being associated with longer travel distances and durations. The study also revealed that most metro travels are within 20 km, with an average travel time of 74 min. These findings suggest that improved infrastructure and better-distributed metro networks throughout the RMSP may contribute to enhancing accessibility, promoting social inclusion, and improving transportation equity. Full article

This paper examines the evolution of urban transport policy in Saudi Arabia from a car-dependent paradigm toward sustainability-oriented planning and early implementation between 2000 and 2025. Using a longitudinal qualitative analysis of national strategies, municipal plans, and giga-project documents, this study traces shifts in policy discourse, governance arrangements, and delivery evidence across three phases: an expansionist phase (2000–2015), a vision transition phase (2016–2020), and a sustainability implementation phase (2021–2025). These phases were selected to capture the transition from pre-Vision 2030 automobile-oriented planning to the early implementation of sustainability-oriented transportation reforms. The findings reveal a clear transition from road-expansion-oriented planning—characterized by highway development, fuel subsidies, and limited public transport—toward system performance, decarbonization, and multimodal integration. Recent years have seen the rollout of metro and bus networks, expansion of rail systems, early electrification of vehicles and public transport, and fuel price rationalization. However, persistent behavioral lock-in, low-density urban forms, climatic constraints, and complex multi-level governance arrangements continue to limit modal shift and equitable mobility outcomes. The findings suggest that infrastructure investment alone cannot achieve substantial modal shift without integrated land-use planning, feeder systems, and demand-management measures. By linking policy ambition to implementation pathways over time, this study provides transferable insights for sustainable mobility transitions in oil-dependent and arid urban contexts. Full article

Urban rail transit projects situated in Quaternary deposits are progressively influenced by ultra-shallow gas. During the investigation and construction phases, this gas may instigate gas outbursts, combustion, explosions, stratum disturbances, and secondary ground deformations. To transparently and applicably identify the most crucial disaster-inducing factors in engineering practice, this research constructs a hierarchical risk factor evaluation framework for shallow gas hazards during the investigation stage of rail transit engineering. Initially, candidate indicators were screened via a literature review of shallow gas hazard studies and metro engineering reports. Subsequently, by employing the AHP, four first-level indicators and fifteen second-level indicators were compared and weighted. The findings indicate that shallow gas pressure, methane content per ton of soil, and the occurrence form of shallow gas are the three most influential factors, with comprehensive weights of 0.2735, 0.2319, and 0.1113 respectively. A metro tunnel case in Guangdong Province was then utilized to illustrate how the ranked indicators can guide the verification of suspected zones, section-based hazard discrimination, and the planning of controlled gas release. In comparison with existing studies that concentrate on descriptive disaster phenomena or single-factor analyses, the contributions of this study are threefold. Firstly, it offers a structured indicator system specifically tailored to Quaternary shallow gas in rail transit engineering. Secondly, it makes the expert-based weighting process explicit. Thirdly, it links the ranking results to practical investigation and prevention decisions. This framework is intended as a preliminary engineering decision support tool rather than a substitute for detailed predictive modeling or large-sample statistical validation. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

Identifying critical stations is fundamental to improving the resilience and operational safety of urban rail transit networks. However, most existing identification methods—especially dynamic node removal approaches—assume that station failures affect only the failed node itself, thereby overlooking the cascading impacts caused by train turnback adjustments under bidirectional service interruptions. This simplification leads to systematic underestimation of stations with strong operational dependencies. To address this gap, this study proposes a framework for identifying critical station that explicitly incorporates bidirectional operational disruptions and the indirect failures they induce within turnback sections. This study is among the first to explicitly model turnback-related failure propagation within operational sections in critical station identification, providing a closer alignment with real-world rail transit operations. A comprehensive evaluation system is then constructed by integrating dynamic network connectivity indicators, network topology characteristics, and station attributes. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), combined with objectively determined indicator weights, is employed to synthesize multidimensional indicators and rank station importance. The method is applied to the Chengdu Metro network (12 lines and 282 stations). Results indicate that considering turnback related indirect failures substantially amplifies the measured impact of station disruptions on network connectivity. Critical stations are highly concentrated at intersections between the loop line and major radial lines, while several non-interchange stations within key turnback sections—such as Lijiatuo Station and Wannianchang Station—exhibit pronounced increases in importance rankings. Comparative analysis shows that the rankings of some stations change by more than 50% relative to the conventional node removal method, indicating that traditional approaches may significantly underestimate operationally critical stations associated with turnback sections. More importantly, the proposed method enables a direct comparison between structurally important stations and operationally critical stations under disruption scenarios. Overall, the proposed framework provides a more realistic and operation oriented identification of critical stations by explicitly accounting for train operation dependencies under bidirectional interruptions, offering practical insights for resilience assessment and emergency management of large scale urban rail transit networks. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Synchronous transfer has been widely studied in public transport scheduling, with most research focusing on coordination among conventional bus lines. However, with the rapid expansion of urban rail transit systems, metro–bus transfers have become increasingly important for enhancing overall urban public transport network performance. This study investigates the maximum synchronous transfer problem between metro and conventional bus services under passenger flow constraints. Considering the large transfer demand and the pulse-arrival characteristics of metro trains, a passenger waiting constraint at bus stops is incorporated to reflect capacity limitations and crowding effects. A passenger-flow-constrained maximum synchronization model is formulated to optimize bus departure times without increasing service frequency. Dongjiekou Metro Station and three surrounding pairs of bus stops are selected as a case study. Model parameters are determined through field surveys and operational data. The Grey Wolf Optimizer (GWO) and a simulated annealing–improved Grey Wolf Optimizer (SA-IGWO) are employed to solve the proposed model. The results show that both algorithms significantly improve synchronized transfer volumes by adjusting departure times without increasing service frequency. Compared with the original schedule, the SA-GWO achieves an improvement in synchronization performance ranging from 45% to 50%, outperforming the standard GWO. Full article

To investigate the deformation law of adjacent metro tunnels under the coupling effect of servo supports and deep foundation pit excavation, this study takes an ultra-deep foundation pit adjacent to Hangzhou Metro Line 2 as the research object. A servo support system was adopted for synchronous active loading during excavation, and field monitoring was conducted to analyze the deformation response of existing operating tunnels before and after servo loading. The results indicate that servo loading significantly reduces the rate of increase in tunnel vertical displacement, horizontal displacement, and horizontal relative convergence. It is found that the servo support closest to the tunnel (i.e., the third servo support in the case) exhibits the most prominent control effect—after loading, the vertical displacement rate of the down-line tunnel decreases from −0.04 mm/d to 0 mm/d, and the horizontal displacement rate is reduced by approximately 70%. Moreover, seven days after loading, the horizontal relative convergence rate of the up-line tunnel tends to be 0 mm/d. Servo supports effectively weaken the tunnel’s deformation development during critical stages of ultra-deep foundation pit construction, enabling active and precise control of adjacent operating metro tunnels. Full article