Search Results (284)

The construction of deep foundation pits for urban metro systems, particularly when in close proximity to existing sensitive structures, can easily induce unacceptable settlement and deformation. When shield tunneling and the top-down method are superimposed in both space and time, the resulting disturbance to the surrounding environment is more complex. A metro station project in Shenzhen was selected as the case study. Field monitoring and numerical simulation were combined to analyze the deformation of nearby bridge piers during different construction stages. The settlement-control effects of different reinforcement measures were also compared. The results indicate that the maximum settlement of the bridge piers throughout the monitored construction period reaches 31.81 mm. In the third stage, the cumulative settlement exceeded 10 mm. After the left-line shield passed through the station, the differential settlement between adjacent piers exceeded 5 mm. In the fourth stage, upon completion of the south section of the negative third floor, the differential settlement within the same pair of piers exceeded 4 mm. The maximum differential settlement between adjacent piers on the side closer to the foundation pit is 8.92 mm, whereas that on the side farther from the pit is 12.53 mm. The maximum values of both the differential settlement between adjacent piers and that within the same pair of piers occur during stages where multiple construction processes overlap. The connections between successive construction procedures are thus identified as weak links in deformation control. The discrepancy in cumulative settlement between symmetrically located piers arises primarily from the different reinforcement methods adopted. Supplementary pile reinforcement provides better performance than sleeve-valve-pipe grouting reinforcement. Because its reinforcement depth is insufficient to extend below the foundation pit excavation surface, the latter method fails to effectively restrain deformation in deep soils. The results describe the spatio-temporal development of pier deformation under combined construction disturbances. They also help identify critical construction stages and compare the settlement-control effects of different reinforcement measures. In addition, the numerical results provide a possible interpretation of pile load-transfer behavior under the combined effects of shield tunneling and top-down excavation. These findings serve as a practical reference for the design, construction sequencing, and risk management of similar metro station projects adjacent to bridges. Full article

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this paper proposes a Tensor Network Extended Kalman Filter (TNEKF) framework for short-term metro OD demand forecasting. First, metro OD demand is formulated as a nonlinear dynamic state-space prediction problem, where a multi-input multi-output Volterra model is adopted to characterize the nonlinear relationship between historical passenger demand and future OD flows. An Extended Kalman Filter (EKF) is then developed to recursively estimate the latent model parameters and continuously refine demand prediction using newly available observations. To improve computational efficiency for high-dimensional OD systems, both the latent state vector and covariance matrix are represented using low-rank tensor network structures, and all recursive filtering operations are implemented through tensor-network contractions without explicitly constructing large-scale matrices. Experiments on real-world smart-card data from the Hangzhou metro system demonstrate that the proposed method consistently outperforms ARIMA, conventional EKF, and several state-of-the-art spatiotemporal prediction models in terms of MAE, RMSE, and MAPE. Compared with the best-performing baseline of the whole-day scenario, the proposed method reduces MAE, RMSE, and MAPE by 30.2%, 9.8%, and 6.3%, respectively. Furthermore, the proposed framework exhibits strong robustness under disruption scenarios, demonstrating its effectiveness and scalability for large-scale metro OD demand forecasting. Full article

Urban last-mile delivery is a rapidly growing source of city-centre emissions, and decarbonizing it without eroding service quality has become imperative for climate goals. Operators are turning to multimodal systems that integrate road vehicles, off-peak metro freight, and electric drones—yet the optimal delivery channel varies dynamically with location and time. Current RL-based schedulers handle constraints via manually tuned penalty weights, lacking formal safety guarantees, and the feasibility of online carbon-cap enforcement under partial observability remains an open question. To address this, we model the problem as a Constrained Markov Decision Process (CMDP) and propose a demonstration-regularized Lagrangian deep RL algorithm. Our approach learns an online policy that is model-free at deployment—it controls emissions in expectation against a hard carbon budget, makes per-order decisions using only state observations, and operates without an emission model at test time (the demonstrator used at training time does access the emissions model, so “model-free” refers strictly to the deployment phase). Experiments on synthetic benchmarks and a Nanjing-inspired scenario—grounded in real metro topology and population-weighted demand—show that our policy achieves emissions within 1.3% of the offline optimum. It robustly tracks a ±17% carbon-budget band across a threefold daily volume range and a threefold city-scale range, with zero per-instance tuning. By contrast, a standard PPO with fixed penalty weights consistently degrades to single-mode selection. Our findings suggest that hard carbon budgets can be controlled in expectation online at modest cost—a step toward operator-facing low-carbon logistics whose average emissions honour a binding carbon budget, though external validation on operational data and a risk-sensitive formulation that upgrades this average control into per-day compliance are still required before deployment. Full article

This study develops and applies an adapted New Urbanism–Transit-Oriented Development (NU–TOD) framework to evaluate spatial, functional, and climatic performance in Al Malqa District, Riyadh. Integrating NU–TOD principles with hot-arid microclimate research, Saudi sociocultural norms, and Vision 2030 governance, the study employs a convergent mixed-methods design that combines Geographic Information Systems (GIS)-based land-use and network analyses, shading and microclimate indicators, and policy review. Results show that Al Malqa benefits from strong civic anchors and proximity to a Line 1 metro station but exhibits limited walkability, low station-area densities, constrained mixed-use intensity, and insufficient shading, which together reduce effective walking catchments and transit viability. A phased intervention strategy is proposed: short-term shading and feeder-mobility improvements; medium-term TOD zoning, parking reform, and public-realm upgrades; and long-term mixed-use redevelopment and district-scale green infrastructure. A reproducible Multi-criteria Decision Analysis (MCDA) feasibility assessment (impact, regulatory ease, cost, timeline) prioritises shaded pedestrian corridors and feeder mobility as the most implementable early actions. The study contributes a replicable evaluation model for hot-arid cities and offers actionable guidance for Vision 2030, aligned with suburban intensification in Riyadh. Full article

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. 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

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

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 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

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

With the rapid development of urban rail transportation, environmental vibrations caused by subways operating are becoming more serious. They have affected people’s living comfort, the stability of precision scientific instruments, and the safety of buildings. In order to make a good and real prediction of metro-induced vibration, this paper constructs a kind of analytical–numerical hybrid method to evaluate the dynamic response of the whole vehicle–track–tunnel–soil system. First, an analytical metro vehicle–track coupled dynamic model is established according to the Zhai method, and then the wheel–rail force acting on trains can be acquired. And then we use a 3D numeric model which incorporates the track, tunnel and ground with a finite element method. At last, the wheel–rail force is used as an excitatory load, and then the train–track–tunnel–ground coupling system dynamic response analytic–numeric model can be created. Based on this, the influence of track quality level, train speed and tunnel burial depth on the characteristics of ground surface vibration is studied in sequence. From the results, we can see that given the same track quality condition, increasing train speed greatly increases the response amplitude on the ground surface; improving track quality is very effective to reduce response amplitude; and with the tunnel buried deeper, the vibration level would be obviously reduced, local peak response would be suppressed and deep tunnels would have better vibration suppression under higher-speed operation conditions. Full article

Accurate short-term metro passenger flow forecasting plays a key role in urban transit management, supporting train scheduling, crowd control, and operational planning. Jointly modeling station-level inflow/outflow (IO) and inter-station origin–destination flows (OD/DO) has proven effective for improving prediction accuracy, as it allows the model to leverage dependencies across different flow granularities. However, effectively exploiting such dependencies remains nontrivial. Station-level intensity (IO) and inter-station migration patterns (OD/DO) differ substantially in both representation and dynamics, and the dependencies between them are inherently directional and uneven. As a result, commonly used parameter-sharing mechanisms in multi-task learning are often insufficient to capture informative cross-task interactions. To address this issue, we propose CATI (Cross-Attention-based Task Interaction), a unified framework for joint multi-granular metro flow forecasting. CATI first learns task-specific spatiotemporal representations for IO, OD, and DO flows, and then introduces directed cross-attention with Gated Residual Fusion to model selective and asymmetric interactions across tasks. In addition, an aggregation-consistency regularization is employed to maintain structural coherence between station-level and inter-station predictions. Experiments on real-world metro datasets from Hangzhou and Shanghai show that CATI consistently outperforms strong baselines across multiple prediction horizons and tasks. Further analysis indicates that the model learns adaptive attention patterns, task-dependent gating behaviors, and controlled interaction strengths, which together explain its improved performance. These results suggest that explicitly modeling asymmetric cross-task interactions is important for multi-granular spatiotemporal forecasting in metro systems. Full article