Search Results (64)

With the rapid growth of urban populations and the expansion of metro networks, accurate energy consumption prediction has become a critical task for optimizing metro operations and supporting low-carbon city development. Traditional statistical and machine learning methods often struggle to model the complex, nonlinear, and time-varying nature of metro energy data. To address these challenges, this paper proposes MTMM, a novel hybrid model that integrates the multi-head attention mechanism of the Transformer with the efficient, state-space-based Mamba architecture. The Transformer effectively captures long-range temporal dependencies, while Mamba enhances inference speed and reduces complexity. Additionally, the model incorporates multivariate energy features, leveraging the correlations among different energy consumption types to improve predictive performance. Experimental results on real-world data from the Guangzhou Metro demonstrate that MTMM significantly outperforms existing methods in terms of both MAE and MSE. The model also shows strong generalization ability across different prediction lengths and time step configurations, offering a promising solution for intelligent energy management in metro systems. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article

With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrasonic testing, magnetic flux leakage) are limited in terms of efficiency and environmental adaptability. This study proposes a machine vision-based approach leveraging deep learning to identify four primary types of rail damages: corrugations, spalls, cracks, and scratches. A self-developed acquisition device collected 298 field images from the Chongqing Metro system, which were expanded into 1556 samples through data augmentation techniques (including rotation, translation, shearing, and mirroring). This study systematically evaluated three object detection models—YOLOv8, SSD, and Faster R-CNN—in terms of detection accuracy (mAP), missed detection rate (mAR), and training efficiency. The results indicate that YOLOv8 outperformed the other models, achieving an mAP of 0.79, an mAR of 0.69, and a shortest training time of 0.28 h. To further enhance performance, this study integrated the Multi-Head Self-Attention (MHSA) module into YOLO, creating MHSA-YOLOv8. The optimized model achieved a significant improvement in mAP by 10% (to 0.89), increased mAR by 20%, and reduced training time by 50% (to 0.14 h). These findings demonstrate the effectiveness of MHSA-YOLO for accurate and efficient rail damage detection in complex environments, offering a robust solution for intelligent railway maintenance. Full article

Urban transportation systems, particularly integrated bus–metro networks, play a critical role in sustaining city functions but face significant vulnerability during extreme flood disasters. Taking Zhengzhou, China, as a case study, this study developed a comprehensive assessment model to evaluate the resilience of urban bus–metro hybrid networks under flood scenarios. First, a complex network-based bus–metro hybrid transportation network model was established, incorporating quantifiable flood disaster risk indices considering disaster-inducing factors, hazard-prone environments, and disaster-bearing entities. A cascading failure model was then constructed to simulate the propagation of node failures and passenger load redistribution during flood events. Subsequently, network resilience was evaluated using the topological metric of the relative size of the largest connected component and the functional metric of global efficiency. The analysis examined the influence of the load capacity sensitivity parameters α and β on resilience outcomes. Simulation results indicated that the parameter combination α = 0.8 and β = 2.0 yielded the highest resilience under the tested conditions, offering a balance between redundancy and the targeted protection of high-load nodes. Additionally, recovery strategies prioritizing nodes based on betweenness centrality significantly improved resilience outcomes. This study provides valuable insights and practical guidance for improving urban transportation resilience, assisting policymakers and planners in better mitigating flood disaster impacts. Full article

In a traction power supply system, the design of traction substations significantly influences both the system’s operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction. Full article

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R² compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R² values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability. Full article

Accurately predicting travel mode choice is crucial for effective transportation planning and policymaking. While traditional approaches rely on discrete choice models, recent advancements in machine learning offer promising alternatives. This study proposes a novel convolutional neural network (CNN) architecture optimized using orthogonal experimental design to predict travel mode choice. Using the SwissMetro dataset, which represents a specific intercity travel context in Switzerland, we evaluate our CNN model’s performance and compare it with traditional machine learning algorithms and previous studies. The key innovations of our study include: (1) an optimized CNN architecture designed to capture complex patterns in travel behavior data, and (2) the application of orthogonal experimental design to efficiently identify optimal hyperparameter settings. The results demonstrate that the proposed CNN model significantly outperforms logit models, support vector machines, random forests, gradient boosting, and even state-of-the-art techniques combining discrete choice models with neural networks. The optimized CNN achieves a remarkable 95% accuracy, surpassing the best-performing benchmarks by 14–25%. The proposed methodology offers a powerful tool for understanding travel behavior, improving travel demand forecasting, and informing transportation planning decisions. Our findings contribute to the growing body of literature on machine learning applications in transportation and pave the way for further advancements in this field. Full article

The purpose of this paper is to optimize the accuracy of airport express passenger flow prediction so as to meet the need for the optimal allocation of traffic resources against the background of accelerated urbanization and the rapid development of airport express services. A fusion intelligence network model (FINM) is proposed, which integrates the advantages of convolutional neural networks, bidirectional long short-term memory networks, and gated recurrent units. Firstly, by using the powerful feature extraction ability of convolutional neural networks, local features and detail information are captured from the input data to improve the data representation ability. Secondly, bidirectional long short-term memory networks are used to process the sequence data, capture the global information and its context relationship, and enhance the model’s understanding of the dependence of time series data. Finally, gated recurrent units are introduced to simplify the computational complexity while maintaining high prediction accuracy and training efficiency. Based on the actual passenger flow data for Tianjin Metro Line 2 on a 30 min time scale, the proposed FINM is verified. The experimental results show that the model achieves an excellent performance, with 0.0160, 0.0947, 0.0160, 0.1255, 18.40, and 0.7788 in key indicators such as loss value (Loss Value), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-Squared). Compared with the comparison algorithm, this model shows significant advantages in all indicators, which proves its effectiveness in dealing with complex time series data. Full article

Tunnel boring machines (TBMs) are essential for excavating metro tunnels, reducing disruptions to surrounding rock, and ensuring efficient progress. This study examines how machine learning (ML) models can predict key tunneling outcomes, focusing on making these predictions clearer. Specifically, the models aim to predict surface settlements (ground sinking) and the TBM’s penetration rate (PR) during the Athens Metro Line 2 extension to Hellinikon. For surface settlements, four artificial neural networks (ANNs) were developed, achieving an accuracy of over 79%, on average. For the TBM’s PR, both an XGBoost Regressor (XGBR) and ANNs performed consistently well, offering reliable predictions. This study emphasizes model transparency mostly. Using the SHapley Additive exPlanations (SHAP) library, it is possible to explain how models make decisions, highlighting key factors like geological conditions and TBM operating data. With SHAP’s Tree Explainer and Deep Explainer techniques, the study reveals which parameters matter most, making ML models less of a “black box” and more practical for real-world metro tunnel projects. By showing how decisions are made, these tools give decision-makers confidence to rely on ML in complex tunneling operations. Full article

The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R² = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability. Full article

Urban metro networks, characterized by their complex systems of interdependent components, are susceptible to a wide range of operational disturbances and threats. Such disruptions can cascade through the system, leading to service delays, operational inefficiencies, and substantial economic losses. Consequently, assessing and understanding network vulnerabilities have become crucial to ensuring resilient metro operations. While many studies focus on single-failure scenarios, comparative vulnerability analyses of various urban metro networks under multiple or simultaneous failures remain limited. To address this gap, our study introduces a comprehensive analytical framework comprising three key components: quantitative indices operating at both network and node levels, methodological approaches to assess the importance of network components (nodes, edges, and lines), and systematic protocols for evaluating vulnerabilities across multiple failure scenarios (stations, tunnels, lines, and areas). A comparative analysis of the Shenzhen Metro Network (SZMN) and the Zhengzhou Metro Network (ZZMN) validates the proposed methods. The results indicate that the SZMN demonstrates higher connectivity and accessibility than the ZZMN, despite a lower network density. Both networks are disassortative and heterogeneous, with edges connecting multiline transfer stations showing significantly higher edge betweenness centrality compared to those connecting general stations. In the SZMN, 6.63% of node failures and 4.74% of tunnel failures exceed a vulnerability threshold of 0.03, compared to 13.74% and 11.27% in the ZZMN. Failures across different lines and areas yield varying impacts on network performance and vulnerability. This study provides essential theoretical and practical insights, helping metro safety managers identify vulnerable points and strengthen the sustainable development of urban metro systems. Full article

Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians’ implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models. Full article

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models. Full article

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model. Full article

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of $S_i$ can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks. Full article