A feature-enhanced Informer framework that embeds Complex Network Representation (CNR) into the Informer architecture to jointly capture both the topological characteristics of the subway network and spatiotemporal passenger flow dynamics is developed, thereby enabling efficient multi-step passenger flow forecasting.
Abstract
As urban rail transit systems enter a stage of high-quality development, accurate short-term passenger flow forecasting has become essential for efficient operation and management. To improve the accuracy and robustness of multi-step short-term passenger flow forecasting under fluctuating demand conditions, this study develops a feature-enhanced Informer framework that embeds Complex Network Representation (CNR) into the Informer architecture to jointly capture both the topological characteristics of the subway network and spatiotemporal passenger flow dynamics. External factors, including subway schedules and land use around stations, are further integrated to enrich the input features. In addition, the ProbSparse self-attention mechanism is adopted to improve long-sequence dependency modeling, thereby enabling efficient multi-step passenger flow forecasting. Experiments were conducted on the Beijing metro passenger flow dataset from January to October 2024 to evaluate the proposed model. The dataset covered 264 stations and was aggregated at 15 min intervals. Based on historical passenger flow and multi-source features, the model predicts passenger flow over multiple future time steps. The overall evaluation metrics were calculated on the test set and averaged over all test samples and observed stations. The experimental results show that, compared with the standard Transformer model, the proposed model reduces the average prediction error by 16.59% on weekdays and 20.48% on weekends while maintaining stable predictive performance during peak hours. Sensitivity analysis and ablation studies are further conducted to evaluate the model performance across different station types and forecasting horizons. The results demonstrate that the proposed model can provide reliable decision support for intelligent urban rail transit operations, including transport capacity scheduling, passenger service improvement, and operating cost reduction.
To predict the multi-step urban rail transit passenger flow, we propose a dual-graph spatio-temporal network (DSGN). This network can perform station-level passenger flow prediction and is beneficial for real-time operation of the stations. Predicting multiple future time steps remains challenging due to complex spatial dependencies and nonlinear temporal dynamics. The DSGN designed in this paper adopts a decoupled architecture to separately handle the passenger flow signals and time covariates. It performs parallel convolution on the physical topology graph, applies adaptive convolution on the learned graph, and then fuses through gates. The model also incorporates dilated causal convolution and attention pooling to model temporal dynamics. To validate the model, we conducted tests on the rapid transit dataset of the Massachusetts Bay Transportation Authority (MBTA) (121 stations, 30-minute resolution). The results showed that DSGN achieved a Mean Absolute Error (MAE) of 34.67 and weighted average absolute percentage error (WMAPE) of 18.60%, outperforming the competing spatio-temporal baseline models, and having lower variance across seeds. To further explore the model, we also designed ablation experiments, which showed that dual-graph fusion, time attention pooling, and decoupled feature encoding can all improve the model performance, while replacing the learned node embeddings with demographic priors would reduce accuracy and training stability.
Chaohan Zhong, Jiayu Shao· 2026 IEEE International Conf...· 0 citations
Reliable traffic flow forecasting is a core component of intelligent transportation systems; however, many current approaches are still unable to simultaneously model spatial interdependencies and long-term temporal correlations, particularly in cross-sea corridors that exhibit directional heterogeneity and pronounced temporal variability. This study aims to develop an accurate and stable traffic flow prediction framework for cross-sea corridors. To achieve this, an HGTransformer model was proposed that constructed a hypergraph from traffic nodes based on spatial proximity and correlated flow variations, and used hypergraph convolution to extract spatial node representations. These representations were then fed into a Transformer equipped with multi-head self-attention and positional encoding, enabling the model to capture global temporal dependencies in the evolution of traffic flow. Using hourly traffic flow data from the Huangmaohai cross-sea corridor, the model was tested on 1 to 4 h forecasting horizons and compared with long short-term memory (LSTM), multi-layer perceptron (MLP), random forest (RF), support vector regression (SVR), and Bayesian regression (BR) models. The proposed model achieved the best overall performance, with average mean absolute percentage error (MAPE), mean absolute error (MAE), weighted mean absolute percentage error (WMAPE), and root mean square error (RMSE) of 0.178, 13.375, 0.140, and 20.538, respectively. At the 1 h horizon, these values further decreased to 0.172, 12.678, 0.132, and 19.401, while preserving peak–valley structures more accurately under both short- and longer-horizon forecasting. The main contribution of this study lies in the systematic application and validation of the Huangmaohai Corridor dataset, including a reproducible hypergraph construction strategy tailored specifically for this particular infrastructure.
Fan Jiang, Zhiyong Ma, Pumulo Mukozomba et al.· Applied Sciences· 0 citations
An integrated prediction-and-visualisation pipeline that transforms complex data distributions into actionable visual analytics, such as interpretable station-to-station demand heatmaps via interactive GIS Folium layers is implemented, providing an operationally robust framework to support smart-city transportation management and build more sustainable urban transit systems.
Berna Çalışkan· Journal of Data Analytics an...· 0 citations
Accurate traffic flow prediction is the core foundation of Intelligent Transportation Systems (ITS) and urban traffic dynamic optimization. However, existing mainstream prediction models have three critical limitations: poor cross-road transferability, mandatory separate training for individual road sections, and insufficient mining of historical traffic peak information, which jointly restrict prediction accuracy and engineering scalability in practical urban traffic management. To address these challenges, this article proposes the Multi-road Multi-Peak-aware Informer (MMP-Informer), an improved Informer-based long-term time series prediction model with a novel dual-peak-aware attention mechanism. This core module adaptively locates historical maximum and minimum traffic peaks
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an adaptive window matched to input sequence length, introduces learnable parameters to weight the influence of different peaks on future traffic flow, and realizes single-model end-to-end simultaneous multi-road traffic flow prediction. Meanwhile, the model retains the encoder-decoder architecture of the original Informer, integrates self-attention distillation in the encoder to reduce computational overhead, and adopts a generative decoder paradigm to avoid error accumulation in multi-step prediction. Experiments on the 2018 University of Technology Sydney (UTS) multi-source traffic dataset show that MMP-Informer consistently outperforms state-of-the-art baselines including SCINet, NsTransformer, Transformer, and original Informer across 24-, 48-, and 96-h prediction horizons. For 24-h short-term prediction, it achieves a Mean Absolute Error (MAE) of 25.43 and Mean Absolute Percentage Error (MAPE) of 11.55%; for 96-h long-term prediction, it reaches an MAE of 31.21 and MAPE of 12.93%, where the MAE is 2.51 lower and MAPE is 1.97 percentage points lower than the original Informer, respectively. Ablation tests verify that the dual-peak-aware design significantly enhances the model’s adaptability to both low-traffic and high-traffic scenarios, balancing prediction accuracy and long-term forecasting stability. Supplementary experiments on merged Electricity Transformer Temperature hourly (ETTh1/ETTh2) datasets further confirm its stable and superior performance in non-traffic time series scenarios, with strong cross-scenario generalization ability. The proposed model eliminates the high cost of single-road separate training, breaks through the cross-road transferability bottleneck of traditional models, maintains stable accuracy in both short-term and long-term prediction, and has high practical engineering value for large-scale deployment in urban intelligent traffic management.
This study proposes an enhanced key-node-driven framework for traffic flow prediction in large-scale transportation networks. Building upon the classical K-shell decomposition, the proposed method integrates traffic-flow-based weighting to jointly capture structural hierarchy, functional relevance, and global topological influence of network nodes. By ranking node importance through composite indicators lambda-c, lambda-f, and lambda-s, the framework identifies structurally dominant nodes and evaluates their effectiveness across multiple traffic prediction models. Comprehensive experiments conducted on three benchmark datasets—PEMS04, METR-LA, and PEMS-BAY—demonstrate that graph-based spatiotemporal models such as ST-GCN, GraphWaveNet, DCRNN, STDN, STTN, and SWAVE maintain high predictive accuracy even under reduced node coverage. In particular, DCRNN exhibits strong robustness in capturing dynamic spatiotemporal dependencies, while STTN effectively models long-term temporal patterns. Overall, the results highlight the robustness and versatility of the proposed framework in real-world traffic scenarios, demonstrating its potential to enhance prediction accuracy and scalability through key-node identification and selective coverage analysis.
Jing Gan, Dongmei Yan, Yue Wang et al.· Systems· 0 citations
A multi-source machine learning framework for segment-direction-level prediction in the Denizli city center showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value.
Muhammed Enes Karaoğlan, Yetis Sazi Murat· Sustainability· 0 citations