This study proposes U-GRU, a staged forecasting model that integrates node-wise gated recurrent temporal encoding, a one-dimensional U-Net-based ordered-node feature transformation module, external-feature alignment and channel–temporal recalibration for short-term traffic speed forecasting.
Abstract
Short-term traffic speed forecasting based on sensor-derived traffic signals is important for route guidance, adaptive signal control, congestion management and intelligent transportation services. Accurate prediction remains challenging because urban traffic states are influenced by nonlinear temporal dynamics, inter-node relationships, heterogeneous contextual information and uncertainty in external conditions. This study proposes U-GRU, a staged forecasting model that integrates node-wise gated recurrent temporal encoding, a one-dimensional U-Net-based ordered-node feature transformation module, external-feature alignment and channel–temporal recalibration. Unlike graph-convolutional methods that explicitly propagate information through a predefined or learned road topology, U-GRU investigates whether a transformation path along the stored node order in the data files can provide a useful representation under an ordered-node assumption. POI-related and weather-related auxiliary matrices are used as external representations provided in the public dataset; because field-level semantic metadata are unavailable, they are treated as dataset-level auxiliary inputs rather than manually interpreted variables. The experiments use chronological train/validation/test splitting, validation-based checkpoint selection, five random seeds for U-GRU, an expanded baseline set including the STGformer baseline evaluated under the same SZ-taxi setting, ablation studies, weather-validity analysis and node-order sensitivity analysis. On the SZ-taxi dataset, U-GRU reports MAE values of 2.70–2.72, RMSE values of 4.04–4.12 and R2 values of 0.83–0.85 across the 15–60 min forecasting horizons. The results suggest comparable or slightly better short-term forecasting performance under the evaluated dataset and protocol, while the conclusions remain dataset-specific and should be further examined on additional traffic networks.
Urban traffic flow is difficult to forecast accurately because its evolution is non-linear and governed by dependencies that operate over different spatial and temporal ranges. This paper introduces the Multi-Perspective Spatio-Temporal Feature Fusion Model (MPSTFFM) to describe these dependencies through complementary views. The temporal signal is separated into a slowly varying trend and a residual fluctuation, while the spatial structure is represented by four graphs: first-order adjacency, second-order in-degree, second-order out-degree, and a data-adaptive graph. These graphs respectively encode physical road connectivity, common inflow sources, common outflow destinations, and latent spatial associations. Whereas the first three are constructed from the known network topology, the adaptive graph is learned together with the prediction model and can therefore identify correlations not expressed by physical links. Within each spatio-temporal view, self-attention captures dependencies over long ranges, and convolutional operations extract local patterns. The features learned from all views are subsequently fused into a high-dimensional representation used to predict future flow. Experiments on real-world datasets compare MPSTFFM with twelve methods published during the preceding five years. On these benchmarks MPSTFFM outperforms every baseline, lowering the average MAE, RMSE, and MAPE across the four datasets by 13.04%, 5.28%, and 9.59%, respectively, relative to the best baseline on each one.
A. Marakhimov, Rustem Jalelov, J.K. Kudaybergenov et al.· Italian National Conference...· 0 citations
A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.
Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al.· International journal of res...· 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
Short-term traffic flow forecasting becomes especially difficult when incomplete observations, within-window frequency variation, and state-dependent sensor relations occur together. Missing readings can affect both node features and the spatial dependencies inferred from them, yet these issues are commonly modeled separately. We therefore propose RIFT-STGNN, a Robust Interleaved Frequency–Trend Spatio-Temporal Graph Neural Network for multi-step traffic flow forecasting. RIFT-STGNN follows a coordinated information flow: observation status is retained during temporal–frequency encoding, the frequency representation supports both node features and graph construction, and four graph sources are fused before each Graph-GRU update. Trend-aware temporal attention then produces direct multi-step forecasts. Experiments on PEMS03, PEMS04, PEMS07, and PEMS08 show competitive numerical performance relative to selected literature-reported baselines under the 12-step setting. On PEMS04 and PEMS08, the three-run mean MAE values are 17.73 and 13.14, respectively. These values are numerically 4.63% and 9.00% lower than the corresponding literature-reported sAMDGCN values. Component ablations, state-dependent graph analysis, and controlled missing-rate experiments support the roles of dynamic graph learning, within-window frequency encoding, and mask-aware input handling under the evaluated settings.
Qianxin Xie, Jinfeng Xu, Yuchen Lu et al.· Mathematics· 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
Urban traffic congestion remains a critical challenge for smart city development, particularly in the context of real-time monitoring and prediction for intelligent transportation systems. To address this issue, this study proposes a Digital Twin-based urban traffic prediction framework using a lightweight Diffusion Convolutional Recurrent Neural Network (DCRNN-Lite). The proposed model integrates spatial dependencies among road segments through diffusion convolution and temporal traffic dynamics through recurrent modeling, enabling effective spatiotemporal traffic forecasting with reduced computational complexity. Experiments were conducted on the real-world METR-LA dataset, consisting of traffic speed data from 207 sensors deployed across the Los Angeles highway network. The experimental results demonstrate that DCRNN-Lite achieves stable prediction performance, as reflected by low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), consistent convergence behavior, and strong correlation between predicted and actual traffic speeds at both node and city-wide levels. Despite its simplified architecture, the model effectively captures local traffic variations and global mobility trends, making it suitable for real-time deployment. The findings indicate that the proposed approach provides a favorable balance between accuracy and efficiency, highlighting its potential as a core component for digital twin-enabled smart cities and metaverse-based urban traffic management systems.
Hussain Ahmad Awad· International Journal Resear...· 0 citations
