2026· International journal of research and scientific innovation· 0 citations
TL;DR
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.
Abstract
Short-term traffic forecasting plays an important role in intelligent transportation systems, as applications such as route guidance, adaptive traffic signal control, emergency response, congestion mitigation, and logistics planning depend on accurate estimates of future traffic states. Recent spatio-temporal graph neural network models, particularly Diffusion Convolutional Recurrent Neural Network (DCRNN) and Spatio-Temporal Graph Convolutional Network (STGCN), have improved traffic prediction by representing road sensors as graph nodes and jointly learning spatial and temporal relationships. However, strong performance on clean benchmark datasets does not always guarantee reliability in real-world conditions, where sensor reading may be missing, noisy, or unavailable due to hardware faults or communication issues. This paper provides a robust focused comparative evaluation of seven traffic forecasting approaches: Persistence, Historical Average, ARIMA, Random Forest, LSTM, STGCN, and DCRNN. Experiments are conducted on the METR-LA and PEMS-BAY speed datasets using 5-minute data intervals, a 12-step historical input sequence, and a 12-step forecasting horizon. Under clean-data conditions, DCRNN achieves the best overall MAE on both datasets, with 3.548 mph on METR-LA and 1.905 mph on PEMS-BAY. However, when 40% random missing input corruption is introduced, STGCN shows greater robustness than DCRNN; for METR-LA, STGCN’s MAE increases by 27.0%, while DCRNN’s MAE increases by 34.5%. Under complete sensor-failure conditions, the model ranking changes further, with the LSTM baseline showing greater stability than both graph-based models on METR-LA. Graph ablation analysis also indicates that temporal modeling accounts for most of the forecast improvement, while the evaluated graph topology provides only limited additional benefits. These findings suggest 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.
The ability to forecast traffic conditions in urban environments is essential for intelligent transport systems because it provides proactive congestion management, traffic control and informed urban mobility planning. However, due to the extreme spatial and temporal volatility of traffic flow patterns, conventional statistical, machine learning, and sequence-based deep learning approaches either fail to account for the spatial relationships between highway segments or fail to sufficiently model long-range temporal dynamics. To address these gaps, the present study uses a Spatial-Temporal Graph Neural Network (STGNN) framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow. The model has a graph-based architecture where graph-convolutional layers are combined with gated recurrent units and transformer-based attention units, thus creating a hybrid architecture that is capable of multi-scale spatio-temporal dependencies. The METR-LA dataset was used in experiments, and it was observed that STGNN had smaller Mean Absolute Percentage Error(MAPE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) at 15, 30, and 60 minute horizons of prediction than the baseline and the implemented models: Sequence to Sequence (Seq2Seq) and Temporal Graph Convolutional Network (T-GCN). The model achieved the best results for the 15-minute interval with an MAE of 2.71, an RMSE of 5.17, and a MAPE of 7.08. The results imply that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecasting stability, which underlines the potential STGNN-based traffic prediction systems have to contribute to real-time traffic control.
Traffic congestion is a growing challenge in urban environments, driven by increasing population and vehicle density, leading to significant economic and societal impacts. Accurate traffic forecasting is a key component of Cooperative Intelligent Transportation Systems (C-ITS), enabling proactive traffic management strategies such as adaptive signal control and dynamic routing. However, existing approaches often struggle to capture spatial dependencies in road networks and long-range temporal dynamics in traffic data. This paper proposes TETRA, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM). By incorporating matrix-based memory and memory mixing, xLSTM enables the model to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models. The proposed approach is evaluated on a real-world urban traffic dataset and the widely used METR-LA benchmark. Experimental results show that TETRA outperforms or matches established baseline models representative of the main spatio-temporal paradigms, with the most pronounced gains at medium- and long-term horizons, achieving up to 13.0% lower MAE, 20.0% lower RMSE, and 6.0% higher
$$R^2$$
on a real-world dataset relative to the strongest investigated baseline at each horizon. Statistical analysis confirms that these improvements are robust across prediction horizons. Additional evaluations, including ablation and sensitivity analyses, demonstrate the effectiveness and scalability of the proposed model.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
Accurate short-term traffic prediction is a critical component of intelligent transportation systems (ITS), yet it remains challenging due to nonlinear temporal dynamics, evolving spatial dependencies, and uncertainty in real-time urban traffic data. This paper proposes a novel uncertainty-aware deep ensemble spatiotemporal forecasting framework integrating Dynamic Graph Convolutional Networks (DGCN), Temporal Transformers, and CNN–LSTM hybrid models. A confidence-guided ensemble fusion strategy dynamically weights individual predictions using Bayesian uncertainty estimation. Experiments conducted on real-time Bhopal city traffic data demonstrate significant improvements over state-of-the-art baselines, achieving up to 90% performance gains during peak and abnormal traffic conditions.
: With the progress of the system of intelligent transportation, traffic flow forecasting is essential to achieve efficient traffic management and control. It can not only optimize real-time traffic flow and improve travel efficiency, but also provide a basis for long-term road network planning and reduce carbon emissions. However, present models are unable to acquire the complicated spatio-temporal dependency of data, leading to low prediction accuracy. Therefore, this research uses a prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN). This model combines channel attention, spatial attention, and temporal attention mechanisms, and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model. The trial results display the performance of HASTGCN on the PEMS04 dataset (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) indicators) performed better than other models which proving its effectiveness in capturing spatiotemporal characteristics and improving prediction accuracy.
Chu-xia Chen· Proceedings of the 3rd Inter...· 0 citations
An improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations.
Jin Zhang, Feng-Min Tan, Wei Bai et al.· Italian National Conference...· 0 citations