Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 2189-2193· 0 citations· 13 references
Abstract
Accurate Traffic Flow Forecasting (TFF) is important for emerging Intelligent Transportation Systems (ITS) that support active traffic management, optimize routes, and reduce congestion. In this paper, Deep Learning (DL) methods for TFF, with an emphasis on models like Recurrent Neural Networks (RNN) reinforced with attention mechanism, Bidirectional Long Short-Term Memory (Bi-LSTM), as well as Stacked Autoencoder (SAE) is used for ITS. In complex traffic situations, these strategies improve prediction accuracy and remove nonlinear spatial-temporal networks. Bio-inspired optimization methods, such as the Fruit Fly Optimization Algorithm (FFOA), Philippine Eagle Optimization (PEO) and Kookaburra Optimization Algorithm (KOA) are reviewed for adaptive learning, weight initialization and optimal parameter adjustment in order to further improve model performance. The model architectures, optimization techniques, and assessment criteria discussed in recent research are compared in this review to show how they contribute to precise RMSE, MAPE, MAE traffic forecasts. With a focus on multi-source data fusion, real-time adaptability and interpretable AI frameworks for next-generation ITS, it concludes by identifying research gaps and future creativities.
This systematic review critically examines recent advancements in deep learning-based traffic flow prediction models, emphasizing studies published between 2018 and 2026 and indicates that although graph-based and transformer-based architectures currently achieve state-of-the-art predictive performance, integrating explainability, real-time adaptation, and privacy-preserving learning remains a significant research priority.
Dr. P. K. S. Bhadauria· International Journal of Cre...· 0 citations
A key contribution of this study lies in its comparative synthesis of ML and DL models, revealing that hybrid and graph-based DL architectures consistently outperform traditional ML methods when handling large-scale, heterogeneous traffic datasets.
Thabo Matue, A. A. Akinyelu, Mase Mokotsolane· International Journal of Dat...· 0 citations
Overall, the proposed Improved Dolphin Swarm‐optimized Dynamic Recurrent Neural Network shows promising potential for supporting intelligent traffic management and reducing traffic congestion; however, further validation using larger and more diverse datasets is required to confirm its generalizability and reliability.
Maosheng Yan, Yihan Wang, Qingfeng Dong et al.· Concurrency and Computation· 0 citations
A critical review of the available literature underscores the potential of DL to improve congestion management and provides important pointers for future development in order to make it more applicable to sustainable and intelligent transportation systems.
Al Ani Mohammed Nsaif Mustafa, Mohd Murtadha Bin Mohamad, F. Muchtar· Acta Universitatis Sapientia...· 0 citations
Accurate and reliable traffic flow prediction constitutes a critical component of intelligent transportation systems (ITS), enabling improved traffic management and environmental sustainability. Ensemble methodologies, which combine the strengths of multiple predictive models, have emerged as powerful approaches in complex forecasting tasks. This paper introduces an advanced SHAP-driven boosting framework that incorporates SHapley Additive exPlanations (SHAP) analysis as an iterative guide within the CatBoost and XGBoost models development process and the grid search optimizer to improve the accuracy and interpretability of traffic flow prediction. The SHAP analysis is introduced to identify the most influential lag features, providing insights into the key patterns driving traffic dynamics. Meanwhile, grid search is incorporated to fine-tune the paradigm hyperparameters, ensuring optimal performance. The proposed framework is systematically evaluated using multi-source datasets from Utah highways, and benchmarked against traditional models (decision tree, Random Forest, SVR, Linear/Lasso Regression) as well as deep learning models (CNN, LSTM, GRU, BiLSTM, CNN-LSTM). Experimental results confirm that the SHAP-driven boosting framework consistently outperforms all comparative models, achieving the highest
R
2
scores and the lowest error metrics (
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M
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E
,
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A
E
, and
M
A
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) across various data sources and sampling intervals. These findings were confirmed by Diebold–Mariano and Wilcoxon signed-rank tests (
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0.05
). The framework consistently yields sharper, well-calibrated prediction intervals, establishing it as a highly accurate, statistically robust, and interpretable solution for traffic flow forecasting.
Amina Bouhali, Abdelhafid Zeroual, F. Harrou· Intelligent Data Analysis· 0 citations
Accurate urban traffic flow prediction is essential for intelligent transportation systems. Traditional time series models and conventional recurrent neural networks (RNNs) often struggle to capture complex nonlinear and long-term temporal dependencies while maintaining computational efficiency. To address this issue, this paper proposes an improved reservoir computing model, termed I-ICM-RC, in which a Simplified Continuous Coupled Neural Network (SCCNN) is employed as the reservoir module. By incorporating bio-inspired integrate-and-fire dynamics and structured local coupling, the proposed model enhances the representation of spatiotemporal patterns in traffic flow. Experiments conducted on real-world traffic datasets evaluate the proposed method under multi-step forecasting scenarios. The results show that the proposed model generally achieves better performance than the standard Echo State Network (ESN), particularly in short-and medium-term prediction tasks, while maintaining low computational cost. These findings indicate that the proposed approach provides an efficient and robust alternative for traffic flow prediction.
Xinyu Shi, Jiatai Cheng, Tailai Bai et al.· International Conference on...· 0 citations