Jul 2026· The eurasia proceedings of science, technology, engineering & mathematics· Vol 40, pp. 202-210· 0 citations
TL;DR
A novel hybrid deep learning model that combines Bayesian inference with backpropagation and a Transformer architecture, improving prediction accuracy while maintaining the model's generalization ability is proposed.
Abstract
Nonlinear fluctuations and abrupt events in traffic flow can lead to significant point prediction errors, thereby limiting the effectiveness of traffic control strategies in Intelligent Transportation Systems (ITS). To address this issue, we propose a novel hybrid deep learning model, Bayes-BP-Transformer. This model combines Bayesian inference with backpropagation and a Transformer architecture, improving prediction accuracy while maintaining the model's generalization ability. Specifically, the backpropagation component captures local temporal patterns, while the Transformer encoder models long-range spatiotemporal dependencies. By introducing Bayesian inference, the model optimizes the probability distribution of network weights, improving robustness to noise and incomplete data. Experiments on two real-world traffic datasets demonstrate the superior performance of our model. Compared to advanced models, Bayes-BP-Transformer reduces the root mean square error (RMSE) by 18.6-38.0% and the mean absolute percentage error (MAPE) by 5.7-32
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.
V. Poornima, M. Subashini· International Conference on...· 0 citations
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
Chu-xia Chen· Proceedings of the 3rd Inter...· 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
The rapid growth of Internet applications has led to increasingly complex network traffic, posing new challenges for accurate long-term forecasting. To address the limitations of traditional linear models in capturing nonlinear temporal dependencies, this paper proposes an improved TSM-Transformer model that introduces a Trend Attention module for long-term tendencies and a Seasonal Attention module for periodic variations. The model also optimizes encoder–decoder fusion to enhance information interaction. Experiments on the Traffic_One_Cell dataset demonstrate that the proposed model achieves the lowest MAE (1.2 × 10⁻3) and RMSE (2.9 × 10⁻3) with an R2 of 92.1%, outperforming ARIMA, GRU, and SVM baselines. The results confirm the model’s superiority in long-horizon network traffic prediction, providing reliable support for proactive resource allocation and network management.
Yuanxin Gao, Jianping Wu, Lei Liu et al.· 2026 IEEE International Conf...· 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.
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