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 at...
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
An improved reservoir computing model, termed I-ICM-RC, in which a Simplified Continuous Coupled Neural Network (SCCNN) is employed as the reservoir module is proposed, in which bio-inspired integrate-and-fire dynamics and structured local coupling are incorporated.
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...
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 spatiotem...
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
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