Aug 2026· Concurrency and Computation· 0 citations· 19 references
TL;DR
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.
Abstract
Traffic congestion in urban areas has become a major challenge due to rapid urbanization and the increasing number of vehicles, resulting in travel delays, environmental pollution, and reduced transportation efficiency. Existing traffic forecasting methods often struggle to capture complex temporal patterns and achieve consistent prediction performance in dynamic Internet of Vehicles (IoV) environments. This study proposes an Improved Dolphin Swarm‐optimized Dynamic Recurrent Neural Network (IDS‐DRNN), which integrates metaheuristic optimization with deep temporal learning for traffic flow prediction. The model is developed using a publicly available Kaggle Traffic Flow Monitoring Dataset comprising 4000 traffic records and 15 traffic‐related features, including vehicle density, speed, weather conditions, and network parameters. Min‐Max normalization is employed for data preprocessing, while principal component analysis (PCA) is used to reduce feature dimensionality and improve computational efficiency. The proposed model is implemented and evaluated using the Python environment. Experimental results indicate that IDS‐DRNN achieves an accuracy of 97.5%, precision of 96.5%, recall of 97.1%, RMSE of 1.01, and MAE of 0.48, demonstrating improved prediction performance compared with the baseline methods considered in this study. Although the Improved Dolphin Swarm optimization stage introduces additional computational complexity during model training, the trained model supports efficient inference for traffic flow prediction in IoV environments. Overall, the proposed IDS‐DRNN 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.
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 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
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
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
With the acceleration of urbanization, traffic congestion at multiple intersections has become a core bottleneck restricting urban operational efficiency. To address this, this paper proposes a traffic signal dynamic optimization algorithm, the Cross-Attention Mechanism and Dueling Double DQN (CAM-D3QN). This method utilizes a novel crisscross attention module to dynamically model spatial dependencies between intersections and incorporates the Dueling Double DQN architecture for robust Q-value estimation. Validated on CityFlow using Grid-4×4 and Hangzhou-real networks, CAM-D3QN significantly outperforms the state-of-the-art baseline, GPLight, achieving relative improvements of approximately 10.5% in average vehicle delay, 11.3% in average queue length, 3.3% in throughput, alongside notable reductions in stops (12.1%) and fuel consumption (7.2%). Ablation experiments further demonstrate that removing the cross-attention module increases queue length by 30.6% in sudden congestion scenarios. The proposed method achieves superior performance to the baseline across four typical traffic scenarios on the Hangzhou-real road network, demonstrating its generalization capabilities. By leveraging the coordinated optimization of dynamic spatial perception and robust value assessment, this paper provides an effective solution for efficient and robust coordinated traffic signal control. The framework can be combined with traffic states acquired from radar, roadside sensors or wireless communication units in intelligent transportation systems.
L. Chang, D. Wei· Advanced Electromagnetics· 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
