This paper aims to provide references for subsequent academic exploration and industrial application, helping to enhance the efficiency of urban traffic management, alleviate congestion, ensure safety and promote sustainable development.
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 survey paper focuses on recent advancements in traffic flow prediction models, examining their merits and drawbacks, and identifies research gaps and challenges and outlines future research directions, aiming to contribute to the ongoing discourse on traffic flow prediction and inform future endeavors in this doma...
N. Chauhan, Neetesh Kumar· ACM Transactions on Intellig...· 0 citations
The rapid growth in traffic volumes has increased the demand for traffic-flow forecasting models with stronger prediction capability. Traditional methods that rely on local feature extraction and static spatial graph construction can no longer fully meet these requirements. To address the short- and long-term fluctuati...
Hai Yan, Rui Jian, Cheng-Cheng Wang et al.· Algorithms· 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.
Mao-Sheng Yan, Yi-Han Wang, Qingfeng Dong et al.· Concurrency and Computation· 0 citations
With the evolution of modern intelligent networks, accurate traffic matrix (TM) prediction has become critical for proactive network management, but remains challenging due to high-dimensional and non-stationary spatio-temporal dependencies. We propose TraDiff, an image-based conditional diffusion framework for TM pred...
Yu Sun, Ya-Qiong Liu, Nan Cheng et al.· IEEE International Symposium...· 0 citations
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 is introduced.
Amina Bouhali, Abdelhafid Zeroual, F. Harrou· Intelligent Data Analysis· 0 citations
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