AUTHOR=Ali Akbar , Nadeem Adnan , Zafar Noureen , Shiraz Muhammad 

TITLE=F-GGRU: a sensor-driven deep learning framework for smart city weather-aware traffic congestion prediction

JOURNAL=Frontiers in Communications and Networks

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2025.1666487

DOI=10.3389/frcmn.2025.1666487

ISSN=2673-530X

ABSTRACT=The deployment of various sensors including inductive loops, radars, GPS devices, cameras and floating car data (FCD) in intelligent transportation systems generates a stream of heterogeneous data, further complicated by exogenous factors like weather conditions and temporal patterns (e.g., peak hours, weekends). For urban traffic development planning, the accurate prediction of congestion under the influence of these exogenous factors remains a major challenge. The proliferation of these diverse data sources creates a complex prediction environment, demanding advanced analytical frameworks. To address this issue, we propose a novel Fusion-based Generative Adversarial Network with Gated Recurrent Unit (F-GGRU) framework. The F-GGRU develops a generic data pipeline for integrating and preprocessing multi-source data, featuring advanced techniques for outlier removal, fuzzy logic-based automatic labeling, and Generative Adversarial Networks (GANs) for class balancing. Extensive experimentation was conducted on a novel real-time dataset from the Safe City Islamabad Pakistan (SCIP) project, integrating heterogeneous and exogenous features. The results demonstrate that our proposed F-GGRU framework achieves superior performance, with 98% accuracy, 0.99 precision, 0.98 recall, and a 0.98 F1-score. This significantly outperforms a suite of benchmark models, including Logistic Regression, Random Forest, XGBoost, and deep learning baselines like ANN, which achieved accuracies between 77% and 83% with correspondingly lower precision, recall, and F1-scores. Significantly, hyperparameter tuning and validation on a second independent dataset (CityPulse, Aarhus) confirmed the proposed framework robustness and generalizability, achieving even higher performance 99.42% accuracy and 0.99 AUC. These findings affirm that the F-GGRU framework is a robust and generalizable solution for real world traffic congestion prediction in smart cities.