Intelligent Urban Traffic Congestion Prediction Through Accident-Aware and Time-Dependent Traffic Analytics

Rapid urban population growth has intensified traffic congestion in smart cities. This has resulted in longer travel times, higher fuel consumption, increased environmental pollution, greater operational costs, and slower emergency response services. Existing traffic congestion prediction models primarily rely on traffic-flow and temporal features; the effects of road accidents and peak-hour conditions are not adequately addressed. This limitation is particularly significant in smart cities where both recurrent congestion (peak-hour demand) and non-recurrent congestion (road accidents) influence traffic conditions they have a significant impact on the performance of the road network. This study introduces a novel Historical Accident-Aware Peak-Hour GAN-GRU (APG-GRU) framework. The proposed framework employs a data processing pipeline integrating traffic data with historical accident-related features to predict traffic congestion using these features. Extensive experiments are conducted on a novel integrated dataset consist on Automatic Number Plate Recognition (ANPR) traffic data and ANPR traffic observations with historical accident features. The results demonstrate that the APG-GRU framework achieved superior performance on the integrated features dataset, attaining an accuracy of 97.50%, a congested precision of 91.86%, a congested recall of 97.31%, and a congested F1-score of 94.51%, outperforming both the ANPR traffic-only dataset and all baseline models. The APG-GRU framework significantly outperforms a suite of benchmark models, including XGBoost, Long Short-Term Memory (LSTM), and Random Forest as baselines, which achieved accuracies between 84% and 95.5% with correspondingly lower precision, recall, and F1-scores. External validation using a traffic dataset collected from Lahore, Pakistan, further demonstrated the robustness and generalizability of the proposed APG-GRU framework. A web-based interface developed for the APG-GRU framework to visualize accident hotspots and route-level traffic conditions. Routes with smooth traffic flow are highlighted in green, whereas congested routes are highlighted in red, demonstrating the practical applicability of the proposed framework for smart city traffic management systems.