This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris, and demonstrates that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification.
Abstract
Efficient traffic-state prediction at urban intersections is a critical component of intelligent transportation systems(ITS), as traffic conditions are influenced by dynamic factors such as traffic demand variability, infrastructure constraints, and operational traffic-control policies. This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris. The objective is to classify traffic conditions into five operational states: Unknown, Flowing, Pre-saturated, Saturated, and Blocked. To investigate the impact of temporal modeling on traffic-state recognition, four deep learning architectures were evaluated under identical experimental conditions: Artificial Neural Networks (ANN), Simple Recurrent Neural Networks (RNN),Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Considering the highly imbalanced nature of the dataset, model performance was assessed using complementary metrics including Accuracy, Precision, Recall, F1-score, Macro-F1 score, and Balanced Accuracy. Experimental results demonstrate that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification. While the conventional RNN achieves high overall accuracy, its performance on minority traffic states remains limited. Among the evaluated models, the LSTM achieves the highest Balanced Accuracy (70.91%), indicating superior recognition of underrepresented traffic conditions. The GRU attains the highest overall F1-score (0.9256) and Macro-F1 score (0.497), while maintaining competitive classification accuracy (91.01%), providing the most favorable trade-off between global predictive performance and balanced class-wise recognition.The analysis of learning curves, classification reports, and confusion matrices further confirms the effectiveness of gated recurrent architectures for handling highly imbalanced multiclass traffic-state classification problems. These findings provide practical insights for the deployment of intelligent traffic-monitoring systems capable of supporting real-time traffic management and decision-making in urban environments.
The Intelligent Traffic Management Systems (ITMS) have become an important feature of the smart city infrastructure because of the fast increase in the city population and the consequent urban traffic congestion, fuel use, and road accidents. Conventional methods of traffic management apply a lot on the operation of fixed-time control mechanisms and rule-based systems, which are not flexible to the dynamic traffic conditions. In the recent past, progress in the field of deep learning has resulted in the creation of data-driven systems of traffic management that can learn intricate spatial and temporal patterns of traffic through major sources of heterogeneous data. The paper provides a detailed research on designing, implementing and testing of an Intelligent Traffic Management System based on deep learning. The suggested system combines convolutional neural networks (CNNs) to estimate the traffic density, recurrent neural networks (RNNs) and long short-term memory (LSTMs) to predict the traffic flow, and reinforcement learning (RLs) to control traffic signals. Various data sources such as live video streams, sensor data and past traffic data are used to improve accuracy of predictions and effectiveness of decisions. The proposed system architecture includes a modular architecture that will include all the layers of data acquisition, preprocessing, model training, and real-time deployment. Numerous experiments on benchmark traffic datasets have shown that congestion is greatly reduced, the average vehicle waiting time is minimized and the traffic throughput is much improved in comparison to traditional systems. The findings confirm that traffic management systems based on deep learning can contribute significantly to the improvement of urban mobility, environmental impact, and road safety. This paper has given relevant information about the application of AI-driven traffic control systems in practice and opened up the prospects of future research in intelligent transportation systems.
Ibrahim A. Lawal· International Journal of Art...· 0 citations
Although the majority of Intelligent Transportation Systems (ITS) and risk-prediction frameworks keep considering pavement quality and dynamic traffic behavior as separate phenomena, road infrastructure degradation and traffic flow instability both contribute to risky driving situations. This study combines segment-level Pavement Condition Index (PCI) data with actual traffic observations from New York City to present an integrated, data-driven methodology for simulating infrastructure-induced unsafe driving circumstances. A supervised machine learning model is developed by combining measures of traffic congestion, speed variation, and pavement deterioration to estimate hourly instability risk, which serves as a proxy for risky driving behavior. Evaluating on a temporally separated test set, the infrastructure-aware model achieves an ROC-AUC of 0.9804 and a PR-AUC of 0.9074, outperforming a traffic-only baseline (ROC-AUC 0.8785, PR-AUC 0.5833). These instability indicators correspond to high-level behavioral patterns commonly observed in ITS monitoring contexts, without relying on raw visual data. Model explainability using SHAP indicates that pavement condition and congestion are the most influential features, with comparable contributions to instability prediction. Predicted risk probabilities are geospatially mapped to identify infrastructure-driven hotspots, and the ORQCIAM framework demonstrates how such risk outputs can inform infrastructure-aware routing and maintenance prioritization. The findings reveal that machine learning enhanced with pavement condition data offers a data-driven approach for predicting hazardous driving situations and supporting infrastructure-aware decision-making, demonstrating how infrastructure-aware risk estimates might help with routing analysis and repair priority in future ITS applications.
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
TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
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