Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1071-1078· 0 citations· 27 references
Abstract
Urban traffic congestion remains a major challenge for metropolitan transportation systems because it increases travel delay, fuel consumption, and environmental pollution. Conventional monitoring systems are mostly reactive and report congestion after traffic breakdown has already occurred. This paper proposes a lightweight Adaptive Anomaly-Residual Early Warning System (AAR-EWS) for proactive congestion detection. The framework integrates a neighbor-aware Temporal Convolutional Network (N-TCN), residual-based anomaly scoring, segment-wise adaptive thresholding, uncertainty-gated alert logic, and propagation-aware neighborhood confirmation. Experiments on the METR-LA benchmark show that the proposed method reduces 15-minute MAE by 6.3% compared with the TCN baseline, provides an average congestion lead time of 13.6 minutes, and reduces false alerts by more than 35% compared with threshold-based warning rules. The revised study further clarifies model configuration, operational assumptions, robustness under noisy and missing data, deployment feasibility, and comparison with graph neural network approaches.
A traffic-prior-guided state-aware framework for robust urban traffic anomaly detection that provides a robust and interpretable solution for intelligent urban traffic monitoring, anomaly warning, and resilient traffic operation management.
This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing and exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy.
R. Elankavi, Imran Alam, Mogadala Mounika et al.· ITM Web of Conferences· 0 citations
The proposed sensor-driven method achieves lane-level accident detection and traffic prediction with high accuracy by fusing historical and real-time data within a three-dimensional Markov model and proves effective in diagnosing other traffic anomalies, such as congestion, and for continuous monitoring of roadway inci...
Meng Zeng, Hang Chen· Italian National Conference...· 0 citations
A theory guided deep learning framework that integrates traffic flow theory with short-term speed prediction and allows operators to act early with measures such as variable speed limits before congestion fully develops, shifting traffic management from reactive monitoring toward proactive control.
Donghyeok Park, Yoon-Young Choi, Juneyoung Park· Journal of Transportation En...· 0 citations
The proposed model's ability to provide accurate short-term predictions while maintaining computational efficiency represents a significant advancement in traffic management strategies for developing urban ecosystems and underscores the efficacy of data-driven decision support systems in optimizing urban mobility and r...
Sreejith Karunakaran, S. Mathi, Preeja Pradeep· Bulletin of Electrical Engin...· 0 citations
A real-time, spatial occupancy–driven framework for adaptive green signal prediction built upon a novel hybrid two-stream detection architecture integrated with a visibility-aware fusion mechanism, offering the research community a benchmark framework for integrating perception-aware learning into traffic management, s...
