TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents

Ammar Zulqarnain, Jacob Buckelew, Jose Paolo Talusan, Ayan Mukhopadhyay, Abhishek Dubey

2025 IEEE International Conference on Smart Computing (SMARTCOMP) 2025

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Why This Matters

Traditional traffic anomaly detection methods struggle with the complexity of capturing spatial-temporal dependencies in interconnected road networks while maintaining scalability and interpretability. TRACE is innovative because it unifies multiple deep learning paradigms (graph neural networks, transformers, normalizing flows) within a probabilistic framework, enabling unsupervised anomaly detection without requiring labeled anomaly data. The density-based approach provides interpretable anomaly scores grounded in learned probability models.

What We Did

TRACE is a novel framework for real-time traffic anomaly detection and localization that combines Graph Neural Networks, Transformers, and normalizing flows. The system learns the spatial-temporal dependencies in road networks through graph convolutions while capturing long-range temporal interactions through transformer attention. To detect anomalies, TRACE computes log-likelihoods under a learned probability distribution, identifying points where traffic patterns deviate significantly from normal conditions. The framework provides both anomaly detection and localization through density-based analysis.

Key Results

Evaluation on real-world traffic data from a mid-sized US metropolitan area demonstrates that TRACE significantly improves incident localization precision by 17% compared to methods that identify anomalies without spatial localization. The framework achieves superior detection latency and mean localization error compared to state-of-the-art baselines.

Full Abstract

Cite This Paper

@inproceedings{zulqarnain2025,
  author = {Zulqarnain, Ammar and Buckelew, Jacob and Talusan, Jose Paolo and Mukhopadhyay, Ayan and Dubey, Abhishek},
  booktitle = {2025 IEEE International Conference on Smart Computing (SMARTCOMP)},
  title = {TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents},
  year = {2025},
  month = {jun},
  abstract = {Effective traffic incident management is critical for road safety and operational efficiency. Yet, many transportation agencies rely on reactionary methods, where incidents are reported by human agents and managed through rule- based frameworks like traditional Traffic Incident Management (TIM) systems. However, these are vulnerable to human error, oversight, and delays during high-stress conditions. Although recent initiatives incorporating real-time sensor data for cor- ridor monitoring and enhanced roadway information systems represent strides toward modernization, these systems often still require substantial human intervention. Recent advancements in graph-based deep learning models offer promising potential for addressing the limitations of traditional methods. While state- of-the-art models exist, the complexities of incident localization within dynamic and interconnected road networks, along with limited availability of high-quality labeled data and variability in real-time traffic measurements, are still open challenges. To address these, we propose the Traffic Response Anomaly Capture Engine (TRACE), a novel approach that combines graph neural networks, transformers, and probabilistic normalizing flows to accurately detect and localize traffic anomalies in real time. TRACE captures spatial-temporal dependencies, manages data uncertainty, and enhances automation, supporting more precise and timely incident localization. Our approach is validated on real-world traffic data and improved incident localization by 0.6 miles (17%) than SOTA methods while maintaining similar incident detection accuracy and mean detection delay.},
  contribution = {lead},
  keywords = {traffic anomaly detection, graph neural networks, transformers, probabilistic modeling, spatial-temporal analysis, smart transportation, anomaly localization},
  month_numeric = {6}
}
Quick Info
Year 2025
Keywords
traffic anomaly detection graph neural networks transformers probabilistic modeling spatial-temporal analysis smart transportation anomaly localization
Research Areas
CPS ML for CPS transit
Search Tags

TRACE, Traffic, Response, Anomaly, Capture, Engine, Localization, Incidents, traffic anomaly detection, graph neural networks, transformers, probabilistic modeling, spatial-temporal analysis, smart transportation, anomaly localization, CPS, ML for CPS, transit, 2025, Zulqarnain, Buckelew, Talusan, Mukhopadhyay, Dubey