On the applicability of time series anomaly detection methods to real-world traffic volume data
Iman Taheri Sarteshnizi, Majid Sarvi, Saeed Asadi Bagloee, Neema Nassir · Transportation Research Part C Emerging Technologies · 2026
Time Series Anomaly Detection (TSAD or TAD) refers to the automatic and data-driven identification of abnormal segments in time series data, a task that has been studied extensively for decades. Despite recent transformative and novel findings revealed by efforts in this field, the literature on traffic anomaly detection has not yet fully reflected on these emerging trends to draw practical conclusions. In this paper, we focus on the applicability of state-of-the-art and well-established TAD methods to road traffic volume data, making contributions in two main ways. First, given the proven and major contribution of evaluation data to TAD outcomes, we argue that existing anomaly-labeled datasets from transportation and traffic systems require substantial enhancements in terms of both data size and label quality. To address this, we propose a new platform to inspect and label large-scale volume data of urban areas based on its unique characteristics and the latest taxonomy of time series anomalies. Second, based on the established framework, we also formulate the TAD problem in traffic volume data and introduce a discord-based, context-embedded, and light-weight traffic anomaly detection method, named Step-isolated Traffic Discords Discovery (Si-TDD), to address this problem. Benefiting from our labeling platform, AnoLT (Anomaly Labeled Traffic) is presented in this paper for the first time as a comprehensive, open-source, and anomaly-labeled spatiotemporal dataset collected from 147 locations across Melbourne, Australia. Comparative results with more than 20 baselines also indicate that Si-TDD considerably outperforms recent TAD solutions when it comes to traffic volume data, achieving a 67% F1 score with the AnoLT dataset. This paper highlights the key role of incorporating context-related information into existing TAD solutions to boost their effectiveness in traffic anomaly detection, a factor that is often overlooked in the current literature.