Abstract
Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. to materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. in this article, we first propose a scalable data-driven anomaly-Based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. the highly correlated clusters enable identifying a Pythagorean Mean-Based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee and compare with the state-of-the-art ML methods to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.
Recommended Citation
M. J. Islam et al., "Scalable Pythagorean Mean-Based Incident Detection in Smart Transportation Systems," ACM Transactions on Cyber-Physical Systems, vol. 8, no. 2, article no. 20, Association for Computing Machinery (ACM), May 2024.
The definitive version is available at https://doi.org/10.1145/3603381
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
anomaly detection; approximation algorithm; cluster analysis; graph algorithms; incident detection; regression; smart transportation; Weakly unsupervised learning
International Standard Serial Number (ISSN)
2378-9638; 2378-962X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
14 May 2024
Comments
National Science Foundation, Grant CNS-1818901