Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, Sajal Das
ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) 2022
Nominated for Best Paper Award
Real-time incident detection in large transportation networks is challenging due to complex spatiotemporal dependencies and high data volumes. This work is significant because it proposes a theoretically grounded approach that guarantees invariance properties necessary for robust anomaly detection. The region growing algorithm addresses scalability challenges while maintaining accuracy in detecting true incidents.
This paper presents a comprehensive tool-chain for anomaly detection in large-scale smart transportation systems using region growing approximation algorithms. The approach combines data-driven learning with spatial structure exploitation to identify traffic incidents across interconnected road segments while maintaining computational tractability. The framework uses harmonic mean and arithmetic mean metrics to detect deviations in transportation patterns.
The experimental evaluation using real traffic data from Nashville, Tennessee demonstrated that the proposed framework successfully detects incidents in real-time with high accuracy. The method's invariance under benign conditions ensures low false alarm rates while remaining sensitive to true incidents. The region growing approximation achieved computationally tractable solutions for large-scale networks without sacrificing detection performance.
@inproceedings{jp2022,
author = {Islam, Jaminur and Talusan, Jose Paolo and Bhattacharjee, Shameek and Tiausas, Francis and Vazirizade, Sayyed Mohsen and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal},
booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
title = {Anomaly based Incident Detection in Large Scale Smart Transportation Systems},
year = {2022},
month = {apr},
publisher = {IEEE},
note = {Nominated for Best Paper Award},
acceptance = {30},
abstract = {Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient transportation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time 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. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.},
contribution = {lead},
keywords = {anomaly detection, smart transportation, incident detection, graph algorithms, traffic monitoring},
month_numeric = {4}
}