ITSC 2025 Paper Abstract

Paper FR-LM-T39.5

Schönböck, Johannes (University of Applied Sciences Upper Austria), Retschitzegger, Werner (Johannes Kepler University Linz (JKU)), Schwinger, Wieland (Johannes Kepler University Linz (JKU)), Pröll, Birgit (Johannes Kepler Univesity Linz (JKU)), Kapsammer, Elisabeth (Johannes Kepler University Linz (JKU)), Zaunmair, Herbert (Johannes Kepler University Linz (JKU)), Graf, David (team Technology Management GmbH), Lechner, Marianne (University of Arts, Department of Visual Communication,)

Alert-Driven Pattern Mining in Large-Scale Road Traffic Management

Scheduled for presentation during the Regular Session "S39a-Data-Driven Optimization in Intelligent Transportation Systems" (FR-LM-T39), Friday, November 21, 2025, 11:50−12:10, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords IoT-based Traffic Sensors and Real-time Data Processing Systems, Real-time Incident Detection and Emergency Management Systems in ITS, AI, Machine Learning and Predictive Analytics for Traffic Incident Detection and Management

Abstract

The immense flood of alerts that is constantly produced in large-scale control systems (LSCS) and particularly in road traffic management (RTM), represents a substantial challenge for efficient and safe operation. Although research for reducing alert floods exists since decades, mining of appropriate alert patterns as the ultimate means to cope with alert quantity is especially challenging since relationships between alerts are commonly unknown, due to heterogeneity, size, and evolutionary nature.

In search of the holy grail for dealing with alert floods, i.e., exploiting relationships between alerts, this paper contributes an alert-driven pattern mining approach, based on a hybrid, multi-objective evolutionary algorithm. This approach is unique in that first, pattern coverage is maximized, ensuring that each alert occurrence is pinned down within a pattern, thus allowing to reason about all underlying relationships for the whole alert log data. Second, pattern frequency is leveraged, ensuring that both frequent as well as rare patterns are found, thus allowing for alert flood reduction in regular as well as exceptional and possibly critical cases. Based on real-world log data in the area of RTM, the applicability of our approach is demonstrated, complemented by a comparative evaluation.