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Title	An Automl-Based Approach For Automatic Traffic Incident Detection In Smart Cities
ID_Doc	7712
Authors	Gkioka G.; Dominguez M.; Mentzas G.
Year	2024
Published	Intelligent Decision Technologies, 18, 2
DOI	http://dx.doi.org/10.3233/IDT-240231
Abstract	In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains. © 2024 – The authors.
Author Keywords	artificial intelligence (AI); automated machine learning (AutoML); big data; Incident detection; intelligent transport systems (ITS)

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