{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:23:08Z","timestamp":1770330188207,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.<\/jats:p>","DOI":"10.3390\/computers13020049","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T03:36:17Z","timestamp":1707363377000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9348-8786","authenticated-orcid":false,"given":"Vasileios","family":"Sevetlidis","sequence":"first","affiliation":[{"name":"Department of Production and Management Engineering, Democritus University of Thrace, Vas. Sofias 12, GR-67100 Xanthi, Greece"},{"name":"Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9909-1584","authenticated-orcid":false,"given":"George","family":"Pavlidis","sequence":"additional","affiliation":[{"name":"Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7799-8273","authenticated-orcid":false,"given":"Spyridon G.","family":"Mouroutsos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, University Campus at Kimmeria, GR-67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-0332","authenticated-orcid":false,"given":"Antonios","family":"Gasteratos","sequence":"additional","affiliation":[{"name":"Department of Production and Management Engineering, Democritus University of Thrace, Vas. Sofias 12, GR-67100 Xanthi, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.aap.2017.11.021","article-title":"Identifying traffic accident black spots with Poisson\u2013Tweedie models","volume":"111","author":"Debrabant","year":"2018","journal-title":"Accid. Anal. Prev."},{"key":"ref_2","unstructured":"Elvik, R. (2007). State-of-the-Art Approaches to Road Accident Black Spot Management and Safety Analysis of Road Networks, Transport\u00f8konomisk Institutt."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tiwari, M., Nagar, P., Arya, G., and Chauhan, S.S. (2021, January 24\u201325). Road Accident Analysis Using ML Classification Algorithms and Plotting Black Spot Areas on Map. 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