{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:40:34Z","timestamp":1775612434109,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.<\/jats:p>","DOI":"10.3390\/s22082933","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T22:48:45Z","timestamp":1649803725000},"page":"2933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-8301","authenticated-orcid":false,"given":"Ayham","family":"Zaitouny","sequence":"first","affiliation":[{"name":"ARC Training Centre for Transforming Maintenance Through Data Science, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"},{"name":"Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0353-1398","authenticated-orcid":false,"given":"Athanasios D.","family":"Fragkou","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece"}]},{"given":"Thomas","family":"Stemler","sequence":"additional","affiliation":[{"name":"Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"}]},{"given":"David M.","family":"Walker","sequence":"additional","affiliation":[{"name":"Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"}]},{"given":"Yuchao","family":"Sun","sequence":"additional","affiliation":[{"name":"Planning and Transport Research Centre (PATREC), University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9580-0702","authenticated-orcid":false,"given":"Theodoros","family":"Karakasidis","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece"},{"name":"Department of Physics, University of Thessaly, 35100 Lamia, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9083-9584","authenticated-orcid":false,"given":"Eftihia","family":"Nathanail","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5378-1582","authenticated-orcid":false,"given":"Michael","family":"Small","sequence":"additional","affiliation":[{"name":"ARC Training Centre for Transforming Maintenance Through Data Science, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"},{"name":"Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"ref_1","unstructured":"European Commission (2011). 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