{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:20:31Z","timestamp":1760242831591,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,8,23]],"date-time":"2016-08-23T00:00:00Z","timestamp":1471910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Science Research Program through the National 847 Research Foundation of Korea(NRF) funded by the Ministry of Education","award":["NRF-2014R1A1A2055639"],"award-info":[{"award-number":["NRF-2014R1A1A2055639"]}]},{"name":"Institute for Information &amp; communications Technology Promotion(IITP) grant funded by the Korea 849 government(MSIP)","award":["R0101-16-0129"],"award-info":[{"award-number":["R0101-16-0129"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system\u2014a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset.<\/jats:p>","DOI":"10.3390\/s16091340","type":"journal-article","created":{"date-parts":[[2016,8,23]],"date-time":"2016-08-23T10:18:55Z","timestamp":1471947535000},"page":"1340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Querying and Extracting Timeline Information from Road Traffic Sensor Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Ardi","family":"Imawan","sequence":"first","affiliation":[{"name":"Department of Big Data, Pusan National University, Busan 46241, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2092-1056","authenticated-orcid":false,"given":"Fitri","family":"Indikawati","sequence":"additional","affiliation":[{"name":"Department of Big Data, Pusan National University, Busan 46241, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8207-9415","authenticated-orcid":false,"given":"Joonho","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Big Data, Pusan National University, Busan 46241, Korea"}]},{"given":"Praveen","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,23]]},"reference":[{"key":"ref_1","unstructured":"Texas A & M Transportation Institute Performance Measure Summary \u2014All 471 Area Averages. 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