乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:54:31Z","timestamp":1740174871995,"version":"3.37.3"},"reference-count":60,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016335","name":"Jaguar Land Rover","doi-asserted-by":"publisher","award":["EP\/N012380\/1"],"id":[{"id":"10.13039\/100016335","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK-EPSRC","award":["EP\/N012380\/1"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"Knowledge of drivers\u2019 mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.<\/jats:p>","DOI":"10.1155\/2021\/9973681","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T18:20:30Z","timestamp":1630693230000},"page":"1-20","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Vehicle Activity to Improve Point of Interest Extraction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1446-4418","authenticated-orcid":true,"given":"James","family":"Van Hinsbergh","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Warwick, Coventry, UK"}]},{"given":"Nathan","family":"Griffiths","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Warwick, Coventry, UK"}]},{"given":"Phillip","family":"Taylor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Warwick, Coventry, UK"}]},{"given":"Zhou","family":"Xu","sequence":"additional","affiliation":[{"name":"Jaguar Land Rover, Engineering Centre, Coventry, UK"}]},{"given":"Alex","family":"Mouzakitis","sequence":"additional","affiliation":[{"name":"Jaguar Land Rover, Engineering Centre, Coventry, UK"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30911-4_21"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.12928\/telkomnika.v18i1.13006"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1145\/2666310.2666417"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.09.184"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107195"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1145\/3281548.3281549"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1145\/1363686.1363886"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/rtss.2010.33"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2015.10.005"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/icdcsw.2011.20"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920968"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/mc.2007.141"},{"first-page":"127","article-title":"Learning to predict driver route and destination intent","author":"R. 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