{"id":"https://openalex.org/W4391793298","doi":"https://doi.org/10.1109/itsc57777.2023.10422584","title":"DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision","display_name":"DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391793298","doi":"https://doi.org/10.1109/itsc57777.2023.10422584"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422584","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itsc57777.2023.10422584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037164812","display_name":"Christian L\u00f6wens","orcid":null},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Christian L\u00f6wens","raw_affiliation_strings":["University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141"],"affiliations":[{"raw_affiliation_string":"University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002395374","display_name":"Daniela Thyssens","orcid":null},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniela Thyssens","raw_affiliation_strings":["University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141"],"affiliations":[{"raw_affiliation_string":"University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030754446","display_name":"Emma Andersson","orcid":"https://orcid.org/0000-0002-8608-625X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emma Andersson","raw_affiliation_strings":["Devoteam Creative Tech,Malmö,Sweden,211 20"],"affiliations":[{"raw_affiliation_string":"Devoteam Creative Tech,Malmö,Sweden,211 20","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110990387","display_name":"Christina Jenkins","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christina Jenkins","raw_affiliation_strings":["Devoteam Creative Tech,Malmö,Sweden,211 20"],"affiliations":[{"raw_affiliation_string":"Devoteam Creative Tech,Malmö,Sweden,211 20","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039470755","display_name":"Lars Schmidt-Thieme","orcid":"https://orcid.org/0000-0001-5729-6023"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmidt-Thieme","raw_affiliation_strings":["University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141"],"affiliations":[{"raw_affiliation_string":"University of Hildesheim,Information Systems and Machine Learning Lab (ISMLL),Hildesheim,Germany,31141","institution_ids":["https://openalex.org/I155765044"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037164812"],"corresponding_institution_ids":["https://openalex.org/I155765044"],"apc_list":null,"apc_paid":null,"fwci":0.1574,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51257426,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.585324764251709},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5740683078765869},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32551491260528564}],"concepts":[{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.585324764251709},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5740683078765869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32551491260528564},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422584","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itsc57777.2023.10422584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3968613329","display_name":null,"funder_award_id":"ZN3492","funder_id":"https://openalex.org/F4320313139","funder_display_name":"Nieders\u00e4chsische Ministerium f\u00fcr Wissenschaft und Kultur"}],"funders":[{"id":"https://openalex.org/F4320313139","display_name":"Nieders\u00e4chsische Ministerium f\u00fcr Wissenschaft und Kultur","ror":"https://ror.org/0116z8r77"},{"id":"https://openalex.org/F4320320882","display_name":"Volkswagen Foundation","ror":"https://ror.org/03bsmfz84"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1563556628","https://openalex.org/W1626398438","https://openalex.org/W1673310716","https://openalex.org/W1975684011","https://openalex.org/W2009155608","https://openalex.org/W2022749020","https://openalex.org/W2038868990","https://openalex.org/W2067193733","https://openalex.org/W2071049788","https://openalex.org/W2090702599","https://openalex.org/W2108577321","https://openalex.org/W2136317921","https://openalex.org/W2140251882","https://openalex.org/W2143394441","https://openalex.org/W2160642098","https://openalex.org/W2167686542","https://openalex.org/W2199718034","https://openalex.org/W2404161646","https://openalex.org/W2529538380","https://openalex.org/W2612690371","https://openalex.org/W2779280457","https://openalex.org/W2910992039","https://openalex.org/W2911968972","https://openalex.org/W2954540134","https://openalex.org/W2963373106","https://openalex.org/W3023534255","https://openalex.org/W3084105304","https://openalex.org/W3122724825","https://openalex.org/W3200083277","https://openalex.org/W4283370926","https://openalex.org/W4289743952","https://openalex.org/W4293661291","https://openalex.org/W4294601533","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6636500457","https://openalex.org/W6637131181","https://openalex.org/W6713197267","https://openalex.org/W6732971103","https://openalex.org/W6743971617","https://openalex.org/W6753114026","https://openalex.org/W6765696844"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Nowadays,":[0],"mobile":[1],"devices":[2],"enable":[3],"constant":[4],"tracking":[5],"of":[6,20,47,77,117,158],"the":[7,115,122,130,156,166,175],"user's":[8],"position":[9],"and":[10,86,98,129,169],"location":[11,108],"trajectories":[12,109,164],"can":[13],"be":[14],"used":[15],"to":[16,31,35,56,110],"infer":[17],"personal":[18],"points":[19],"interest":[21],"(POIs)":[22],"like":[23],"homes,":[24],"workplaces,":[25],"or":[26,64,84],"stores.":[27],"A":[28],"common":[29],"way":[30],"extract":[32],"POIs":[33],"is":[34,105,121,134,179],"first":[36,123,131],"identify":[37],"spatio-temporal":[38],"regions":[39,52],"where":[40],"a":[41,44,66,96,137,152],"user":[42],"spends":[43],"significant":[45],"amount":[46],"time,":[48],"known":[49],"as":[50,70],"stay":[51,112],"(SRs).":[53],"Common":[54],"approaches":[55],"SR":[57,142],"extraction":[58,143],"are":[59,74],"evaluated":[60,135],"either":[61],"solely":[62],"unsupervised":[63],"on":[65,81,107,126,136,155],"small-scale":[67],"private":[68],"dataset,":[69],"popular":[71],"public":[72],"datasets":[73],"unlabeled.":[75],"Most":[76],"these":[78],"methods":[79],"rely":[80],"hand-crafted":[82],"features":[83],"thresholds":[85],"do":[87],"not":[88],"learn":[89],"beyond":[90],"hyperparameter":[91],"optimization.":[92],"Therefore,":[93],"we":[94,150],"propose":[95],"weakly":[97],"self-supervised":[99],"transformer-based":[100],"model":[101],"called":[102],"DeepStay,":[103],"which":[104],"trained":[106],"predict":[111],"regions.":[113],"To":[114],"best":[116],"our":[118],"knowledge,":[119],"this":[120],"approach":[124,132],"based":[125],"deep":[127],"learning":[128],"that":[133],"public,":[138],"labeled":[139],"dataset.":[140],"Our":[141,177],"method":[144],"outperforms":[145],"state-of-the-art":[146],"methods.":[147],"In":[148],"addition,":[149],"conducted":[151],"limited":[153],"experiment":[154],"task":[157],"transportation":[159],"mode":[160],"detection":[161],"from":[162],"GPS":[163],"using":[165],"same":[167],"architecture":[168],"achieved":[170],"significantly":[171],"higher":[172],"scores":[173],"than":[174],"state-of-the-art.":[176],"code":[178],"available":[180],"at":[181],"https://github.com/christianll9/deepstay.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}