{"id":"https://openalex.org/W4414990964","doi":"https://doi.org/10.1145/3748636.3762740","title":"Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]","display_name":"Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4414990964","doi":"https://doi.org/10.1145/3748636.3762740"},"language":"en","primary_location":{"id":"doi:10.1145/3748636.3762740","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762740","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762740","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762740","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101859711","display_name":"Yueyang Liu","orcid":"https://orcid.org/0000-0001-5894-8740"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yueyang Liu","raw_affiliation_strings":["Emory University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0000-0001-5894-8740","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019675145","display_name":"Levi Kennedy","orcid":"https://orcid.org/0009-0004-6815-2219"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lance Kennedy","raw_affiliation_strings":["Emory University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0009-0004-6815-2219","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066260636","display_name":"Ruochen Kong","orcid":"https://orcid.org/0009-0006-0329-8019"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruochen Kong","raw_affiliation_strings":["Emory University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0009-0006-0329-8019","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Joon-Seok Kim","orcid":"https://orcid.org/0000-0001-9963-6698"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joon-Seok Kim","raw_affiliation_strings":["Emory University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0000-0001-9963-6698","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017299501","display_name":"Andreas Z\u00fcfle","orcid":"https://orcid.org/0000-0001-7001-4123"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Z\u00fcfle","raw_affiliation_strings":["Emory University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0000-0001-7001-4123","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150468666"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30858209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"324","last_page":"327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9980999827384949,"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/T11106","display_name":"Data Management and Algorithms","score":0.9674999713897705,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10757","display_name":"Geographic Information Systems Studies","score":0.9567999839782715,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5347999930381775},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4828000068664551},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.46239998936653137},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.44200000166893005},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.42660000920295715},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3950999975204468},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.34860000014305115}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7505000233650208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368999719619751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427000164985657},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5347999930381775},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.46239998936653137},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39910000562667847},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C108771440","wikidata":"https://www.wikidata.org/wiki/Q368475","display_name":"Lifelong learning","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C151915977","wikidata":"https://www.wikidata.org/wiki/Q5937740","display_name":"Human dynamics","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C49898467","wikidata":"https://www.wikidata.org/wiki/Q1517706","display_name":"Stratified sampling","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3748636.3762740","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762740","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762740","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.13135","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.13135","pdf_url":"https://arxiv.org/pdf/2508.13135","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3748636.3762740","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762740","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762740","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5379231435","display_name":null,"funder_award_id":"140D0423C0025","funder_id":"https://openalex.org/F4320333051","funder_display_name":"Intelligence Advanced Research Projects Activity"}],"funders":[{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320333452","display_name":"Interior Business Center","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414990964.pdf","grobid_xml":"https://content.openalex.org/works/W4414990964.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Individual-level":[0],"human":[1,22,79,180],"mobility":[2,23,80,181],"prediction":[3],"has":[4],"emerged":[5],"as":[6],"a":[7,31,56,137],"significant":[8],"topic":[9],"of":[10,60,73,105,112,128],"research.":[11],"In":[12,50],"this":[13,51],"paper,":[14,53],"we":[15,54,123,150],"focus":[16],"on":[17],"an":[18,40,70],"underexplored":[19],"problem":[20],"in":[21,78,136,140],"prediction:":[24],"determining":[25],"the":[26,45,74,103,106,110,126,161],"best":[27],"practices":[28],"to":[29,38,120,158],"train":[30],"machine":[32],"learning":[33],"model":[34,176],"using":[35],"historical":[36],"data":[37,132,145,183],"forecast":[39],"individuals":[41],"complete":[42],"trajectory":[43],"over":[44],"next":[46],"days":[47],"and":[48,65,90,93,134,147],"weeks.":[49],"experiment":[52],"undertake":[55],"comprehensive":[57],"experimental":[58],"analysis":[59],"diverse":[61],"models,":[62],"parameter":[63],"configurations,":[64],"training":[66,182],"strategies,":[67],"accompanied":[68],"by":[69],"in-depth":[71],"examination":[72],"statistical":[75],"distribution":[76],"inherent":[77],"patterns.":[81],"Our":[82,166],"empirical":[83],"evaluations":[84],"encompass":[85],"both":[86],"Long":[87],"Short-Term":[88],"Memory":[89],"Transformer-based":[91],"architectures,":[92],"further":[94,168],"investigate":[95],"how":[96],"incorporating":[97],"individual":[98],"life":[99],"patterns":[100],"can":[101],"enhance":[102],"effectiveness":[104],"prediction.":[107],"Moreover,":[108],"since":[109],"absence":[111],"explicit":[113],"user":[114,121,152],"information":[115],"is":[116,184],"often":[117],"missing":[118],"due":[119],"privacy,":[122],"show":[124,169],"that":[125,160,170],"sampling":[127,157],"users":[129],"may":[130],"exacerbate":[131],"skewness":[133],"result":[135],"substantial":[138],"loss":[139],"predictive":[141],"accuracy.":[142],"To":[143],"mitigate":[144],"imbalance":[146],"preserve":[148],"diversity,":[149],"apply":[151],"semantic":[153],"clustering":[154],"with":[155],"stratified":[156],"ensure":[159],"sampled":[162],"dataset":[163],"remains":[164],"representative.":[165],"results":[167],"small-batch":[171],"stochastic":[172],"gradient":[173],"optimization":[174],"improves":[175],"performance,":[177],"especially":[178],"when":[179],"limited.":[185]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}