{"id":"https://openalex.org/W4405424345","doi":"https://doi.org/10.1145/3681771.3699917","title":"Personalized and On-device Trajectory Mobility Prediction","display_name":"Personalized and On-device Trajectory Mobility Prediction","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4405424345","doi":"https://doi.org/10.1145/3681771.3699917"},"language":"en","primary_location":{"id":"doi:10.1145/3681771.3699917","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681771.3699917","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3681771.3699917","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006919137","display_name":"Cuauht\u00e9moc Anda","orcid":"https://orcid.org/0000-0002-4565-8418"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cuauhtemoc Anda","raw_affiliation_strings":["NCS, Sinapore"],"affiliations":[{"raw_affiliation_string":"NCS, Sinapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115457738","display_name":"Ning Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I3004594783","display_name":"Institute of High Performance Computing","ror":"https://ror.org/02n0ejh50","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3004594783","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ning Cao","raw_affiliation_strings":["College of Computing & Data Science, NTU Singapore"],"affiliations":[{"raw_affiliation_string":"College of Computing & Data Science, NTU Singapore","institution_ids":["https://openalex.org/I3004594783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320474","display_name":"Shuai Liu","orcid":"https://orcid.org/0000-0001-8573-9450"},"institutions":[{"id":"https://openalex.org/I3004594783","display_name":"Institute of High Performance Computing","ror":"https://ror.org/02n0ejh50","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3004594783","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shuai Liu","raw_affiliation_strings":["College of Computing & Data Science, NTU Singapore"],"affiliations":[{"raw_affiliation_string":"College of Computing & Data Science, NTU Singapore","institution_ids":["https://openalex.org/I3004594783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115457739","display_name":"Shaowei Ying","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaowei Ying","raw_affiliation_strings":["NCS, Sinapore"],"affiliations":[{"raw_affiliation_string":"NCS, Sinapore","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045198704","display_name":"Gao Cong","orcid":"https://orcid.org/0000-0002-4430-6373"},"institutions":[{"id":"https://openalex.org/I3004594783","display_name":"Institute of High Performance Computing","ror":"https://ror.org/02n0ejh50","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3004594783","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Gao Cong","raw_affiliation_strings":["College of Computing & Data Science, NTU Singapore"],"affiliations":[{"raw_affiliation_string":"College of Computing & Data Science, NTU Singapore","institution_ids":["https://openalex.org/I3004594783"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006919137"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20648626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"42","last_page":"45"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.764175534248352},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6129012703895569},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06037360429763794}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.764175534248352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6129012703895569},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06037360429763794},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3681771.3699917","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681771.3699917","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3681771.3699917","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681771.3699917","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2795142517","https://openalex.org/W2798476254","https://openalex.org/W2963123139","https://openalex.org/W2963608065","https://openalex.org/W2972348273","https://openalex.org/W3099011804","https://openalex.org/W3108807132","https://openalex.org/W3158027657","https://openalex.org/W3174082502","https://openalex.org/W3216660278","https://openalex.org/W4287114832","https://openalex.org/W4312768171","https://openalex.org/W4386589995","https://openalex.org/W4394932368","https://openalex.org/W6796780854"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4323768008"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"individual":[2,132],"trajectory":[3,30,49,73,94,133],"mobility":[4,142],"is":[5],"critical":[6],"for":[7,57,72,107,131],"various":[8],"urban":[9,141],"applications,":[10],"including":[11],"traffic":[12],"management":[13],"and":[14,32,68,83,87,110,138],"personalized":[15],"services.":[16],"However,":[17],"existing":[18],"deep":[19],"learning":[20],"models":[21],"often":[22],"suffer":[23],"from":[24],"overfitting":[25],"due":[26],"to":[27,34,54,79,90],"noisy,":[28],"large-scale":[29],"data":[31,50,95],"struggle":[33],"capture":[35,80],"the":[36,105,116],"unique":[37],"movement":[38],"patterns":[39],"of":[40],"minority":[41],"groups.":[42],"Additionally,":[43],"privacy":[44,102],"concerns":[45],"arise":[46],"when":[47],"personal":[48],"must":[51],"be":[52],"uploaded":[53],"cloud":[55,108],"servers":[56],"processing.":[58],"To":[59],"address":[60],"these":[61],"challenges,":[62],"we":[63],"propose":[64],"a":[65,127],"novel":[66],"feature-engineering":[67],"machine":[69],"learning-based":[70],"framework":[71,125],"prediction.":[74],"Our":[75,124],"method":[76],"incorporates":[77],"Time2Vec":[78],"both":[81,136],"periodic":[82],"trend-based":[84],"temporal":[85],"features":[86],"utilizes":[88],"CatBoost":[89],"handle":[91],"structured,":[92],"non-sequential":[93],"efficiently.":[96],"This":[97],"approach":[98],"reduces":[99],"overfitting,":[100],"enhances":[101],"by":[103],"eliminating":[104],"need":[106],"processing,":[109],"achieves":[111],"competitive":[112],"performance,":[113],"ranking":[114],"in":[115,140],"top":[117],"10":[118],"among":[119],"over":[120],"100":[121],"team":[122],"submissions.":[123],"offers":[126],"robust,":[128],"privacy-conscious":[129],"solution":[130],"prediction,":[134],"advancing":[135],"accuracy":[137],"inclusivity":[139],"applications.":[143]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}