{"id":"https://openalex.org/W2810422975","doi":"https://doi.org/10.1109/uic-atc.2017.8397485","title":"Spatiotemporal multi-task learning for citywide passenger flow prediction","display_name":"Spatiotemporal multi-task learning for citywide passenger flow prediction","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2810422975","doi":"https://doi.org/10.1109/uic-atc.2017.8397485","mag":"2810422975"},"language":"en","primary_location":{"id":"doi:10.1109/uic-atc.2017.8397485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uic-atc.2017.8397485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)","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/A5000172319","display_name":"Runxing Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Runxing Zhong","raw_affiliation_strings":["State Key Lab of Software Development Environment, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Lab of Software Development Environment, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109299440","display_name":"Weifeng Lv","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifeng Lv","raw_affiliation_strings":["State Key Lab of Software Development Environment, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Lab of Software Development Environment, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053487836","display_name":"Bowen Du","orcid":"https://orcid.org/0000-0003-0975-2367"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Du","raw_affiliation_strings":["State Key Lab of Software Development Environment, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Lab of Software Development Environment, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034757629","display_name":"Shuo Lei","orcid":"https://orcid.org/0000-0001-7031-2438"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Lei","raw_affiliation_strings":["School of Software, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005081022","display_name":"Runhe Huang","orcid":"https://orcid.org/0000-0003-4366-0458"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Runhe Huang","raw_affiliation_strings":["Computer and Information Sciences, Hosei University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Computer and Information Sciences, Hosei University, Tokyo, Japan","institution_ids":["https://openalex.org/I204291657"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000172319"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":2.4539,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90158067,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9979000091552734,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9846000075340271,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7560681104660034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6135653853416443},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4944274425506592},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.4709784984588623},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4177374243736267},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.35711634159088135},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3444589674472809},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33597609400749207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28887563943862915},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22477570176124573},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09378078579902649}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7560681104660034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6135653853416443},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4944274425506592},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.4709784984588623},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4177374243736267},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.35711634159088135},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3444589674472809},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33597609400749207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28887563943862915},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22477570176124573},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09378078579902649},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uic-atc.2017.8397485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uic-atc.2017.8397485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1614862348","https://openalex.org/W1968060540","https://openalex.org/W1979820468","https://openalex.org/W1986660883","https://openalex.org/W1989946722","https://openalex.org/W1999140058","https://openalex.org/W2043406008","https://openalex.org/W2051530877","https://openalex.org/W2065180801","https://openalex.org/W2072942628","https://openalex.org/W2075433852","https://openalex.org/W2085592822","https://openalex.org/W2085987121","https://openalex.org/W2090138387","https://openalex.org/W2093635770","https://openalex.org/W2119791987","https://openalex.org/W2121460124","https://openalex.org/W2139628333","https://openalex.org/W2143104527","https://openalex.org/W2146108992","https://openalex.org/W2159514083","https://openalex.org/W2165644552","https://openalex.org/W2222716234","https://openalex.org/W2239827863","https://openalex.org/W2268120789","https://openalex.org/W2277230653","https://openalex.org/W2515292392","https://openalex.org/W2541573029","https://openalex.org/W2913340405","https://openalex.org/W2914746235","https://openalex.org/W2949664970","https://openalex.org/W2951229220","https://openalex.org/W3101035442","https://openalex.org/W3209042722","https://openalex.org/W4239943352","https://openalex.org/W6640290990","https://openalex.org/W6677479975","https://openalex.org/W6680335960","https://openalex.org/W6689880502","https://openalex.org/W6695165298","https://openalex.org/W6726070053"],"related_works":["https://openalex.org/W2032233321","https://openalex.org/W3121970507","https://openalex.org/W2110028391","https://openalex.org/W54497855","https://openalex.org/W217960748","https://openalex.org/W3125814499","https://openalex.org/W2090827041","https://openalex.org/W2094012830","https://openalex.org/W187246281","https://openalex.org/W2079194830"],"abstract_inverted_index":{"Massive":[0],"data":[1,77,136,150],"collected":[2],"by":[3],"automated":[4],"fare":[5],"collection":[6],"(AFC)":[7],"systems":[8],"provides":[9],"unprecedented":[10],"opportunities":[11],"for":[12,23,126,141],"studying":[13],"and":[14,28,38,61,86,101,107,109,144],"predicting":[15,127,159],"citywide":[16,40,83,129,160],"passenger":[17,41,59,84,130,161],"flow,":[18,85],"which":[19],"can":[20],"be":[21],"beneficial":[22],"travel":[24],"planning,":[25],"traffic":[26,102],"management,":[27],"public":[29],"safety.":[30],"However,":[31],"it":[32],"is":[33],"very":[34],"challenging":[35],"to":[36,81],"model":[37,142],"predict":[39],"flow":[42,60],"because":[43],"there":[44],"are":[45,79],"a":[46,119],"variety":[47],"of":[48,58,75,93,154],"factors":[49],"potentially":[50],"influencing":[51],"it,":[52],"such":[53],"as":[54],"the":[55,82,94,128,152],"dynamic":[56],"change":[57],"spatiotemporal":[62,120],"correlations.":[63],"To":[64],"this":[65,68],"end,":[66],"in":[67,158],"paper,":[69],"we":[70,117,133],"first":[71],"investigate":[72],"multiple":[73,139],"types":[74],"heterogeneous":[76],"that":[78],"related":[80,98],"extract":[87],"different":[88],"features":[89],"from":[90,138],"each":[91],"type":[92],"data,":[95],"including":[96],"mobility":[97],"features,":[99,116],"connectivity":[100],"capacity,":[103],"regional":[104],"characteristics,":[105],"event":[106],"weather,":[108],"temporal":[110],"view":[111],"features.":[112],"Then,":[113],"upon":[114],"these":[115],"develop":[118],"multi-task":[121],"learning":[122],"based":[123],"regression":[124],"approach":[125,157],"flow.":[131,162],"Finally,":[132],"leverage":[134],"real-world":[135,149],"sets":[137],"sources":[140],"training":[143],"validation.":[145],"Experimental":[146],"results":[147],"on":[148],"demonstrate":[151],"effectiveness":[153],"our":[155],"proposed":[156]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}