{"id":"https://openalex.org/W3208454183","doi":"https://doi.org/10.1145/3477577","title":"Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency","display_name":"Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency","publication_year":2021,"publication_date":"2021-10-22","ids":{"openalex":"https://openalex.org/W3208454183","doi":"https://doi.org/10.1145/3477577","mag":"3208454183"},"language":"en","primary_location":{"id":"doi:10.1145/3477577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477577","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3477577","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110713149","display_name":"Jie Feng","orcid":"https://orcid.org/0000-0002-4683-1665"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Feng","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039721156","display_name":"Ziqian Lin","orcid":"https://orcid.org/0000-0002-6977-0156"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqian Lin","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035525018","display_name":"Can Rong","orcid":"https://orcid.org/0000-0002-5846-724X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Can Rong","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101439558","display_name":"Funing Sun","orcid":"https://orcid.org/0000-0002-1388-4541"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Funing Sun","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003854328","display_name":"Diansheng Guo","orcid":"https://orcid.org/0000-0003-3483-4153"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Diansheng Guo","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044100655","display_name":"Depeng Jin","orcid":"https://orcid.org/0000-0003-0419-5514"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Depeng Jin","raw_affiliation_strings":["BNRist, Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"BNRist, Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5110713149"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.5421,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.80932507,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"16","issue":"3","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9962000250816345,"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/T10524","display_name":"Traffic control and management","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/crowds","display_name":"Crowds","score":0.8601992726325989},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7436108589172363},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5838668942451477},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5738810896873474},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5566020011901855},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5250368118286133},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5116565823554993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4875950217247009},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.46499648690223694},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4627950191497803},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40152624249458313},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.21438658237457275},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.12059682607650757},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09766587615013123}],"concepts":[{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.8601992726325989},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7436108589172363},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5838668942451477},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5738810896873474},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5566020011901855},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5250368118286133},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5116565823554993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4875950217247009},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.46499648690223694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4627950191497803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40152624249458313},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.21438658237457275},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.12059682607650757},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09766587615013123},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477577","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3477577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477577","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1757078732","display_name":null,"funder_award_id":"20031887521","funder_id":"https://openalex.org/F4320329777","funder_display_name":"Beijing National Research Center For Information Science And Technology"},{"id":"https://openalex.org/G8964616020","display_name":null,"funder_award_id":"61971267, 61972223, 61861136003, and 61621091","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329777","display_name":"Beijing National Research Center For Information Science And Technology","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2073013176","https://openalex.org/W2097117768","https://openalex.org/W2153458569","https://openalex.org/W2217607432","https://openalex.org/W2297059404","https://openalex.org/W2417429787","https://openalex.org/W2514936170","https://openalex.org/W2528040708","https://openalex.org/W2530386080","https://openalex.org/W2614121823","https://openalex.org/W2752782242","https://openalex.org/W2768008502","https://openalex.org/W2805992315","https://openalex.org/W2808377988","https://openalex.org/W2904813135","https://openalex.org/W2910892140","https://openalex.org/W2945622688","https://openalex.org/W2949117887","https://openalex.org/W2950817888","https://openalex.org/W2962790412","https://openalex.org/W2963420686","https://openalex.org/W3080466448","https://openalex.org/W3135728061","https://openalex.org/W4241115065","https://openalex.org/W4255949318"],"related_works":["https://openalex.org/W4240200267","https://openalex.org/W1511510665","https://openalex.org/W2154955495","https://openalex.org/W1749168706","https://openalex.org/W606709926","https://openalex.org/W2980347456","https://openalex.org/W1643071209","https://openalex.org/W4299159492","https://openalex.org/W422913495","https://openalex.org/W2148067325"],"abstract_inverted_index":{"Crowd":[0],"flow":[1,61,162],"prediction":[2,163],"is":[3],"of":[4,11,29,43,51,111,118,151,159],"great":[5],"importance":[6],"in":[7,34,54,78,96],"a":[8,32,35,46,69,125],"wide":[9],"range":[10],"applications":[12],"from":[13],"urban":[14],"planning,":[15],"traffic":[16,28,42],"control":[17],"to":[18,74,87,107,114,130],"public":[19],"safety.":[20],"It":[21],"aims":[22],"at":[23],"predicting":[24],"the":[25,55,59,79,84,89,109,119,132,138,149,157,160,169],"inflow":[26],"(the":[27,41],"crowds":[30,44],"entering":[31],"region":[33,47,53],"given":[36],"time":[37,103],"interval)":[38],"and":[39,102],"outflow":[40],"leaving":[45],"for":[48],"other":[49],"places)":[50],"each":[52],"city":[56],"with":[57,168],"knowing":[58],"historical":[60],"data.":[62],"In":[63],"this":[64],"article,":[65],"we":[66,123],"propose":[67,124],"DeepSTN+,":[68],"deep":[70],"learning-based":[71],"convolutional":[72],"model,":[73,153],"predict":[75],"crowd":[76,94,120,161],"flows":[77,95],"metropolis.":[80],"First,":[81],"DeepSTN+":[82,155],"employs":[83],"ConvPlus":[85],"structure":[86],"model":[88],"long-range":[90],"spatial":[91],"dependence":[92],"among":[93],"different":[97],"regions.":[98],"Further,":[99],"PoI":[100],"distributions":[101],"factor":[104],"are":[105],"combined":[106],"express":[108],"effect":[110],"location":[112],"attributes":[113],"introduce":[115],"prior":[116],"knowledge":[117],"movements.":[121],"Finally,":[122],"temporal":[126],"attention-based":[127],"fusion":[128],"mechanism":[129],"stabilize":[131],"training":[133],"process,":[134],"which":[135],"further":[136],"improves":[137],"performance.":[139],"Extensive":[140],"experimental":[141],"results":[142],"based":[143],"on":[144],"four":[145],"real-life":[146],"datasets":[147],"demonstrate":[148],"superiority":[150],"our":[152],"i.e.,":[154],"reduces":[156],"error":[158],"by":[164],"approximately":[165],"10%\u201321%":[166],"compared":[167],"state-of-the-art":[170],"baselines.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
