{"id":"https://openalex.org/W3115560852","doi":"https://doi.org/10.1145/3437963.3441785","title":"Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network","display_name":"Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network","publication_year":2021,"publication_date":"2021-03-06","ids":{"openalex":"https://openalex.org/W3115560852","doi":"https://doi.org/10.1145/3437963.3441785","mag":"3115560852"},"language":"en","primary_location":{"id":"doi:10.1145/3437963.3441785","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441785","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","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/A5027623536","display_name":"Congcong Miao","orcid":"https://orcid.org/0000-0003-3265-049X"},"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"]},{"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":true,"raw_author_name":"Congcong Miao","raw_affiliation_strings":["Tsinghua University, BNRist, & Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, BNRist, & Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101925655","display_name":"Jiajun Fu","orcid":"https://orcid.org/0000-0002-8542-9556"},"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":"Jiajun Fu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100660344","display_name":"Jilong Wang","orcid":"https://orcid.org/0000-0002-4493-5145"},"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":"Jilong Wang","raw_affiliation_strings":["Tsinghua University & BNRist, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University & BNRist, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101462416","display_name":"Heng Yu","orcid":"https://orcid.org/0000-0001-6907-9958"},"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":"Heng Yu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046491594","display_name":"Botao Yao","orcid":null},"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":"Botao Yao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109483749","display_name":"An\u2010qi Zhong","orcid":null},"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":"Anqi Zhong","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084932231","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0002-3864-9265"},"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":"Jie Chen","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008004626","display_name":"Zekun He","orcid":"https://orcid.org/0009-0000-8020-0595"},"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":"Zekun He","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5027623536"],"corresponding_institution_ids":["https://openalex.org/I2250653659","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4745,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60587904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"806","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9869999885559082,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.769301176071167},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6804335713386536},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5690162777900696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5570948123931885},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.48208481073379517},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43731653690338135},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4272601902484894},{"id":"https://openalex.org/keywords/temporal-scales","display_name":"Temporal scales","score":0.4257311224937439},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3716106414794922},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35363703966140747},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.15526551008224487},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13987243175506592}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.769301176071167},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6804335713386536},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5690162777900696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5570948123931885},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.48208481073379517},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43731653690338135},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4272601902484894},{"id":"https://openalex.org/C2777489503","wikidata":"https://www.wikidata.org/wiki/Q7698936","display_name":"Temporal scales","level":2,"score":0.4257311224937439},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3716106414794922},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35363703966140747},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.15526551008224487},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13987243175506592},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3437963.3441785","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441785","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W846669277","https://openalex.org/W1973943669","https://openalex.org/W1982978808","https://openalex.org/W1988580225","https://openalex.org/W2027392238","https://openalex.org/W2034707435","https://openalex.org/W2069929199","https://openalex.org/W2150355110","https://openalex.org/W2530386080","https://openalex.org/W2550553598","https://openalex.org/W2561568083","https://openalex.org/W2592023122","https://openalex.org/W2614121823","https://openalex.org/W2624190409","https://openalex.org/W2695874637","https://openalex.org/W2736334449","https://openalex.org/W2741249238","https://openalex.org/W2743316574","https://openalex.org/W2767655600","https://openalex.org/W2782920454","https://openalex.org/W2788114581","https://openalex.org/W2808377988","https://openalex.org/W2904813135","https://openalex.org/W2962790412","https://openalex.org/W2962933129","https://openalex.org/W2997590591","https://openalex.org/W3002621527"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W2498789492","https://openalex.org/W2521347458","https://openalex.org/W4249847449","https://openalex.org/W2729981612","https://openalex.org/W4233449973","https://openalex.org/W2925692864","https://openalex.org/W44395729"],"abstract_inverted_index":{"Predicting":[0],"crowd":[1,15,38,76],"flows":[2,16,77],"is":[3,17,120,150],"crucial":[4],"for":[5,104],"urban":[6],"planning,":[7],"traffic":[8],"management":[9],"and":[10,98,145],"public":[11],"safety.":[12],"However,":[13],"predicting":[14],"not":[18],"trivial":[19],"because":[20],"of":[21,37,75,172],"three":[22,162],"challenges:":[23],"1)":[24],"highly":[25,163],"heterogeneous":[26,164],"mobility":[27,166],"data":[28],"collected":[29],"by":[30,122],"various":[31],"services;":[32],"2)":[33],"complex":[34,91],"spatio-temporal":[35],"correlations":[36,43,96],"flows,":[39],"including":[40],"multi-scale":[41,131],"spatial":[42,112,132],"along":[44],"with":[45,78],"non-linear":[46],"temporal":[47,53,155],"correlations.":[48],"3)":[49],"diversity":[50],"in":[51,148],"long-term":[52,143],"patterns.":[54,156],"To":[55],"tackle":[56],"these":[57],"challenges,":[58],"we":[59],"proposed":[60],"an":[61],"end-to-end":[62],"architecture,":[63],"called":[64],"pyramid":[65,116],"dilated":[66,117,125],"spatial-temporal":[67,73,95],"network":[68,119],"(PDSTN),":[69],"to":[70,89,129,152,168],"effectively":[71],"learn":[72],"representations":[74],"a":[79,115,136],"novel":[80,137],"attention":[81,138],"mechanism.":[82],"Specifically,":[83],"PDSTN":[84,173,179],"employs":[85],"the":[86,111,146,170,175,184],"ConvLSTM":[87,102,127],"structure":[88],"identify":[90],"features":[92],"that":[93],"capture":[94],"simultaneously,":[97],"then":[99],"stacks":[100],"multiple":[101],"units":[103],"deeper":[105],"feature":[106],"extraction.":[107],"For":[108],"further":[109],"improving":[110],"learning":[113],"ability,":[114],"residual":[118,126],"introduced":[121],"adopting":[123],"several":[124],"networks":[128],"extract":[130],"information.":[133],"In":[134],"addition,":[135],"mechanism,":[139],"which":[140],"considers":[141],"both":[142],"periodicity":[144],"shift":[147],"periodicity,":[149],"designed":[151],"study":[153],"diverse":[154],"Extensive":[157],"experiments":[158],"were":[159],"conducted":[160],"on":[161],"real-world":[165],"datasets":[167],"illustrate":[169],"effectiveness":[171],"beyond":[174],"state-of-the-art":[176],"methods.":[177],"Moreover,":[178],"provides":[180],"intuitive":[181],"interpretation":[182],"into":[183],"prediction.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}