{"id":"https://openalex.org/W4312880083","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892179","title":"Towards Dual-Modal Crowd Density Forecasting in Transportation Building","display_name":"Towards Dual-Modal Crowd Density Forecasting in Transportation Building","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312880083","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892179"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892179","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5033493807","display_name":"Weiheng Liu","orcid":"https://orcid.org/0009-0008-6756-6590"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiheng Liu","raw_affiliation_strings":["School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109994098","display_name":"Yitao Yang","orcid":"https://orcid.org/0000-0002-2842-3975"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yitao Yang","raw_affiliation_strings":["School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081658553","display_name":"Jinghui Zhong","orcid":"https://orcid.org/0000-0003-0113-3430"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghui Zhong","raw_affiliation_strings":["School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science &#x0026; Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033493807"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.8955,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71485623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7114027142524719},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6701986193656921},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.6633133888244629},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6186730861663818},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4927733242511749},{"id":"https://openalex.org/keywords/crowd-sourcing","display_name":"Crowd sourcing","score":0.4184947609901428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33308279514312744},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3327229619026184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28700360655784607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7114027142524719},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6701986193656921},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.6633133888244629},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6186730861663818},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4927733242511749},{"id":"https://openalex.org/C3018396927","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowd sourcing","level":2,"score":0.4184947609901428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33308279514312744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3327229619026184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28700360655784607},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892179","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G7061044932","display_name":null,"funder_award_id":"62076098","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W192919555","https://openalex.org/W1888172398","https://openalex.org/W1910776219","https://openalex.org/W1988134233","https://openalex.org/W2097729326","https://openalex.org/W2122659384","https://openalex.org/W2424778531","https://openalex.org/W2463631526","https://openalex.org/W2792764867","https://openalex.org/W2895051362","https://openalex.org/W2925926810","https://openalex.org/W2951659295","https://openalex.org/W2963001155","https://openalex.org/W2963358464","https://openalex.org/W2964209782","https://openalex.org/W2991653934","https://openalex.org/W2998436408","https://openalex.org/W3027606690","https://openalex.org/W3034785991","https://openalex.org/W3035096461","https://openalex.org/W3103720336","https://openalex.org/W3175016653","https://openalex.org/W3175126800","https://openalex.org/W4206507298","https://openalex.org/W6678029071","https://openalex.org/W6746015598","https://openalex.org/W6749825310","https://openalex.org/W6753331806","https://openalex.org/W6754756387","https://openalex.org/W6777463810","https://openalex.org/W6785773631"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W147410782","https://openalex.org/W4287804464","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W2152352598","https://openalex.org/W803346624","https://openalex.org/W3108539254"],"abstract_inverted_index":{"Crowd":[0],"density":[1,62],"forecasting":[2],"in":[3,31,63],"transportation":[4,54,65,85,105],"building":[5],"has":[6],"valuable":[7],"applications":[8],"involving":[9],"security,":[10],"crowd":[11,27,61,124],"management,":[12],"and":[13,52,83,103,113,140],"service":[14],"design.":[15],"The":[16,67,87,108,126],"existing":[17],"methods":[18],"lack":[19],"the":[20,35,48,53,59,64,70,76,80,84,98,101,104,115,123,137],"prediction":[21,144],"performance":[22],"to":[23,34,57,74,96,121],"forecast":[24,58],"long-term":[25],"(minutes-long)":[26],"density,":[28],"which":[29],"specializes":[30],"being":[32],"sensitive":[33],"external":[36],"condition.":[37],"Thus,":[38],"we":[39],"propose":[40],"a":[41],"method":[42,132],"that":[43,130],"can":[44],"combine":[45],"dual-modal":[46,138],"information:":[47],"surveillance":[49],"video":[50,81,102],"streams":[51,82],"schedule":[55,106],"information":[56,112,139],"future":[60],"building.":[66],"model":[68],"utilizes":[69],"temporal":[71],"convolution":[72],"layers":[73,120],"extract":[75],"time":[77],"dependence":[78],"of":[79],"schedule.":[86],"pooling":[88],"with":[89],"an":[90],"assignment":[91],"matrix":[92],"technique":[93],"is":[94],"used":[95],"learn":[97],"correlation":[99],"between":[100],"information.":[107],"predictor":[109],"fuses":[110],"both":[111],"uses":[114],"Gated":[116],"Recurrent":[117],"Unit":[118],"(GRU)":[119],"predict":[122],"density.":[125],"experimental":[127],"results":[128],"show":[129],"our":[131],"could":[133],"effectively":[134],"benefit":[135],"from":[136],"give":[141],"more":[142],"accurate":[143],"results.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
