{"id":"https://openalex.org/W2946160394","doi":"https://doi.org/10.1109/tits.2019.2915525","title":"Contextualized Spatial\u2013Temporal Network for Taxi Origin-Destination Demand Prediction","display_name":"Contextualized Spatial\u2013Temporal Network for Taxi Origin-Destination Demand Prediction","publication_year":2019,"publication_date":"2019-05-22","ids":{"openalex":"https://openalex.org/W2946160394","doi":"https://doi.org/10.1109/tits.2019.2915525","mag":"2946160394"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2019.2915525","doi":"https://doi.org/10.1109/tits.2019.2915525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2915525","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-scientific","ccf-b","cwts-core","dongbi-a","jufo-2","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.06335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102971183","display_name":"Lingbo Liu","orcid":"https://orcid.org/0000-0001-8179-6685","observed_orcids":["https://orcid.org/0000-0001-8179-6685"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingbo Liu","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8179-6685","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102993114","display_name":"Zhilin Qiu","orcid":"https://orcid.org/0000-0002-2367-5110","observed_orcids":["https://orcid.org/0000-0002-2367-5110"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhilin Qiu","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2367-5110","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042965510","display_name":"Guanbin Li","orcid":"https://orcid.org/0000-0002-4805-0926","observed_orcids":["https://orcid.org/0000-0002-4805-0926","https://orcid.org/0000-0002-2486-2890"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanbin Li","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4805-0926","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100434787","display_name":"Qing Wang","orcid":"https://orcid.org/0000-0001-7298-2734","observed_orcids":["https://orcid.org/0000-0001-7298-2734"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Wang","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087818121","display_name":"Wanli Ouyang","orcid":"https://orcid.org/0000-0002-9163-2761","observed_orcids":["https://orcid.org/0000-0002-9163-2761"]},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanli Ouyang","raw_affiliation_strings":["School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-9163-2761","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412937","display_name":"Liang Lin","orcid":"https://orcid.org/0000-0003-2248-3755","observed_orcids":["https://orcid.org/0000-0003-2248-3755","https://orcid.org/0000-0001-5704-4168"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Lin","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2248-3755","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":25.4651,"has_fulltext":true,"cited_by_count":287,"citation_normalized_percentile":{"value":0.99835756,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"20","issue":"10","first_page":"3875","last_page":"3887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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/T10698","display_name":"Transportation Planning and Optimization","score":9.999999747378752e-05,"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/T11106","display_name":"Data Management and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/taxi-demand-prediction","display_name":"taxi demand prediction","score":0.9570000171661377},{"id":"https://openalex.org/keywords/origin-destination-demand","display_name":"origin-destination demand","score":0.9570000171661377},{"id":"https://openalex.org/keywords/spatial-temporal-network","display_name":"spatial-temporal network","score":0.8019999861717224},{"id":"https://openalex.org/keywords/convlstm","display_name":"ConvLSTM","score":0.9070000052452087},{"id":"https://openalex.org/keywords/meteorological-characteristics","display_name":"meteorological features","score":0.7450000047683716},{"id":"https://openalex.org/keywords/regional-similarity","display_name":"regional similarity","score":0.42399999499320984},{"id":"https://openalex.org/keywords/intelligent-transportation-systems","display_name":"intelligent transportation systems","score":0.6079999804496765}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7065579295158386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705706775188446},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5033614039421082},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.46301978826522827},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4551948308944702},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4395732581615448},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4145687520503998},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39626505970954895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3879038691520691},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.24941453337669373},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.23632889986038208},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10098046064376831},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09353429079055786},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tits.2019.2915525","doi":"https://doi.org/10.1109/tits.2019.2915525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2019.2915525","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-scientific","ccf-b","cwts-core","dongbi-a","jufo-2","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1905.06335","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.06335","pdf_url":"https://arxiv.org/pdf/1905.06335","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1905.06335","doi":"https://doi.org/10.48550/arxiv.1905.06335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.06335","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1905.06335","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.06335","pdf_url":"https://arxiv.org/pdf/1905.06335","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.761900007724762},{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5378999710083008}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1945170033","display_name":null,"funder_award_id":"18lgpy63","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3146272683","display_name":null,"funder_award_id":"#U1811463","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G492365670","display_name":null,"funder_award_id":"61602533","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6734290121","display_name":null,"funder_award_id":"2017B010116001","funder_id":"https://openalex.org/F4320335795","funder_display_name":"Science and Technology Planning Project of Guangdong Province"},{"id":"https://openalex.org/G8057038435","display_name":null,"funder_award_id":"61702565","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/F4320329972","display_name":"SenseTime Group","ror":"https://ror.org/049wsmj07"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320335795","display_name":"Science and Technology Planning Project of Guangdong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946160394.pdf","grobid_xml":"https://content.openalex.org/works/W2946160394.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W605877012","https://openalex.org/W1485009520","https://openalex.org/W1489720431","https://openalex.org/W1504021368","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1669302834","https://openalex.org/W1942214758","https://openalex.org/W1982668936","https://openalex.org/W1988580225","https://openalex.org/W2023703387","https://openalex.org/W2028893095","https://openalex.org/W2064675550","https://openalex.org/W2069866087","https://openalex.org/W2082642205","https://openalex.org/W2113298739","https://openalex.org/W2132968912","https://openalex.org/W2134316083","https://openalex.org/W2135046866","https://openalex.org/W2148343118","https://openalex.org/W2163930022","https://openalex.org/W2183556041","https://openalex.org/W2204578866","https://openalex.org/W2270657321","https://openalex.org/W2271840356","https://openalex.org/W2276747974","https://openalex.org/W2295598076","https://openalex.org/W2475515783","https://openalex.org/W2489780108","https://openalex.org/W2513610673","https://openalex.org/W2518599539","https://openalex.org/W2528639018","https://openalex.org/W2566236551","https://openalex.org/W2614121823","https://openalex.org/W2614459600","https://openalex.org/W2695874637","https://openalex.org/W2742211145","https://openalex.org/W2743316574","https://openalex.org/W2743905801","https://openalex.org/W2766311542","https://openalex.org/W2769716261","https://openalex.org/W2782920454","https://openalex.org/W2788134583","https://openalex.org/W2792861066","https://openalex.org/W2805992315","https://openalex.org/W2808519136","https://openalex.org/W2885354784","https://openalex.org/W2890385714","https://openalex.org/W2962864047","https://openalex.org/W2963091558","https://openalex.org/W2963951674","https://openalex.org/W2965247173","https://openalex.org/W2966285804","https://openalex.org/W3102476541","https://openalex.org/W4241050402","https://openalex.org/W4285719527","https://openalex.org/W6686528296"],"related_works":["https://openalex.org/W2966285804","https://openalex.org/W4376278921","https://openalex.org/W4200351708","https://openalex.org/W4317213352","https://openalex.org/W7136602388","https://openalex.org/W3091801004","https://openalex.org/W3021687427","https://openalex.org/W3200793483","https://openalex.org/W2900503258","https://openalex.org/W3045966257"],"abstract_inverted_index":{"Taxi":[0],"demand":[1,30,78,86,111,164,185,203,269],"prediction":[2,31],"has":[3],"recently":[4],"attracted":[5],"increasing":[6],"research":[7],"interest":[8],"due":[9],"to":[10,52,101,108,156,190,211],"its":[11],"huge":[12],"potential":[13],"application":[14],"in":[15,32,90],"large-scale":[16,252],"intelligent":[17],"transportation":[18],"systems.":[19],"However,":[20],"most":[21],"of":[22,39,43,126,132,162,183,201,228,258],"the":[23,28,37,40,44,54,61,84,104,110,130,158,167,171,179,187,199,213,233,238,241,256,266],"previous":[24],"methods":[25,264],"only":[26],"considered":[27],"taxi":[29,55,62,76,85,184,202,267],"origin":[33,63,168],"regions,":[34],"but":[35],"neglected":[36],"modeling":[38,131],"specific":[41],"situation":[42],"destination":[45,172],"passengers.":[46],"We":[47,113],"believe":[48],"it":[49],"is":[50,209],"suboptimal":[51],"preallocate":[53],"into":[56],"each":[57],"region-based":[58],"solely":[59],"on":[60,250],"demand.":[64],"In":[65],"this":[66,115],"paper,":[67],"we":[68],"present":[69],"a":[70,91,118,175,191,206,220,225,251],"challenging":[71],"and":[72,141,170,186,248],"worth-exploring":[73],"task,":[74],"called":[75],"origin-destination":[77,268],"prediction,":[79],"which":[80,124],"aims":[81],"at":[82],"predicting":[83],"between":[87,215,240],"all-region":[88],"pairs":[89],"future":[92],"time":[93],"interval.":[94],"Its":[95],"main":[96],"challenges":[97],"come":[98],"from":[99,166],"how":[100],"effectively":[102],"capture":[103],"diverse":[105],"contextual":[106],"information":[107,189],"learn":[109,157],"patterns.":[112],"address":[114],"problem":[116],"with":[117,232],"novel":[119],"contextualized":[120],"spatial-temporal":[121],"network":[122],"(CSTN),":[123],"consists":[125],"three":[127],"components":[128],"for":[129,198,265],"local":[133,159,180],"spatial":[134,160,181],"context":[135,139,144],"(LSC),":[136],"temporal":[137],"evolution":[138],"(TEC),":[140],"global":[142,221],"correlation":[143,214,222],"(GCC),":[145],"respectively.":[146],"First,":[147],"an":[148],"LSC":[149],"module":[150,177,208],"utilizes":[151],"two":[152],"convolution":[153],"neural":[154],"networks":[155],"dependencies":[161],"taxi,":[163],"respectively,":[165],"view":[169],"view.":[173],"Second,":[174],"TEC":[176],"incorporates":[178],"features":[182],"meteorological":[188],"Convolutional":[192],"Long":[193],"Short-term":[194],"Memory":[195],"Network":[196],"(ConvLSTM)":[197],"analysis":[200],"evolution.":[204],"Finally,":[205],"GCC":[207],"applied":[210],"model":[212],"all":[216,229],"regions":[217],"by":[218],"computing":[219],"feature":[223],"as":[224,237],"weighted":[226],"sum":[227],"regional":[230],"features,":[231],"weights":[234],"being":[235],"calculated":[236],"similarity":[239],"corresponding":[242],"region":[243],"pairs.":[244],"The":[245],"extensive":[246],"experiments":[247],"evaluations":[249],"dataset":[253],"well":[254],"demonstrate":[255],"superiority":[257],"our":[259],"CSTN":[260],"over":[261],"other":[262],"compared":[263],"prediction.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":23},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":41},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":62},{"year":2021,"cited_by_count":44},{"year":2020,"cited_by_count":28},{"year":2019,"cited_by_count":7}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-05-29T00:00:00"}
