{"id":"https://openalex.org/W4402258633","doi":"https://doi.org/10.1109/tits.2024.3447282","title":"Deep Probabilistic Forecasting of Multivariate Count Data With \u201cSums and Shares\u201d Distributions: A Case Study on Pedestrian Counts in a Multimodal Transport Hub","display_name":"Deep Probabilistic Forecasting of Multivariate Count Data With \u201cSums and Shares\u201d Distributions: A Case Study on Pedestrian Counts in a Multimodal Transport Hub","publication_year":2024,"publication_date":"2024-09-05","ids":{"openalex":"https://openalex.org/W4402258633","doi":"https://doi.org/10.1109/tits.2024.3447282"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3447282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3447282","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,"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"},"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/A5017137676","display_name":"Paul de Nailly","orcid":"https://orcid.org/0000-0001-6204-6176"},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Paul de Nailly","raw_affiliation_strings":["COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, France"],"raw_orcid":"https://orcid.org/0000-0001-6204-6176","affiliations":[{"raw_affiliation_string":"COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087274722","display_name":"\u00c9tienne C\u00f4me","orcid":"https://orcid.org/0000-0002-0459-6388"},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Etienne C\u00f4me","raw_affiliation_strings":["COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France"],"raw_orcid":"https://orcid.org/0000-0002-0459-6388","affiliations":[{"raw_affiliation_string":"COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029516915","display_name":"Latifa Oukhellou","orcid":"https://orcid.org/0000-0002-5193-1732"},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Latifa Oukhellou","raw_affiliation_strings":["COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France"],"raw_orcid":"https://orcid.org/0000-0002-5193-1732","affiliations":[{"raw_affiliation_string":"COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076973948","display_name":"Allou Sam\u00e9","orcid":"https://orcid.org/0000-0003-1531-6019"},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Allou Sam\u00e9","raw_affiliation_strings":["COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France"],"raw_orcid":"https://orcid.org/0000-0003-1531-6019","affiliations":[{"raw_affiliation_string":"COSYS-GRETTIA, Universit&#x00E9; Gustave Eiffel, Marne-la-Vall&#x00E9;e, Champs-sur-Marne, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080045653","display_name":"Jacques Ferri\u00e8re","orcid":"https://orcid.org/0000-0001-9643-752X"},"institutions":[{"id":"https://openalex.org/I910266844","display_name":"R\u00e9gie Autonome des Transports Parisiens (France)","ror":"https://ror.org/020k76687","country_code":"FR","type":"company","lineage":["https://openalex.org/I910266844"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jacques Ferri\u00e8re","raw_affiliation_strings":["RATP, Paris, France"],"raw_orcid":"https://orcid.org/0000-0001-9643-752X","affiliations":[{"raw_affiliation_string":"RATP, Paris, France","institution_ids":["https://openalex.org/I910266844"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074593281","display_name":"Yasmine Merad-Boudia","orcid":null},"institutions":[{"id":"https://openalex.org/I910266844","display_name":"R\u00e9gie Autonome des Transports Parisiens (France)","ror":"https://ror.org/020k76687","country_code":"FR","type":"company","lineage":["https://openalex.org/I910266844"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yasmine Merad-Boudia","raw_affiliation_strings":["RATP, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RATP, Paris, France","institution_ids":["https://openalex.org/I910266844"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5017137676"],"corresponding_institution_ids":["https://openalex.org/I4210154111"],"apc_list":null,"apc_paid":null,"fwci":1.5223,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81172357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"25","issue":"11","first_page":"15687","last_page":"15701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9835000038146973,"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":0.9835000038146973,"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":0.9822999835014343,"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":0.9818999767303467,"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/multivariate-statistics","display_name":"Multivariate statistics","score":0.7471058368682861},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7386130690574646},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.703700602054596},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6546688079833984},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5558510422706604},{"id":"https://openalex.org/keywords/multivariate-analysis","display_name":"Multivariate analysis","score":0.46889060735702515},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4650496244430542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42932915687561035},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.38699713349342346},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3482147455215454},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3445364832878113},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29011374711990356},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2885620594024658}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7471058368682861},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7386130690574646},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.703700602054596},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6546688079833984},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5558510422706604},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.46889060735702515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4650496244430542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42932915687561035},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38699713349342346},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3482147455215454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3445364832878113},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29011374711990356},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2885620594024658},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3447282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3447282","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,"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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1586335931","https://openalex.org/W1982978808","https://openalex.org/W1984113680","https://openalex.org/W1992570173","https://openalex.org/W2002841906","https://openalex.org/W2049952439","https://openalex.org/W2064675550","https://openalex.org/W2071291375","https://openalex.org/W2093635770","https://openalex.org/W2111991989","https://openalex.org/W2131819535","https://openalex.org/W2135591260","https://openalex.org/W2279382301","https://openalex.org/W2544770515","https://openalex.org/W2573587735","https://openalex.org/W2695874637","https://openalex.org/W2775254614","https://openalex.org/W2830616863","https://openalex.org/W2884128153","https://openalex.org/W2901356556","https://openalex.org/W2919115771","https://openalex.org/W2979058910","https://openalex.org/W2980994438","https://openalex.org/W2983172253","https://openalex.org/W3013767593","https://openalex.org/W3032980493","https://openalex.org/W3043505188","https://openalex.org/W3090285306","https://openalex.org/W3122056586","https://openalex.org/W3123909522","https://openalex.org/W3125564657","https://openalex.org/W3133216520","https://openalex.org/W3139421027","https://openalex.org/W3169919178","https://openalex.org/W3184671611","https://openalex.org/W3186458376","https://openalex.org/W3214734733","https://openalex.org/W4210616753","https://openalex.org/W4280550128","https://openalex.org/W4281758366","https://openalex.org/W4308080202","https://openalex.org/W4309734258","https://openalex.org/W4378650900","https://openalex.org/W4387496398","https://openalex.org/W6631190155","https://openalex.org/W6639064113","https://openalex.org/W6679436768","https://openalex.org/W6729542563","https://openalex.org/W6730235577","https://openalex.org/W6738536549","https://openalex.org/W6767610075","https://openalex.org/W6768257707","https://openalex.org/W6773967291","https://openalex.org/W6788328477"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W40745829","https://openalex.org/W4318262572","https://openalex.org/W1978357124","https://openalex.org/W1578824628","https://openalex.org/W2032728545","https://openalex.org/W1570805059","https://openalex.org/W4250754046","https://openalex.org/W4243682621","https://openalex.org/W2036849593"],"abstract_inverted_index":{"Forecasting":[0],"counts":[1,77,186,234],"data":[2,78,99,114,173,205],"in":[3,28,192,201,218,235],"transportation":[4,30],"areas":[5],"can":[6,89],"enrich":[7],"passenger":[8],"information":[9],"for":[10,55],"public":[11],"transport":[12,190,244],"passengers,":[13],"who":[14],"may":[15],"thus":[16],"better":[17],"plan":[18],"their":[19],"trips.":[20],"Moreover,":[21,223],"forecasting":[22,168,225],"with":[23,73,115,131,161],"uncertainty":[24,91],"is":[25,40,154],"particularly":[26],"important":[27],"the":[29,33,66,69,74,112,116,151,165,193,204,213,219],"domain,":[31],"where":[32,203],"risk":[34],"of":[35,68,76,97,111,118,150,215],"poorly":[36],"managed":[37],"high":[38],"demand":[39],"to":[41,139,230,237],"be":[42],"avoided.":[43],"In":[44],"this":[45,159,224],"paper,":[46],"we":[47],"propose":[48],"a":[49,108,119,132,180,188],"new":[50],"probabilistic":[51,167],"prediction":[52],"model":[53,64,106,153,160,197,217],"well-suited":[54],"multivariate,":[56],"overdispersed,":[57],"and":[58,82,100,123,134,143,179],"possibly":[59],"correlated":[60,144],"count":[61,129,145],"data.":[62,146],"This":[63],"combines":[65],"strength":[67],"deep":[70,86],"learning":[71,87],"framework":[72],"modeling":[75],"allowed":[79],"by":[80,92,101],"\u201csums":[81,133],"shares\u201d":[83,135],"distributions.":[84,104],"Indeed,":[85],"models":[88,169,200],"handle":[90],"relying":[93],"on":[94,184],"an":[95,227],"abstraction":[96],"contextual":[98],"assuming":[102],"output":[103],"Our":[105,196],"learns":[107],"latent":[109],"representation":[110],"input":[113],"help":[117],"recurrent":[120],"neural":[121],"network":[122],"then":[124],"translates":[125],"it":[126],"into":[127],"multivariate":[128,141],"predictions":[130],"distribution,":[136],"well":[137],"suited":[138],"tackle":[140],"overdispersed":[142],"An":[147],"extensive":[148],"benchmark":[149],"proposed":[152],"carried":[155],"out.":[156],"We":[157],"compare":[158],"seven":[162],"others":[163],"from":[164],"state-of-the-art":[166],"using":[170],"five":[171],"open-source":[172],"(bikes,":[174],"taxis,":[175],"railways,":[176],"traffic,":[177],"wikipedia)":[178],"specific":[181,220],"use":[182,221],"case":[183],"pedestrian":[185,233],"within":[187],"multimodal":[189],"hub":[191],"Paris":[194],"Region.":[195],"outperforms":[198],"other":[199],"situations":[202],"present":[206],"temporal":[207],"regularities.":[208],"The":[209],"results":[210],"also":[211],"highlight":[212],"potential":[214],"our":[216],"case.":[222],"represents":[226],"interesting":[228],"way":[229],"predict":[231],"short-term":[232],"response":[236],"different":[238],"events,":[239],"such":[240],"as":[241],"concerts":[242],"or":[243],"disruptions.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
