{"id":"https://openalex.org/W4412368979","doi":"https://doi.org/10.1186/s40537-025-01214-6","title":"Early crowd forecasting away from stations by geographically complemented regression using transit search and mobility logs","display_name":"Early crowd forecasting away from stations by geographically complemented regression using transit search and mobility logs","publication_year":2025,"publication_date":"2025-07-08","ids":{"openalex":"https://openalex.org/W4412368979","doi":"https://doi.org/10.1186/s40537-025-01214-6"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01214-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01214-6","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-025-01214-6.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1186/s40537-025-01214-6.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010626872","display_name":"Soto Anno","orcid":"https://orcid.org/0009-0009-7075-8953"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Soto Anno","raw_affiliation_strings":["Department of Computer Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043621466","display_name":"Kota Tsubouchi","orcid":"https://orcid.org/0000-0002-7753-8939"},"institutions":[{"id":"https://openalex.org/I71799228","display_name":"KDDI (Japan)","ror":"https://ror.org/03r7fm174","country_code":"JP","type":"company","lineage":["https://openalex.org/I71799228"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kota Tsubouchi","raw_affiliation_strings":["LY Corporation, Kioi Tower, 1-3 Kioicho, Chiyoda-ku, Tokyo, 102-8282, Japan"],"affiliations":[{"raw_affiliation_string":"LY Corporation, Kioi Tower, 1-3 Kioicho, Chiyoda-ku, Tokyo, 102-8282, Japan","institution_ids":["https://openalex.org/I71799228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032839697","display_name":"Masamichi Shimosaka","orcid":"https://orcid.org/0000-0003-0558-2006"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masamichi Shimosaka","raw_affiliation_strings":["Department of Computer Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010626872"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":3.928,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.92782152,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991000294685364,"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/T10298","display_name":"Urban Transport and Accessibility","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7242954969406128},{"id":"https://openalex.org/keywords/poisson-regression","display_name":"Poisson regression","score":0.58678138256073},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5802907347679138},{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.5142597556114197},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.5046509504318237},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4868268370628357},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.29656335711479187}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7242954969406128},{"id":"https://openalex.org/C73269764","wikidata":"https://www.wikidata.org/wiki/Q954529","display_name":"Poisson regression","level":3,"score":0.58678138256073},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5802907347679138},{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.5142597556114197},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.5046509504318237},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4868268370628357},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.29656335711479187},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01214-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01214-6","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-025-01214-6.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7fb70e97a8224b889aa074221d0b5b2e","is_oa":true,"landing_page_url":"https://doaj.org/article/7fb70e97a8224b889aa074221d0b5b2e","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-52 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01214-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01214-6","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-025-01214-6.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G6522415300","display_name":null,"funder_award_id":"22J22725","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412368979.pdf","grobid_xml":"https://content.openalex.org/works/W4412368979.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W221898952","https://openalex.org/W1592177705","https://openalex.org/W1607712735","https://openalex.org/W1966139184","https://openalex.org/W1970846899","https://openalex.org/W2008345648","https://openalex.org/W2009415795","https://openalex.org/W2017288758","https://openalex.org/W2027047167","https://openalex.org/W2051434435","https://openalex.org/W2053695737","https://openalex.org/W2064675550","https://openalex.org/W2068245435","https://openalex.org/W2075433852","https://openalex.org/W2091745653","https://openalex.org/W2163605009","https://openalex.org/W2165178985","https://openalex.org/W2304101694","https://openalex.org/W2404453404","https://openalex.org/W2468907370","https://openalex.org/W2515356824","https://openalex.org/W2515454571","https://openalex.org/W2528040708","https://openalex.org/W2528639018","https://openalex.org/W2530386080","https://openalex.org/W2556647056","https://openalex.org/W2760426769","https://openalex.org/W2788950488","https://openalex.org/W2802679249","https://openalex.org/W2904048914","https://openalex.org/W2904560462","https://openalex.org/W2904813135","https://openalex.org/W2907492528","https://openalex.org/W2913340405","https://openalex.org/W2914657261","https://openalex.org/W2950099298","https://openalex.org/W2963155035","https://openalex.org/W2965341826","https://openalex.org/W2972229264","https://openalex.org/W2988110904","https://openalex.org/W2990955039","https://openalex.org/W2998347490","https://openalex.org/W3003220537","https://openalex.org/W3022643593","https://openalex.org/W3080253043","https://openalex.org/W3108840981","https://openalex.org/W3132782787","https://openalex.org/W3160778082","https://openalex.org/W3168997536","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3193281533","https://openalex.org/W3200036893","https://openalex.org/W3208915345","https://openalex.org/W3217016897","https://openalex.org/W4214914421","https://openalex.org/W4235213111","https://openalex.org/W4280531713","https://openalex.org/W4283821444","https://openalex.org/W4293775970","https://openalex.org/W4367046688","https://openalex.org/W4367299897","https://openalex.org/W4375754785","https://openalex.org/W4387068124","https://openalex.org/W4389945737","https://openalex.org/W4393158355","https://openalex.org/W4396578404","https://openalex.org/W6796780854","https://openalex.org/W6997162883"],"related_works":["https://openalex.org/W3130070104","https://openalex.org/W1580193255","https://openalex.org/W3102840199","https://openalex.org/W3001427781","https://openalex.org/W3048859969","https://openalex.org/W2052743154","https://openalex.org/W3162329824","https://openalex.org/W4388420020","https://openalex.org/W4238517002","https://openalex.org/W4385572368"],"abstract_inverted_index":{"Abstract":[0],"Forecasting":[1],"crowd":[2,26,80,104,267,293],"gatherings":[3,268],"in":[4,17,89,91,158,237,274],"advance,":[5],"such":[6,243],"as":[7,179,244],"1":[8,156,168,269],"week":[9,88,157,169,270],"before":[10,271],"they":[11,123],"happen,":[12],"plays":[13],"a":[14,87,228,288],"vital":[15],"role":[16],"ensuring":[18],"smooth":[19],"mobility":[20,34,131,198,215],"and":[21,95,133,184,197,214,219,254],"public":[22],"safety.":[23],"Although":[24],"early":[25,79,292],"forecasting":[27,42,81,105],"has":[28],"become":[29],"possible":[30],"by":[31,100,137,160],"leveraging":[32,138],"visitors\u2019":[33],"schedules":[35,196],"extracted":[36],"from":[37,64,98,239],"transit":[38,134,162],"search":[39,135,163],"logs,":[40],"the":[41,52,61,118,139,146,149,172,182,188,201,245,250,255,263],"area":[43],"is":[44],"limited":[45],"to":[46,153,171,208,241,278,282],"regions":[47],"near":[48],"railroad":[49,126],"stations":[50,65,99,127,218],"because":[51],"logs":[53,132,136,164],"do":[54],"not":[55],"explicitly":[56],"reflect,":[57],"but":[58],"only":[59],"implicitly,":[60],"locations":[62],"away":[63,97],"where":[66],"people":[67,121,206],"go":[68],"after":[69,122],"arriving.":[70],"To":[71],"address":[72],"this":[73,75],"issue,":[74],"paper":[76],"presents":[77],"an":[78,102,154,224],"method":[82,116],"capable":[83],"of":[84,120,142,151,181,203],"predicting":[85,205],"crowding":[86],"advance":[90,159],"both":[92],"station":[93,194],"vicinities":[94],"areas":[96,275],"introducing":[101],"innovative":[103],"model":[106,147,189],"called":[107],"geographically":[108],"complemented":[109],"multi-task":[110,191],"Poisson":[111],"regression":[112],"(GCPR)":[113],".":[114],"Our":[115],"infers":[117],"flows":[119],"arrive":[124],"at":[125],"based":[128,211],"on":[129,212],"GPS-based":[130],"heterogeneous":[140],"characteristics":[141],"nearby":[143],"stations.":[144],"Specifically,":[145],"forecasts":[148],"number":[150],"visitors":[152],"event":[155],"using":[161,227],"recorded":[165],"more":[166],"than":[167],"prior":[170],"event,":[173],"along":[174],"with":[175],"contextual":[176],"features":[177],"(such":[178],"day":[180],"week)":[183],"time":[185],"information.":[186],"Furthermore,":[187],"performs":[190],"learning":[192],"for":[193,291],"arrival":[195],"patterns,":[199],"addressing":[200],"challenge":[202],"accurately":[204],"flow":[207],"congestion":[209],"points":[210],"geographical":[213],"proximity":[216],"between":[217],"crowded":[220],"areas.":[221],"We":[222],"conduct":[223],"empirical":[225],"evaluation":[226],"real-world":[229],"dataset":[230],"that":[231,262],"includes":[232],"12":[233],"large-scale":[234],"events":[235],"held":[236],"Japan":[238],"2019":[240],"2020,":[242],"Jingu":[246],"Gaien":[247],"Fireworks":[248],"Festival,":[249],"Comik":[251],"Market":[252],"96,":[253],"Rugby":[256],"World":[257],"Cup":[258],"2019.":[259],"Results":[260],"demonstrate":[261],"GCPR":[264],"can":[265],"forecast":[266],"their":[272],"occurrence":[273],"previously":[276],"challenging":[277],"predict,":[279],"achieving":[280],"up":[281],"42%":[283],"performance":[284],"improvement":[285],"over":[286],"CityOutlook+,":[287],"state-of-the-art":[289],"approach":[290],"forecasting.":[294]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}