{"id":"https://openalex.org/W2784246916","doi":"https://doi.org/10.1109/bigdata.2017.8258322","title":"Practical approach to evacuation planning via network flow and deep learning","display_name":"Practical approach to evacuation planning via network flow and deep learning","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2784246916","doi":"https://doi.org/10.1109/bigdata.2017.8258322","mag":"2784246916"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5105269463","display_name":"Akira Tanaka","orcid":"https://orcid.org/0000-0003-0055-8234"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Akira Tanaka","raw_affiliation_strings":["Graduate School of Mathematics, Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Mathematics, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007525787","display_name":"Nozomi Hata","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nozomi Hata","raw_affiliation_strings":["Graduate School of Mathematics, Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Mathematics, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032235409","display_name":"Nariaki Tateiwa","orcid":"https://orcid.org/0000-0001-7161-6687"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nariaki Tateiwa","raw_affiliation_strings":["Graduate School of Mathematics, Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Mathematics, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067662454","display_name":"Katsuki Fujisawa","orcid":"https://orcid.org/0000-0001-8549-641X"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Katsuki Fujisawa","raw_affiliation_strings":["Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5105269463"],"corresponding_institution_ids":["https://openalex.org/I135598925"],"apc_list":null,"apc_paid":null,"fwci":1.1634,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80710854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"35","issue":null,"first_page":"3368","last_page":"3377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9998999834060669,"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":0.9998999834060669,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9973000288009644,"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":0.9872000217437744,"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":0.7575322389602661},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6089120507240295},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5326964855194092},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5155805945396423},{"id":"https://openalex.org/keywords/flow-network","display_name":"Flow network","score":0.5111034512519836},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.4851582646369934},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4584805965423584},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4525541365146637},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4400835931301117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42120957374572754},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.4134209454059601},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.26505836844444275},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09690123796463013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7575322389602661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6089120507240295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5326964855194092},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5155805945396423},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.5111034512519836},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.4851582646369934},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4584805965423584},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4525541365146637},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4400835931301117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42120957374572754},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.4134209454059601},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26505836844444275},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09690123796463013},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata.2017.8258322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:t2r2.star.titech.ac.jp:50704101","is_oa":false,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100919349","pdf_url":null,"source":{"id":"https://openalex.org/S4377196385","display_name":"Tokyo Tech Research Repository (Tokyo Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114531698","host_organization_name":"Tokyo Institute of Technology","host_organization_lineage":["https://openalex.org/I114531698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6399999856948853}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W764651262","https://openalex.org/W1968457313","https://openalex.org/W1976948919","https://openalex.org/W2007637265","https://openalex.org/W2047907098","https://openalex.org/W2059287750","https://openalex.org/W2112796928","https://openalex.org/W2117731089","https://openalex.org/W2130406232","https://openalex.org/W2146502635","https://openalex.org/W2147800946","https://openalex.org/W2163605009","https://openalex.org/W2184356634","https://openalex.org/W2194775991","https://openalex.org/W2469134594","https://openalex.org/W2519887557","https://openalex.org/W2546302380","https://openalex.org/W2568149362","https://openalex.org/W2604909019","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964321699","https://openalex.org/W4255349897","https://openalex.org/W6664948060","https://openalex.org/W6681435938","https://openalex.org/W6684191040","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6764350350"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2065514426"],"abstract_inverted_index":{"In":[0,19,84],"this":[1],"paper,":[2],"we":[3,125,149,163],"propose":[4],"a":[5,70,105,112,133,172,180],"practical":[6,113],"approach":[7,208],"to":[8,56,61,154],"evacuation":[9,89,157,198],"planning":[10],"by":[11,182,203,261],"utilizing":[12,272],"network":[13,135],"flow":[14,130],"and":[15,34,50,69,77,108,140,193,230],"deep":[16,173,277],"learning":[17],"algorithms.":[18],"recent":[20],"years,":[21],"large":[22],"amounts":[23],"of":[24,64,87,171,186,218,256,268,271],"data":[25,36,170],"are":[26],"rapidly":[27],"being":[28],"stored":[29],"in":[30,66,123,190,194,242],"the":[31,62,74,85,92,120,128,161,165,184,187,197,204,254,257,266,269,273],"cloud":[32],"system,":[33],"effective":[35],"utilization":[37],"for":[38,73,143,225,276],"solving":[39,67],"real-world":[40,82],"problems":[41,68,131],"is":[42,109,119,251],"required":[43],"more":[44],"than":[45],"ever.":[46],"Hierarchical":[47],"Data":[48],"Analysis":[49],"Optimization":[51],"System":[52],"(HDAOS)":[53],"enables":[54],"us":[55],"select":[57],"appropriate":[58],"algorithms":[59],"according":[60],"degree":[63],"difficulty":[65],"given":[71],"time":[72,103,200],"decision-making":[75],"process,":[76],"such":[78],"selection":[79],"helps":[80],"address":[81],"problems.":[83],"field":[86],"emergency":[88],"planning,":[90],"however,":[91],"Lexicographically":[93],"Quickest":[94],"Flow":[95],"(LQF)":[96],"algorithm":[97,189,275],"has":[98,238],"an":[99,145,156,214],"extremely":[100],"long":[101],"computation":[102],"on":[104,132],"large-scale":[106,134],"network,":[107],"therefore":[110],"not":[111],"solution.":[114],"For":[115],"Osaka":[116],"city,":[117],"which":[118,237,264],"second-largest":[121],"city":[122],"Japan,":[124],"must":[126],"solve":[127,160],"maximum":[129],"with":[136,228],"over":[137],"8.3M":[138],"nodes":[139],"32.8M":[141],"arcs":[142],"obtaining":[144],"optimal":[146,166],"plan.":[147,158],"Consequently,":[148],"can":[150],"feed":[151],"back":[152],"nothing":[153],"make":[155],"To":[159],"problem,":[162],"utilize":[164],"solution":[167],"as":[168,234],"training":[169],"Convolutional":[174],"Neural":[175],"Network":[176],"(CNN).":[177],"We":[178,221],"train":[179],"CNN":[181,229],"using":[183],"results":[185],"LQF":[188,227,250,274],"normal":[191],"time,":[192],"emergencies":[195],"predict":[196],"completion":[199],"(ECT)":[201],"immediately":[202],"well-learned":[205],"CNN.":[206],"Our":[207],"provides":[209],"almost":[210],"precise":[211],"ECT,":[212],"achieving":[213],"average":[215],"regression":[216],"error":[217],"about":[219],"2%.":[220],"provide":[222],"several":[223],"techniques":[224],"combining":[226],"addressing":[231],"numerous":[232],"movements":[233],"CNN's":[235],"input,":[236],"rarely":[239],"been":[240],"considered":[241],"previous":[243],"studies.":[244],"Hodge":[245],"decomposition":[246],"also":[247],"demonstrates":[248],"that":[249],"efficient":[252],"from":[253],"standpoint":[255],"total":[258],"distance":[259],"traveled":[260],"all":[262],"evacuees,":[263],"reinforces":[265],"validity":[267],"method":[270],"learning.":[278]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
