{"id":"https://openalex.org/W3194471736","doi":"https://doi.org/10.1145/3469085","title":"DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features","display_name":"DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3194471736","doi":"https://doi.org/10.1145/3469085","mag":"3194471736"},"language":"en","primary_location":{"id":"doi:10.1145/3469085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3469085","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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/A5044231605","display_name":"Amin Vahedian Khezerlou","orcid":"https://orcid.org/0000-0003-3413-3744"},"institutions":[{"id":"https://openalex.org/I183533211","display_name":"University of Wisconsin\u2013Whitewater","ror":"https://ror.org/049hrzs50","country_code":"US","type":"education","lineage":["https://openalex.org/I183533211"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amin Vahedian Khezerlou","raw_affiliation_strings":["University of Wisconsin-Whitewater, Whitewater, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Whitewater, Whitewater, WI, USA","institution_ids":["https://openalex.org/I183533211"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198510","display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["The University of Iowa, Iowa City, IA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa, Iowa City, IA, USA","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370278","display_name":"Xinyi Li","orcid":"https://orcid.org/0000-0002-4346-066X"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyi Li","raw_affiliation_strings":["The University of Iowa, Iowa City, IA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa, Iowa City, IA, USA","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031033430","display_name":"W. Nick Street","orcid":"https://orcid.org/0000-0002-1632-5905"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"W. Nick Street","raw_affiliation_strings":["The University of Iowa, Iowa City, IA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Iowa, Iowa City, IA, USA","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100630059","display_name":"Yanhua Li","orcid":"https://orcid.org/0000-0001-8972-503X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanhua Li","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5188,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76123146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"12","issue":"4","first_page":"1","last_page":"25"},"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":0.9994000196456909,"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":0.9994000196456909,"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":0.9987999796867371,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biological-dispersal","display_name":"Biological dispersal","score":0.8560049533843994},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7719806432723999},{"id":"https://openalex.org/keywords/law-enforcement","display_name":"Law enforcement","score":0.5950215458869934},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5575538277626038},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4236559569835663},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.32778364419937134},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.09067890048027039}],"concepts":[{"id":"https://openalex.org/C47559259","wikidata":"https://www.wikidata.org/wiki/Q778143","display_name":"Biological dispersal","level":3,"score":0.8560049533843994},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7719806432723999},{"id":"https://openalex.org/C2780262971","wikidata":"https://www.wikidata.org/wiki/Q44554","display_name":"Law enforcement","level":2,"score":0.5950215458869934},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5575538277626038},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4236559569835663},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32778364419937134},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.09067890048027039},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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":1,"locations":[{"id":"doi:10.1145/3469085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3469085","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7599999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1973366456","https://openalex.org/W1988580225","https://openalex.org/W1994096159","https://openalex.org/W2002151188","https://openalex.org/W2029767187","https://openalex.org/W2038943544","https://openalex.org/W2051530877","https://openalex.org/W2064675550","https://openalex.org/W2075158265","https://openalex.org/W2089036804","https://openalex.org/W2102201884","https://openalex.org/W2108400301","https://openalex.org/W2110676972","https://openalex.org/W2119721623","https://openalex.org/W2124499489","https://openalex.org/W2145425472","https://openalex.org/W2165874743","https://openalex.org/W2294628100","https://openalex.org/W2513610673","https://openalex.org/W2528040708","https://openalex.org/W2533098469","https://openalex.org/W2565239705","https://openalex.org/W2565638783","https://openalex.org/W2584174354","https://openalex.org/W2739060064","https://openalex.org/W2743969099","https://openalex.org/W2766311542","https://openalex.org/W2775800859","https://openalex.org/W2904120175","https://openalex.org/W2950418200","https://openalex.org/W2971318403","https://openalex.org/W4288278954"],"related_works":["https://openalex.org/W1530101107","https://openalex.org/W2375382787","https://openalex.org/W228809297","https://openalex.org/W1586782953","https://openalex.org/W2357007603","https://openalex.org/W3098582471","https://openalex.org/W2369105145","https://openalex.org/W2554121427","https://openalex.org/W1560330117","https://openalex.org/W2954280381"],"abstract_inverted_index":{"Urban":[0],"dispersal":[1,64,137,169,231],"events":[2,65,138,170,361],"occur":[3],"when":[4],"an":[5,12,37,82,368],"unexpectedly":[6],"large":[7],"number":[8],"of":[9,19,40,99,120,143,179,195,229,325,370],"people":[10],"leave":[11],"area":[13],"in":[14,153,161,187,216,218,233,362],"a":[15,158,191,200,205,244,261,309],"relatively":[16],"short":[17],"period":[18],"time.":[20,188,273],"It":[21,372],"is":[22,66,241,373],"beneficial":[23,68],"for":[24,280,384],"the":[25,49,121,151,154,176,210,219,227,230,234,252,287,313,346,363,379],"city":[26,33],"authorities,":[27],"such":[28,41,56,92,106],"as":[29,43,57,75,93],"law":[30],"enforcement":[31],"and":[32,52,60,85,97,128,146,181,198,317,320,343,378],"management,":[34],"to":[35,69,81,125,225,243,289,311,353],"have":[36,286],"advance":[38],"knowledge":[39],"events,":[42],"it":[44,76],"can":[45,171,256,359],"help":[46,78],"them":[47,79,186],"mitigate":[48],"safety":[50],"risks":[51],"handle":[53],"important":[54],"challenges":[55],"managing":[58],"traffic,":[59],"so":[61],"forth.":[62],"Predicting":[63],"also":[67,278],"Taxi":[70],"drivers":[71],"and/or":[72],"ride-sharing":[73],"services,":[74],"will":[77],"respond":[80],"unexpected":[83],"demand":[84,116,122,386],"gain":[86],"competitive":[87],"advantage.":[88],"Large":[89],"urban":[90],"datasets":[91],"detailed":[94],"trip":[95],"records":[96],"point":[98,215],"interest":[100],"(":[101],"POI":[102],")":[103],"data":[104],"make":[105],"predictions":[107],"achievable.":[108],"The":[109,118],"related":[110],"literature":[111],"mainly":[112],"focused":[113],"on":[114,249,296,328,345],"taxi":[115,349,385],"prediction.":[117,387],"pattern":[119],"was":[123],"assumed":[124],"be":[126,172],"repetitive":[127],"proposed":[129,157,190,199],"methods":[130,152],"aimed":[131],"at":[132,213,260],"capturing":[133],"those":[134,144],"patterns.":[135,299,331],"However,":[136,239],"are,":[139,147],"by":[140,150,174,307],"definition,":[141],"violations":[142],"patterns":[145,178,255,271,276,319],"understandably,":[148],"missed":[149],"literature.":[155],"We":[156,166,189,221,332,338],"different":[159,269,279,281,293],"approach":[160],"our":[162,334],"prior":[163],"work":[164],"[32].":[165],"showed":[167],"that":[168,184,223,357],"predicted":[173,209],"learning":[175,207,382],"complex":[177],"arrival":[180],"other":[182],"features":[183],"precede":[185],"survival":[192,211],"analysis":[193],"formulation":[194],"this":[196,301],"problem":[197],"two-stage":[201],"framework":[202],"(DILSA),":[203],"where":[204],"deep":[206,381],"model":[208],"function":[212],"each":[214],"time":[217,228,259],"future.":[220],"used":[222],"prediction":[224],"determine":[226],"event":[232],"future,":[235],"or":[236],"its":[237],"non-occurrence.":[238],"DILSA":[240,264,283,377],"subject":[242],"few":[245],"limitations.":[246],"First,":[247],"based":[248,295,327],"evidence":[250],"from":[251,351],"data,":[253],"mobility":[254,270,275,298,318,330],"vary":[257],"through":[258,272],"given":[262],"location.":[263],"does":[265,284],"not":[266,285],"distinguish":[267,291],"between":[268,292,315],"Second,":[274],"are":[277],"locations.":[282],"capability":[288],"directly":[290],"locations":[294,326],"their":[297,329],"In":[300],"article,":[302],"we":[303,321],"address":[304],"these":[305],"limitations":[306],"proposing":[308],"method":[310,336],"capture":[312],"interaction":[314],"POIs":[316],"create":[322],"vector":[323],"representations":[324],"call":[333],"new":[335],"DILSA+.":[337],"conduct":[339],"extensive":[340],"case":[341],"studies":[342],"experiments":[344],"NYC":[347],"Yellow":[348],"dataset":[350],"2014":[352],"2016.":[354],"Results":[355],"show":[356],"DILSA+":[358],"predict":[360],"next":[364],"5":[365],"hours":[366],"with":[367],"F1-score":[369],"0.66.":[371],"significantly":[374],"better":[375],"than":[376],"state-of-the-art":[380],"approaches":[383]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
