{"id":"https://openalex.org/W4312911571","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892652","title":"Time-aware Neural Trip Planning Reinforced by Human Mobility","display_name":"Time-aware Neural Trip Planning Reinforced by Human Mobility","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312911571","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892652"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892652","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082640575","display_name":"Linlang Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlang Jiang","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China","Business Intelligence Lab, Baidu Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101481194","display_name":"Jingbo Zhou","orcid":"https://orcid.org/0000-0003-2677-7021"},"institutions":[{"id":"https://openalex.org/I4400573203","display_name":"Nature Inspires Creativity Engineers Lab","ror":"https://ror.org/02bczqy30","country_code":null,"type":"facility","lineage":["https://openalex.org/I201841394","https://openalex.org/I4400573203"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingbo Zhou","raw_affiliation_strings":["Baidu Research,Business Intelligence Lab","Business Intelligence Lab, Baidu Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research,Business Intelligence Lab","institution_ids":["https://openalex.org/I4400573203","https://openalex.org/I98301712"]},{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025292786","display_name":"Tong Xu","orcid":"https://orcid.org/0000-0003-4246-5386"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Xu","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324137","display_name":"Yanyan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4400573203","display_name":"Nature Inspires Creativity Engineers Lab","ror":"https://ror.org/02bczqy30","country_code":null,"type":"facility","lineage":["https://openalex.org/I201841394","https://openalex.org/I4400573203"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Li","raw_affiliation_strings":["Baidu Research,Business Intelligence Lab","Business Intelligence Lab, Baidu Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research,Business Intelligence Lab","institution_ids":["https://openalex.org/I4400573203","https://openalex.org/I98301712"]},{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353673","display_name":"Hao Chen","orcid":"https://orcid.org/0009-0001-6480-7976"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066063885","display_name":"Dejing Dou","orcid":"https://orcid.org/0000-0001-7561-1672"},"institutions":[{"id":"https://openalex.org/I4400573203","display_name":"Nature Inspires Creativity Engineers Lab","ror":"https://ror.org/02bczqy30","country_code":null,"type":"facility","lineage":["https://openalex.org/I201841394","https://openalex.org/I4400573203"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dejing Dou","raw_affiliation_strings":["Baidu Research,Business Intelligence Lab","Business Intelligence Lab, Baidu Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research,Business Intelligence Lab","institution_ids":["https://openalex.org/I4400573203","https://openalex.org/I98301712"]},{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3691,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54435428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"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":0.9948999881744385,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7937180995941162},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.6690223813056946},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.62812340259552},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6160603165626526},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5599130988121033},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5504480600357056},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4838098883628845},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4435020387172699},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4370706379413605},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.41836851835250854},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.13194140791893005},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1302613615989685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7937180995941162},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.6690223813056946},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.62812340259552},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6160603165626526},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5599130988121033},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5504480600357056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4838098883628845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4435020387172699},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4370706379413605},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.41836851835250854},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.13194140791893005},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1302613615989685},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892652","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1544092585","https://openalex.org/W2003281104","https://openalex.org/W2010070294","https://openalex.org/W2072699031","https://openalex.org/W2076337359","https://openalex.org/W2082764616","https://openalex.org/W2119717200","https://openalex.org/W2123517900","https://openalex.org/W2157331557","https://openalex.org/W2236009467","https://openalex.org/W2508716390","https://openalex.org/W2581637843","https://openalex.org/W2741462375","https://openalex.org/W2770857071","https://openalex.org/W2798326382","https://openalex.org/W2951846985","https://openalex.org/W2952118120","https://openalex.org/W2952332632","https://openalex.org/W2964268978","https://openalex.org/W2997281057","https://openalex.org/W2997739225","https://openalex.org/W3008578452","https://openalex.org/W3078516413","https://openalex.org/W3080637781","https://openalex.org/W3080932802","https://openalex.org/W3105196786","https://openalex.org/W3119100156","https://openalex.org/W3130684690","https://openalex.org/W3134124738","https://openalex.org/W3198850291","https://openalex.org/W4214717370","https://openalex.org/W4294643831","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6621543089","https://openalex.org/W6632527360","https://openalex.org/W6689584527","https://openalex.org/W6725207838","https://openalex.org/W6730742100","https://openalex.org/W6739901393","https://openalex.org/W6745560452","https://openalex.org/W6758687306","https://openalex.org/W6782199421"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2057775761","https://openalex.org/W1608433645","https://openalex.org/W2768698792"],"abstract_inverted_index":{"Trip":[0,137],"planning,":[1],"which":[2,197,277],"targets":[3],"at":[4],"planning":[5,34],"a":[6,70,85,111,134,151,173,195,249],"trip":[7,33,47,71,115,155,188,240],"consisting":[8],"of":[9,13,32,147,274],"several":[10],"ordered":[11],"Points":[12],"Interest":[14],"(POIs)":[15],"under":[16,167],"user-provided":[17],"constraints,":[18],"has":[19],"long":[20],"been":[21],"treated":[22],"as":[23],"an":[24,186,229,259],"important":[25],"application":[26],"for":[27,258],"location-based":[28],"services.":[29],"The":[30],"goal":[31],"is":[35,50,224],"to":[36,52,65,68,82,92,141,179,184,208,212,217,237,253],"maximize":[37],"the":[38,41,45,57,94,100,105,123,143,160,182,199,214,219,222,234,239,243,256,270,284],"chance":[39],"that":[40,157,279],"users":[42],"will":[43],"follow":[44],"planned":[46],"while":[48],"it":[49],"difficult":[51],"directly":[53,180],"quantify":[54],"and":[55,164,242,272],"optimize":[56,181,213],"chance.":[58],"Conventional":[59],"methods":[60,89,119],"either":[61],"leverage":[62],"statistical":[63],"analysis":[64],"rank":[66],"POIs":[67,163],"form":[69],"or":[72],"generate":[73,165],"trips":[74,166,201,204],"following":[75],"pre-defined":[76],"objectives":[77],"based":[78],"on":[79,265],"constraint":[80,126],"programming":[81],"bypass":[83],"such":[84],"problem.":[86],"However,":[87],"these":[88,118],"may":[90],"fail":[91],"reflect":[93],"complex":[95],"latent":[96],"patterns":[97],"hidden":[98],"in":[99],"human":[101],"mobility":[102],"data.":[103],"On":[104],"other":[106],"hand,":[107],"though":[108],"there":[109],"are":[110],"few":[112],"deep":[113],"learning-based":[114],"recommendation":[116],"methods,":[117],"still":[120],"cannot":[121],"handle":[122],"time":[124],"budget":[125],"so":[127],"far.":[128],"To":[129],"this":[130,191],"end,":[131],"we":[132,149,171,193,227,247],"propose":[133,172],"TIme-aware":[135],"Neural":[136],"Planning":[138],"(TINT)":[139],"framework":[140],"tackle":[142],"above":[144],"challenges.":[145],"First":[146],"all,":[148],"devise":[150,248],"novel":[152,250],"attention-based":[153],"encoder-decoder":[154],"generator":[156,241],"can":[158],"learn":[159],"correlations":[161],"among":[162],"given":[168],"constraints.":[169],"Then,":[170],"specially-designed":[174],"reinforcement":[175],"learning":[176,231],"(RL)":[177],"paradigm":[178,236],"objective":[183],"obtain":[185],"optimal":[187],"generator.":[189,215],"For":[190],"purpose,":[192],"introduce":[194],"discriminator,":[196],"distinguishes":[198],"generated":[200],"from":[202,221],"real-life":[203],"taken":[205],"by":[206],"users,":[207],"provide":[209],"reward":[210],"signals":[211],"Subsequently,":[216],"ensure":[218],"feedback":[220],"discriminator":[223,244],"always":[225],"instructive,":[226],"integrate":[228],"adversarial":[230],"strategy":[232],"into":[233],"RL":[235],"update":[238],"alternately.":[245],"Moreover,":[246],"pre-training":[251],"schema":[252],"speed":[254],"up":[255],"convergence":[257],"efficient":[260],"training":[261],"process.":[262],"Extensive":[263],"experiments":[264],"four":[266],"real-world":[267],"datasets":[268],"validate":[269],"effectiveness":[271],"efficiency":[273],"our":[275],"framework,":[276],"shows":[278],"TINT":[280],"could":[281],"remarkably":[282],"outperform":[283],"state-of-the-art":[285],"baselines":[286],"within":[287],"short":[288],"response":[289],"time.":[290]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}