{"id":"https://openalex.org/W4225868478","doi":"https://doi.org/10.1109/tmc.2022.3168553","title":"Time-Dependent Visiting Trip Planning with Crowd Density Prediction Based on Internet of Things Localization","display_name":"Time-Dependent Visiting Trip Planning with Crowd Density Prediction Based on Internet of Things Localization","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4225868478","doi":"https://doi.org/10.1109/tmc.2022.3168553"},"language":"en","primary_location":{"id":"doi:10.1109/tmc.2022.3168553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2022.3168553","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Mobile Computing","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/A5033507661","display_name":"Lien\u2010Wu Chen","orcid":"https://orcid.org/0000-0001-5453-9675"},"institutions":[{"id":"https://openalex.org/I4880106","display_name":"Feng Chia University","ror":"https://ror.org/05vhczg54","country_code":"TW","type":"education","lineage":["https://openalex.org/I4880106"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Lien-Wu Chen","raw_affiliation_strings":["Information Engineering and Computer Science, Feng Chia University, 34902 Taichung, Taichung, Taiwan, 40724"],"affiliations":[{"raw_affiliation_string":"Information Engineering and Computer Science, Feng Chia University, 34902 Taichung, Taichung, Taiwan, 40724","institution_ids":["https://openalex.org/I4880106"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091438128","display_name":"Chia-Chun Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I4880106","display_name":"Feng Chia University","ror":"https://ror.org/05vhczg54","country_code":"TW","type":"education","lineage":["https://openalex.org/I4880106"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Chun Weng","raw_affiliation_strings":["Information Engineering and Computer Science, Feng Chia University, 34902 Taichung, Taichung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Information Engineering and Computer Science, Feng Chia University, 34902 Taichung, Taichung, Taiwan","institution_ids":["https://openalex.org/I4880106"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5033507661"],"corresponding_institution_ids":["https://openalex.org/I4880106"],"apc_list":null,"apc_paid":null,"fwci":1.5556,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82723062,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7989951372146606},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.6095897555351257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5266323685646057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48129716515541077},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.4490998089313507},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.444401353597641},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3457845449447632},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10046142339706421},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08060187101364136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7989951372146606},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.6095897555351257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5266323685646057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48129716515541077},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.4490998089313507},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.444401353597641},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3457845449447632},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10046142339706421},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08060187101364136},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2022.3168553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2022.3168553","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6499999761581421}],"awards":[{"id":"https://openalex.org/G7769397874","display_name":null,"funder_award_id":"109-2221-E-035-062-MY3","funder_id":"https://openalex.org/F4320309618","funder_display_name":"Ministry of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1587650745","https://openalex.org/W1747229408","https://openalex.org/W1829285201","https://openalex.org/W1986003024","https://openalex.org/W2017056417","https://openalex.org/W2028139390","https://openalex.org/W2077638917","https://openalex.org/W2121789540","https://openalex.org/W2159767430","https://openalex.org/W2185099718","https://openalex.org/W2278051381","https://openalex.org/W2319598986","https://openalex.org/W2536063825","https://openalex.org/W2583260348","https://openalex.org/W2603651050","https://openalex.org/W2604662095","https://openalex.org/W2735633969","https://openalex.org/W2766901904","https://openalex.org/W2789305192","https://openalex.org/W2794417171","https://openalex.org/W2798056406","https://openalex.org/W2802344988","https://openalex.org/W2807536558","https://openalex.org/W2807553909","https://openalex.org/W2863526491","https://openalex.org/W2885264287","https://openalex.org/W2890600481","https://openalex.org/W2946391111","https://openalex.org/W2963446712","https://openalex.org/W2976882027","https://openalex.org/W2996063062","https://openalex.org/W3000197461","https://openalex.org/W3023849923","https://openalex.org/W3099019051","https://openalex.org/W6634935877","https://openalex.org/W6750135745"],"related_works":["https://openalex.org/W4245926026","https://openalex.org/W2586548817","https://openalex.org/W4311097251","https://openalex.org/W2625093826","https://openalex.org/W2950174689","https://openalex.org/W4200598720","https://openalex.org/W2921026492","https://openalex.org/W4361251261","https://openalex.org/W3031181660","https://openalex.org/W4285201139"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,36,45,122],"time-dependent":[4,46,119],"visiting":[5,15,47,124,143,207],"trip":[6,48],"planning":[7,49,74,120],"(TVTP)":[8],"framework":[9,33,109,179],"to":[10,66,79,87,99,154,165],"find":[11],"the":[12,19,52,72,107,141,149,167,187,199,204],"most":[13],"efficient":[14],"order":[16,144],"and":[17,44,138,145,172,183],"plan":[18,140],"fastest":[20],"moving":[21,90,131,152,201],"paths":[22,147],"based":[23,39,92,133],"on":[24,93,134],"Internet":[25],"of":[26,35,102,169,191],"Things":[27],"(IoT)":[28],"localization.":[29],"The":[30,159],"proposed":[31],"TVTP":[32],"consists":[34],"deep":[37,112],"learning":[38,113],"crowd":[40,96,115,136,161,189],"density":[41,116,190],"prediction":[42,54,69,117],"model":[43],"algorithm.":[50],"In":[51,71],"developed":[53],"model,":[55],"densely":[56],"connected":[57],"convolutional":[58],"networks":[59],"are":[60,77],"explored":[61],"with":[62,118,148],"spatiotemporal":[63],"data":[64],"fusion":[65],"further":[67],"reduce":[68,198],"errors.":[70],"designed":[73],"algorithm,":[75],"visitors":[76],"guided":[78],"multiple":[80],"target":[81,157],"places":[82],"at":[83],"feasible":[84],"time":[85,91,153,202],"points":[86],"minimize":[88],"total":[89,151,200],"predicted":[94,135],"future":[95,130,188],"densities.":[97],"According":[98],"our":[100,178],"review":[101],"relevant":[103],"research,":[104],"this":[105],"is":[106,163],"first":[108],"that":[110,177],"integrates":[111],"for":[114,121],"multi-target":[123,206],"trip,":[125],"which":[126],"can":[127,184],"precisely":[128],"estimate":[129],"times":[132],"densities":[137],"efficiently":[139],"optimal":[142],"guiding":[146],"shortest":[150],"visit":[155],"all":[156],"places.":[158],"open":[160],"dataset":[162],"adopted":[164],"evaluate":[166],"performance":[168],"existing":[170,181],"works":[171],"TVTP.":[173],"Experimental":[174],"results":[175],"show":[176],"outperforms":[180],"methods":[182],"accurately":[185],"predict":[186],"indoor":[192],"people":[193],"as":[194,196],"well":[195],"significantly":[197],"in":[203],"planned":[205],"trip.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}