{"id":"https://openalex.org/W4391770318","doi":"https://doi.org/10.1109/itsc57777.2023.10422374","title":"Deep Learning Approach to Logistics Trips Generation: Enhancing Pseudo People Flow with Agent-Based Modeling","display_name":"Deep Learning Approach to Logistics Trips Generation: Enhancing Pseudo People Flow with Agent-Based Modeling","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391770318","doi":"https://doi.org/10.1109/itsc57777.2023.10422374"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","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/A5101466665","display_name":"Kunyi Zhang","orcid":"https://orcid.org/0000-0002-5915-4018"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kunyi Zhang","raw_affiliation_strings":["School of Engineering, the University of Tokyo,Bunkyo-Ku, Tokyo,Japan,113-0032"],"affiliations":[{"raw_affiliation_string":"School of Engineering, the University of Tokyo,Bunkyo-Ku, Tokyo,Japan,113-0032","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035214173","display_name":"Yanbo Pang","orcid":"https://orcid.org/0000-0003-0599-9995"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yanbo Pang","raw_affiliation_strings":["University of Tokyo,Center for Spatial Information Science,Meguro-Ku, Tokyo,Japan,153-8505"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Center for Spatial Information Science,Meguro-Ku, Tokyo,Japan,153-8505","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024842056","display_name":"Yoshihide Sekimoto","orcid":"https://orcid.org/0000-0003-0305-7056"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihide Sekimoto","raw_affiliation_strings":["School of Engineering, the University of Tokyo,Bunkyo-Ku, Tokyo,Japan,113-0032","Center for Spatial Information Science, University of Tokyo, Meguro-Ku, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"School of Engineering, the University of Tokyo,Bunkyo-Ku, Tokyo,Japan,113-0032","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Center for Spatial Information Science, University of Tokyo, Meguro-Ku, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974","https://openalex.org/I161296585"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101466665"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.367,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.74283738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2535","last_page":"2542"},"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":0.991599977016449,"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":0.991599977016449,"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":0.9775999784469604,"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"}},{"id":"https://openalex.org/T12306","display_name":"Urban and Freight Transport Logistics","score":0.955299973487854,"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/trips-architecture","display_name":"TRIPS architecture","score":0.7654436826705933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5953101515769958},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4545818865299225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36501604318618774},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.3259183168411255},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.06751763820648193}],"concepts":[{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.7654436826705933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5953101515769958},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4545818865299225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36501604318618774},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3259183168411255},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.06751763820648193},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2781998419","https://openalex.org/W2793820729","https://openalex.org/W2800189317","https://openalex.org/W2885334923","https://openalex.org/W2900207796","https://openalex.org/W2922254780","https://openalex.org/W3034077089","https://openalex.org/W3039274539","https://openalex.org/W3089251088","https://openalex.org/W3096892513","https://openalex.org/W3153516896","https://openalex.org/W3175979325","https://openalex.org/W3179398129","https://openalex.org/W3191705065","https://openalex.org/W3214685769","https://openalex.org/W4205918026","https://openalex.org/W4210245510","https://openalex.org/W4214647333","https://openalex.org/W4225408533","https://openalex.org/W4320024309"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2807758032","https://openalex.org/W4224254130","https://openalex.org/W2152103536","https://openalex.org/W3048948123","https://openalex.org/W2107643127","https://openalex.org/W413879896","https://openalex.org/W1983530038","https://openalex.org/W2972374246","https://openalex.org/W4293174494"],"abstract_inverted_index":{"This":[0],"study":[1],"developed":[2],"a":[3],"new":[4],"trip":[5],"generation":[6],"strategy":[7],"to":[8,46,56],"simulate":[9,60],"the":[10,63,73,76,83,90,94,103],"trajectories":[11,61],"of":[12,30,37,72,78,97],"logistics":[13,23,35,68,98],"trips.":[14,88],"An":[15],"agent-based":[16],"modeling":[17],"framework":[18],"was":[19,44,54],"employed.":[20],"We":[21],"considered":[22],"drivers":[24],"as":[25,33],"agents":[26],"and":[27,59,82,93],"define":[28],"Points":[29],"Interest":[31],"(POI)":[32],"potential":[34],"destinations":[36],"agents.":[38],"A":[39],"deep":[40],"learning-based":[41],"gravity":[42],"model":[43],"adopted":[45],"generate":[47],"destination":[48],"choice":[49],"behaviors.":[50],"Time-spatial":[51],"interpolation":[52],"method":[53],"applied":[55],"agent":[57],"trips":[58,81],"on":[62],"road":[64],"network.":[65],"The":[66],"synthetic":[67],"volume":[69,99],"supplemented":[70],"74.92%":[71],"disparity":[74],"between":[75],"volumes":[77],"baseline":[79],"truck":[80],"pseudo-people":[84],"flow":[85],"(PFlow)":[86],"business":[87],"Furthermore,":[89],"temporal":[91],"distribution":[92,96],"cross-section":[95],"align":[100],"closely":[101],"with":[102],"traffic":[104],"census":[105],"data.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
