{"id":"https://openalex.org/W7164552209","doi":"https://doi.org/10.48550/arxiv.2606.12657","title":"TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation","display_name":"TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164552209","doi":"https://doi.org/10.48550/arxiv.2606.12657"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.12657","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138528144","display_name":"Siyu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Siyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138523476","display_name":"Toan Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Toan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074800190","display_name":"Lingyi Zhao","orcid":"https://orcid.org/0000-0001-8544-3326"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Lingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083630319","display_name":"Khurram Shafique","orcid":"https://orcid.org/0000-0002-3834-324X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shafique, Khurram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138504429","display_name":"Li Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.5034999847412109,"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":0.5034999847412109,"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/T11106","display_name":"Data Management and Algorithms","score":0.3262999951839447,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.0203000009059906,"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/trajectory","display_name":"Trajectory","score":0.7753000259399414},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7475000023841858},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5683000087738037},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.46950000524520874},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4009000062942505},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.31189998984336853},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.29350000619888306},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.290800005197525}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7753000259399414},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7475000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7444000244140625},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5250999927520752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4925999939441681},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.46950000524520874},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C117035363","wikidata":"https://www.wikidata.org/wiki/Q3769299","display_name":"Human behavior","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.12657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7866711020469666,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Human":[0],"mobility":[1,71],"data":[2],"is":[3,16],"important":[4],"for":[5,69],"transportation,":[6],"urban":[7],"planning,":[8],"and":[9,19,56,89,99,121,145,151,163,170],"epidemic":[10],"control,":[11],"but":[12,39,51],"large-scale":[13,152],"trajectory":[14,24,72],"collection":[15],"often":[17],"costly":[18],"privacy-constrained,":[20],"motivating":[21],"realistic":[22],"synthetic":[23],"generation.":[25],"Existing":[26],"LLM-based":[27,122,171],"generators":[28],"typically":[29],"rely":[30],"on":[31,149],"either":[32],"prompt":[33],"engineering,":[34],"which":[35,47],"preserves":[36],"zero-shot":[37],"reasoning":[38],"lacks":[40],"fine-grained":[41],"spatiotemporal":[42,130,159],"grounding,":[43],"or":[44],"trajectory-level":[45],"fine-tuning,":[46],"improves":[48,158],"statistical":[49],"precision":[50],"incurs":[52],"substantial":[53],"computational":[54],"cost":[55],"may":[57],"weaken":[58],"general":[59],"reasoning.":[60],"We":[61],"propose":[62],"TrajGenAgent,":[63],"a":[64,79,100,108],"semantic-aware":[65],"hierarchical":[66],"LLM-agent":[67],"framework":[68,137],"human":[70],"generation":[73],"without":[74],"model":[75],"fine-tuning.":[76],"TrajGenAgent":[77,157],"uses":[78],"two-stage":[80],"orchestrator-worker":[81],"design:":[82],"an":[83,87,134],"LLM":[84],"first":[85],"synthesizes":[86],"individual-":[88],"weekday-conditioned":[90],"activity":[91,106],"chain":[92],"from":[93],"historical":[94],"evidence":[95],"via":[96],"in-context":[97],"learning,":[98],"deterministic":[101],"workflow":[102],"then":[103],"grounds":[104],"each":[105],"into":[107],"complete":[109],"visit":[110],"using":[111,138],"personalized":[112],"POI":[113],"retrieval,":[114],"distance-aware":[115],"location":[116],"selection,":[117],"kinematics-aware":[118],"travel-time":[119],"propagation,":[120],"duration":[123],"estimation.":[124],"To":[125],"evaluate":[126],"realism":[127,166],"beyond":[128],"aggregate":[129],"statistics,":[131],"we":[132],"introduce":[133],"anomaly-detection-based":[135],"evaluation":[136],"two":[139],"complementary":[140],"detectors":[141],"to":[142],"assess":[143],"behavioral":[144,165],"semantic":[146,161],"plausibility.":[147],"Experiments":[148],"benchmark":[150],"simulation":[153],"datasets":[154],"show":[155],"that":[156],"fidelity,":[160],"coherence,":[162],"individual-specific":[164],"over":[167],"representative":[168],"neural":[169],"baselines,":[172],"while":[173],"avoiding":[174],"parameter":[175],"updates.":[176]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-13T00:00:00"}
