{"id":"https://openalex.org/W4416707111","doi":"https://doi.org/10.1109/access.2025.3637173","title":"Efficient Travel Time Estimation Between Geographical Locations Using Derived Nonlinear Spatial Features for Offline VRP Optimization","display_name":"Efficient Travel Time Estimation Between Geographical Locations Using Derived Nonlinear Spatial Features for Offline VRP Optimization","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416707111","doi":"https://doi.org/10.1109/access.2025.3637173"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3637173","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637173","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3637173","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067217362","display_name":"Sanghwan Lee","orcid":"https://orcid.org/0000-0001-8431-0097"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghwan Lee","raw_affiliation_strings":["Department of Computer Science, Kookmin University, Seoul, South Korea","Department of Computer Science, Kookmin University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0001-8431-0097","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kookmin University, Seoul, South Korea","institution_ids":["https://openalex.org/I110273157"]},{"raw_affiliation_string":"Department of Computer Science, Kookmin University, Seoul, Korea","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109742136","display_name":"Jinsoo Moon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinsoo Moon","raw_affiliation_strings":["m2Cloud Inc., Seoul, South Korea","m2Cloud Inc, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"m2Cloud Inc., Seoul, South Korea","institution_ids":[]},{"raw_affiliation_string":"m2Cloud Inc, Seoul, Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34002544,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"201363","last_page":"201376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4408999979496002,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4408999979496002,"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/T11106","display_name":"Data Management and Algorithms","score":0.24979999661445618,"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/T10524","display_name":"Traffic control and management","score":0.12399999797344208,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/global-positioning-system","display_name":"Global Positioning System","score":0.5094000101089478},{"id":"https://openalex.org/keywords/geodesic","display_name":"Geodesic","score":0.44209998846054077},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4406000077724457},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43849998712539673},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4383000135421753},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4327999949455261},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4311999976634979},{"id":"https://openalex.org/keywords/vehicle-routing-problem","display_name":"Vehicle routing problem","score":0.3903999924659729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505000233650208},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5094000101089478},{"id":"https://openalex.org/C165818556","wikidata":"https://www.wikidata.org/wiki/Q213488","display_name":"Geodesic","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41909998655319214},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.3903999924659729},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3847000002861023},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37529999017715454},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2849999964237213},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2597000002861023}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3637173","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637173","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f5882f503ff342178e6f32ef90a42e55","is_oa":true,"landing_page_url":"https://doaj.org/article/f5882f503ff342178e6f32ef90a42e55","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 201363-201376 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3637173","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637173","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1949835739","display_name":null,"funder_award_id":"22183MFDS436","funder_id":"https://openalex.org/F4320322014","funder_display_name":"Ministry of Food and Drug Safety"},{"id":"https://openalex.org/G5815685210","display_name":null,"funder_award_id":"2022R1F1A1074672","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322014","display_name":"Ministry of Food and Drug Safety","ror":"https://ror.org/01f7dp456"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"Vehicle":[1],"Routing":[2],"Problems":[3],"(VRPs)":[4],"often":[5],"require":[6],"computing":[7],"travel":[8,53,70,128],"times":[9,54,145],"between":[10,55],"tens":[11],"of":[12,14,22,34,43,67,167,176,213,246,257,262],"thousands":[13,42],"geographic":[15],"locations":[16],"to":[17,41,109,180,185,242,270,285],"plan":[18],"routes":[19,78],"for":[20,105,133,205,300],"hundreds":[21,33],"vehicles.":[23],"For":[24],"example,":[25],"during":[26],"the":[27,65,165,177,211,237,247,254,291],"COVID-19":[28],"vaccine":[29],"distribution":[30],"in":[31],"Korea,":[32],"vehicles":[35],"were":[36],"tasked":[37],"with":[38,64,135,250],"delivering":[39],"vaccines":[40],"destinations":[44],"each":[45],"week.":[46],"Solving":[47],"such":[48,85,172],"VRPs":[49,107],"necessitates":[50],"estimating":[51],"pairwise":[52],"origin-destination":[56],"(OD)":[57],"pairs,":[58,288],"a":[59,259,295],"process":[60],"that":[61,290],"scales":[62],"quadratically":[63],"number":[66],"destinations.":[68],"Existing":[69],"time":[71,129],"estimation":[72,130],"methods":[73,99],"typically":[74],"focus":[75],"on":[76,81,225],"individual":[77],"and":[79,93,103,113,124,143,148,160,222,253,282,297],"rely":[80],"rich":[82],"contextual":[83],"data":[84],"as":[86,146,173,233],"GPS":[87],"trajectories,":[88],"road":[89],"types,":[90],"traffic":[91],"volume,":[92],"weather":[94],"conditions.":[95],"While":[96],"accurate,":[97],"these":[98,117],"are":[100],"computationally":[101],"expensive":[102],"impractical":[104],"large-scale":[106],"due":[108],"their":[110],"input":[111,170,190,223,235],"requirements":[112],"inefficiency.":[114],"To":[115],"address":[116],"challenges,":[118],"this":[119],"paper":[120],"proposes":[121],"an":[122,234],"efficient":[123],"scalable":[125],"deep":[126],"learning-based":[127],"approach":[131],"designed":[132],"datasets":[134],"limited":[136],"features.":[137],"Our":[138],"model":[139,226],"uses":[140],"only":[141],"geocoordinates":[142],"departure":[144],"inputs,":[147,179],"enhances":[149],"predictive":[150,280],"accuracy":[151,281],"by":[152],"incorporating":[153],"derived":[154,169],"nonlinear":[155],"spatial":[156],"features":[157],"specifically,":[158],"geodesic":[159,229],"Manhattan-style":[161],"distances.We":[162],"also":[163,209],"investigate":[164,210],"effect":[166],"other":[168],"features,":[171],"second-order":[174],"polynomials":[175],"basic":[178],"assess":[181],"whether":[182],"they":[183],"contribute":[184],"performance":[186],"improvement.":[187],"This":[188],"lightweight":[189],"representation":[191],"enables":[192],"rapid":[193],"<italic":[194],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[195,197,199],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">O</i>(<italic":[196],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">n</i><sup":[198],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>)":[200],"inference,":[201],"making":[202],"it":[203],"suitable":[204],"VRP-scale":[206],"scenarios.":[207],"We":[208],"effects":[212],"architectural":[214],"choices,":[215],"including":[216],"batch":[217],"normalization,":[218,224],"loss":[219,239],"function":[220],"variants,":[221],"performance.":[227],"When":[228],"distance":[230],"is":[231,294],"added":[232],"feature,":[236],"validation":[238],"(MSE)":[240],"drops":[241],"about":[243],"14":[244],"%":[245,269],"value":[248],"obtained":[249],"coordinates":[251],"alone,":[252],"cumulative":[255],"probability":[256],"achieving":[258],"relative":[260],"error":[261],"at":[263],"most":[264],"0.2":[265],"rises":[266],"from":[267],"25":[268],"more":[271],"than":[272],"50":[273],"%.":[274],"These":[275],"quantitative":[276],"gains":[277],"demonstrate":[278],"high":[279],"strong":[283],"generalization":[284],"unseen":[286],"OD":[287],"suggesting":[289],"proposed":[292],"framework":[293],"practical":[296],"effective":[298],"solution":[299],"large-scale,":[301],"real-world":[302],"VRP":[303],"applications.":[304]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-27T00:00:00"}