{"id":"https://openalex.org/W7155054682","doi":"https://doi.org/10.1109/synasc69064.2025.00038","title":"Zonal Estimation Method for Offline Travel Time Prediction in Sparse Data Environments","display_name":"Zonal Estimation Method for Offline Travel Time Prediction in Sparse Data Environments","publication_year":2025,"publication_date":"2025-09-22","ids":{"openalex":"https://openalex.org/W7155054682","doi":"https://doi.org/10.1109/synasc69064.2025.00038"},"language":null,"primary_location":{"id":"doi:10.1109/synasc69064.2025.00038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/synasc69064.2025.00038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 27th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","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/A5126974316","display_name":"Cezar Tudor","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108695","display_name":"Gheorghe Asachi Technical University of Ia\u0219i","ror":"https://ror.org/014zxnz40","country_code":"RO","type":"education","lineage":["https://openalex.org/I4210108695"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Cezar Tudor","raw_affiliation_strings":["Gheorghe Asachi Technical University, Nexus Media SRL,Iasi,Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gheorghe Asachi Technical University, Nexus Media SRL,Iasi,Romania","institution_ids":["https://openalex.org/I4210108695"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057284368","display_name":"Cristian Simionescu","orcid":"https://orcid.org/0000-0003-1274-0914"},"institutions":[{"id":"https://openalex.org/I138164181","display_name":"Alexandru Ioan Cuza University","ror":"https://ror.org/022kvet57","country_code":"RO","type":"education","lineage":["https://openalex.org/I138164181"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Cristian Simionescu","raw_affiliation_strings":["Alexandru Ioan Cuza University, Nexus Media SRL,Iasi,Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexandru Ioan Cuza University, Nexus Media SRL,Iasi,Romania","institution_ids":["https://openalex.org/I138164181"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5126974316"],"corresponding_institution_ids":["https://openalex.org/I4210108695"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69265944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"228","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.975600004196167,"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":0.975600004196167,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.006399999838322401,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.00419999985024333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.41359999775886536},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3330000042915344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.31619998812675476},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.3140999972820282},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.31200000643730164},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3068999946117401}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5676000118255615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4171000123023987},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36649999022483826},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3206999897956848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/synasc69064.2025.00038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/synasc69064.2025.00038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 27th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","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":11,"referenced_works":["https://openalex.org/W2055493058","https://openalex.org/W2137398591","https://openalex.org/W2156423229","https://openalex.org/W2911668048","https://openalex.org/W2984141054","https://openalex.org/W3121099426","https://openalex.org/W4283580362","https://openalex.org/W4360982405","https://openalex.org/W4387496398","https://openalex.org/W4391768504","https://openalex.org/W4396506123"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"travel":[1,44,181],"time":[2,45,182],"estimation":[3,46],"plays":[4],"a":[5,69,92,122,146,193,201,210,214,228],"pivotal":[6],"role":[7],"in":[8,22,77,213,273],"modern":[9],"navigation,":[10],"logistics,":[11],"and":[12,134,166,177,259],"urban":[13,85,268],"transportation":[14,265],"planning.":[15],"Nevertheless,":[16],"many":[17],"practical":[18],"environments":[19],"face":[20],"constraints":[21],"data":[23,79,221,276],"availability,":[24],"leading":[25],"to":[26,49,74,98,172,243,257],"sparse":[27,78],"or":[28,51,60,270],"incomplete":[29],"historical":[30],"records":[31],"that":[32,108,129,148,186],"are":[33,114,222],"insufficient":[34],"for":[35,263],"large-scale":[36],"machine":[37],"learning":[38],"approaches.":[39],"In":[40,64],"such":[41],"contexts,":[42],"conventional":[43],"models":[47],"tend":[48],"underperform":[50],"fail,":[52],"given":[53],"their":[54],"reliance":[55],"on":[56,200],"dense,":[57],"long-term":[58],"datasets":[59,185],"continuous":[61],"sensor":[62],"inputs.":[63],"this":[65],"paper,":[66],"we":[67],"present":[68],"Zonal":[70],"Estimation":[71],"Method":[72],"designed":[73],"operate":[75],"effectively":[76],"environments.":[80],"The":[81,250],"method":[82,154,199,254],"organizes":[83],"the":[84,99,109,153,160,219,253],"space":[86],"into":[87,121],"concentric":[88,111],"zones":[89],"derived":[90],"from":[91,151],"Gaussian":[93],"Mixture":[94],"Model":[95],"(GMM)":[96],"applied":[97],"spatial":[100,132],"distribution":[101],"of":[102,104,124,137,164,191,203,252],"points":[103],"interest":[105],"(POIs),":[106],"ensuring":[107],"city's":[110],"developmental":[112],"patterns":[113],"captured.":[115],"Each":[116],"zone":[117],"is":[118,255,260],"then":[119],"discretized":[120],"grid":[123],"hexagonal":[125],"cells,":[126],"an":[127],"approach":[128,226],"facilitates":[130],"coherent":[131],"representation":[133],"local":[135],"averaging":[136],"speed":[138,175],"profiles.":[139],"By":[140],"incorporating":[141],"time-based":[142],"aggregation":[143],"procedures,":[144],"particularly":[145],"scheme":[147],"distinguishes":[149],"weekdays":[150],"weekends,":[152],"accommodates":[155],"temporal":[156,167],"fluctuations":[157],"without":[158],"over-segmenting":[159],"data.":[161],"This":[162],"combination":[163],"zonal":[165],"structuring":[168],"makes":[169],"it":[170],"feasible":[171],"obtain":[173],"stable":[174],"estimates":[176],"provide":[178],"reasonably":[179],"accurate":[180],"predictions":[183],"using":[184],"extend":[187],"over":[188],"short":[189],"periods":[190],"only":[192],"few":[194],"days.":[195],"We":[196],"evaluated":[197],"our":[198,225],"set":[202],"approximately":[204],"23,000":[205],"route":[206],"traces":[207],"collected":[208],"within":[209],"ten-day":[211],"interval":[212],"mid-sized":[215],"European":[216],"city.":[217],"Although":[218],"available":[220],"comparatively":[223],"limited,":[224],"achieves":[227],"mean":[229],"absolute":[230],"percentage":[231],"error":[232],"(MAPE)":[233],"as":[234,236],"low":[235],"12.91":[237],"percent,":[238],"achieving":[239],"competitive":[240],"accuracy":[241],"compared":[242],"widely-used":[244],"commercial":[245],"APIs":[246],"like":[247],"Google":[248],"Maps.":[249],"architecture":[251],"simple":[256],"implement":[258],"especially":[261],"well-suited":[262],"early-stage":[264],"analytics,":[266],"data-constrained":[267],"environments,":[269],"fallback":[271],"situations":[272],"which":[274],"real-time":[275],"streams":[277],"cannot":[278],"be":[279],"maintained.":[280]},"counts_by_year":[],"updated_date":"2026-04-23T06:14:38.165362","created_date":"2026-04-22T00:00:00"}