{"id":"https://openalex.org/W2580769846","doi":null,"title":"Mode Choice Modeling Using Artificial Neural Networks","display_name":"Mode Choice Modeling Using Artificial Neural Networks","publication_year":2003,"publication_date":"2003-10-02","ids":{"openalex":"https://openalex.org/W2580769846","mag":"2580769846"},"language":"en","primary_location":{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/9845","doi":null,"is_oa":true,"landing_page_url":"http://scholar.lib.vt.edu/theses/available/etd-10152003-144051","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/5936a979-fade-4184-9cb8-a831ca5d48cb/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vtechworks.lib.vt.edu/bitstreams/5936a979-fade-4184-9cb8-a831ca5d48cb/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068508206","display_name":"Praveen K. Edara","orcid":"https://orcid.org/0000-0003-2707-642X","observed_orcids":["https://orcid.org/0000-0003-2707-642X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Praveen Edara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5068508206"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":9,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9491999745368958,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9491999745368958,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.020800000056624413,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.00430000014603138,"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/neural-network","display_name":"artificial neural networks","score":0.9829999804496765},{"id":"https://openalex.org/keywords/multinomial-logit-model","display_name":"multinomial logistic regression","score":0.906000018119812},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.8130000233650208},{"id":"https://openalex.org/keywords/travel-demand-forecasting","display_name":"travel demand forecasting","score":0.9340000152587891},{"id":"https://openalex.org/keywords/data-mining","display_name":"data mining","score":0.6970000267028809},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.8109999895095825},{"id":"https://openalex.org/keywords/transportation-planning","display_name":"transportation planning","score":0.7200000286102295},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.5270000100135803}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6279848217964172},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5466360449790955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47677189111709595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.423550009727478},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.09310939908027649}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/9845","doi":null,"is_oa":true,"landing_page_url":"http://scholar.lib.vt.edu/theses/available/etd-10152003-144051","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/5936a979-fade-4184-9cb8-a831ca5d48cb/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/35388","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10919/35388","pdf_url":null,"source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"mag:2580769846","doi":null,"is_oa":false,"landing_page_url":"https://vtechworks.lib.vt.edu/handle/10919/9845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/9845","doi":null,"is_oa":true,"landing_page_url":"http://scholar.lib.vt.edu/theses/available/etd-10152003-144051","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/5936a979-fade-4184-9cb8-a831ca5d48cb/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[{"score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2580769846.pdf","grobid_xml":"https://content.openalex.org/works/W2580769846.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W63816938","https://openalex.org/W639590528","https://openalex.org/W656348742","https://openalex.org/W1550812296","https://openalex.org/W1555857857","https://openalex.org/W1979258712","https://openalex.org/W1995341919","https://openalex.org/W2032793477","https://openalex.org/W2103496339","https://openalex.org/W2137983211","https://openalex.org/W3207342693"],"related_works":["https://openalex.org/W2304613467","https://openalex.org/W591924570","https://openalex.org/W2889960786","https://openalex.org/W2467969003","https://openalex.org/W3084823049","https://openalex.org/W3213293541","https://openalex.org/W2125423411","https://openalex.org/W2738589343","https://openalex.org/W2402073912","https://openalex.org/W2354703879"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1,245],"techniques":[2,39],"have":[3,21],"produced":[4],"excellent":[5],"results":[6,94,136,158,171,212,218,230],"in":[7,49],"many":[8],"diverse":[9],"fields":[10],"of":[11,37,78,137,163,184,213,219,231],"engineering.":[12,27],"Techniques":[13],"such":[14,170],"as":[15],"neural":[16,31,53,89,152,207],"networks":[17,32,54,90],"and":[18,61,65,91,105,145,174,209,225,240],"fuzzy":[19],"systems":[20],"found":[22,150],"their":[23],"way":[24],"into":[25],"transportation":[26,238,249],"In":[28],"recent":[29],"years,":[30],"are":[33,55,128,140,172,187],"being":[34],"used":[35,111,129],"instead":[36],"regression":[38,107,227],"for":[40,112,159,169,247],"travel":[41],"demand":[42],"forecasting":[43],"purposes.":[44],"The":[45,75,109,135,166,180,229],"basic":[46],"reason":[47],"lies":[48],"the":[50,93,101,118,132,156,160,177,194,201,211,217],"fact":[51],"that":[52,151],"able":[56,67],"to":[57,68,82,130,176,192,242],"capture":[58],"complex":[59],"relationships":[60],"learn":[62],"from":[63,117,196],"examples":[64],"also":[66],"adapt":[69],"when":[70],"new":[71],"data":[72,110,195],"become":[73],"available.":[74],"primary":[76],"goal":[77],"this":[79,113,185,214,232],"thesis":[80,186],"is":[81,115,149],"develop":[83],"mode":[84,97,202],"choice":[85,98,203],"models":[86,99,139,221],"using":[87,205],"artificial":[88,206,244],"compare":[92,210],"with":[95,216],"traditional":[96,220],"like":[100,126],"multinomial":[102],"logit":[103,223],"model":[104,224],"linear":[106,226],"method.":[108],"modeling":[114],"extracted":[116,133],"American":[119],"Travel":[120],"Survey":[121],"data.":[122,134],"Data":[123],"mining":[124],"procedures":[125],"clustering":[127],"process":[131],"three":[138,181],"compared":[141],"based":[142],"on":[143],"residuals":[144],"error":[146],"criteria.":[147],"It":[148],"network":[153],"approach":[154,191,215],"produces":[155],"best":[157],"chosen":[161],"set":[162],"explanatory":[164],"variables.":[165],"possible":[167],"reasons":[168],"identified":[173],"explained":[175],"extent":[178],"possible.":[179],"major":[182],"objectives":[183],"to:":[188],"present":[189],"an":[190],"handle":[193],"a":[197],"survey":[198],"database,":[199],"address":[200],"problem":[204],"networks,":[208],"vis-\u00e0-vis":[222],"approach.":[228],"research":[233],"work":[234],"should":[235],"encourage":[236],"more":[237],"researchers":[239],"professionals":[241],"consider":[243],"tools":[246],"solving":[248],"planning":[250],"problems.":[251]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2}],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2017-02-03T00:00:00"}