{"id":"https://openalex.org/W1972916106","doi":"https://doi.org/10.1109/tits.2015.2405759","title":"Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data","display_name":"Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data","publication_year":2015,"publication_date":"2015-03-19","ids":{"openalex":"https://openalex.org/W1972916106","doi":"https://doi.org/10.1109/tits.2015.2405759","mag":"1972916106"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2015.2405759","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2015.2405759","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5012227013","display_name":"Arash Jahangiri","orcid":"https://orcid.org/0000-0002-8825-961X"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]},{"id":"https://openalex.org/I4387930269","display_name":"Virginia Tech Transportation Institute","ror":"https://ror.org/05953j253","country_code":null,"type":"facility","lineage":["https://openalex.org/I4387930269","https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arash Jahangiri","raw_affiliation_strings":["Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, USA","Center for Sustainable Mobility, Virginia Tech Transp. Inst., Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795","https://openalex.org/I4387930269"]},{"raw_affiliation_string":"Center for Sustainable Mobility, Virginia Tech Transp. Inst., Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002736916","display_name":"Hesham Rakha","orcid":"https://orcid.org/0000-0002-5845-2929"},"institutions":[{"id":"https://openalex.org/I4387930269","display_name":"Virginia Tech Transportation Institute","ror":"https://ror.org/05953j253","country_code":null,"type":"facility","lineage":["https://openalex.org/I4387930269","https://openalex.org/I859038795"]},{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hesham A. Rakha","raw_affiliation_strings":["Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, USA","Center for Sustainable Mobility, Virginia Tech Transp. Inst., Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795","https://openalex.org/I4387930269"]},{"raw_affiliation_string":"Center for Sustainable Mobility, Virginia Tech Transp. Inst., Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012227013"],"corresponding_institution_ids":["https://openalex.org/I4387930269","https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":31.3255,"has_fulltext":false,"cited_by_count":289,"citation_normalized_percentile":{"value":0.99653598,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"16","issue":"5","first_page":"2406","last_page":"2417"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9781000018119812,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9659000039100647,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7865433692932129},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7794326543807983},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.648893415927887},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6119512319564819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5936115980148315},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.5908905267715454},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5761563777923584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5728116631507874},{"id":"https://openalex.org/keywords/mobile-phone","display_name":"Mobile phone","score":0.5306949615478516},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4901888966560364},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.48758524656295776},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.44786375761032104},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4202045500278473},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38465389609336853},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.34374111890792847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3416464328765869}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7865433692932129},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7794326543807983},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.648893415927887},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6119512319564819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5936115980148315},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.5908905267715454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5761563777923584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5728116631507874},{"id":"https://openalex.org/C2777421447","wikidata":"https://www.wikidata.org/wiki/Q17517","display_name":"Mobile phone","level":2,"score":0.5306949615478516},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4901888966560364},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.48758524656295776},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.44786375761032104},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4202045500278473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38465389609336853},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34374111890792847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3416464328765869},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2015.2405759","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2015.2405759","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W37176611","https://openalex.org/W56054144","https://openalex.org/W240891515","https://openalex.org/W273955616","https://openalex.org/W1480376833","https://openalex.org/W1513618424","https://openalex.org/W1540358749","https://openalex.org/W1581198862","https://openalex.org/W1594031697","https://openalex.org/W1680392829","https://openalex.org/W1983645263","https://openalex.org/W1986324410","https://openalex.org/W1995945562","https://openalex.org/W2002927086","https://openalex.org/W2017634428","https://openalex.org/W2020642377","https://openalex.org/W2022749020","https://openalex.org/W2025797281","https://openalex.org/W2042742742","https://openalex.org/W2064508781","https://openalex.org/W2065807460","https://openalex.org/W2090425484","https://openalex.org/W2096558133","https://openalex.org/W2105046342","https://openalex.org/W2108328714","https://openalex.org/W2109943925","https://openalex.org/W2118286367","https://openalex.org/W2119349310","https://openalex.org/W2130578587","https://openalex.org/W2131147042","https://openalex.org/W2131987814","https://openalex.org/W2143394441","https://openalex.org/W2148948822","https://openalex.org/W2153635508","https://openalex.org/W2154053567","https://openalex.org/W2166619467","https://openalex.org/W2169559282","https://openalex.org/W2172000360","https://openalex.org/W2487770199","https://openalex.org/W2787894218","https://openalex.org/W2898320034","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W4212883601","https://openalex.org/W4399585701","https://openalex.org/W6609277357","https://openalex.org/W6610017368","https://openalex.org/W6632355836","https://openalex.org/W6634833582","https://openalex.org/W6637386731","https://openalex.org/W6675583992","https://openalex.org/W6684320606"],"related_works":["https://openalex.org/W2356006086","https://openalex.org/W2545168295","https://openalex.org/W2365897603","https://openalex.org/W4234814094","https://openalex.org/W2156308897","https://openalex.org/W4303613760","https://openalex.org/W2361871310","https://openalex.org/W2417246878","https://openalex.org/W1982154684","https://openalex.org/W4290466010"],"abstract_inverted_index":{"This":[0],"paper":[1],"adopts":[2],"different":[3,131],"supervised":[4],"learning":[5,12],"methods":[6,132,141],"from":[7,69,98,113],"the":[8,19,105,114,146],"field":[9],"of":[10,130,165],"machine":[11],"to":[13,119,123,144,154],"develop":[14,155],"multiclass":[15],"classifiers":[16],"that":[17,36,49,160],"identify":[18],"transportation":[20,126],"mode,":[21],"including":[22,72],"driving":[23],"a":[24,27,30,51,100,156,163,170,175],"car,":[25],"riding":[26,29],"bicycle,":[28],"bus,":[31],"walking,":[32],"and":[33,46,56,63,75,91,135,139,174],"running.":[34],"Methods":[35],"were":[37,67,96,117,142],"considered":[38],"include":[39],"K-nearest":[40],"neighbor,":[41],"support":[42],"vector":[43,77],"machines":[44],"(SVMs),":[45],"tree-based":[47],"models":[48],"comprise":[50],"single":[52],"decision":[53],"tree,":[54],"bagging,":[55],"random":[57,176],"forest":[58,177],"(RF)":[59],"methods.":[60],"For":[61],"training":[62],"validating":[64],"purposes,":[65],"data":[66],"obtained":[68,112],"smartphone":[70,115],"sensors,":[71],"accelerometer,":[73],"gyroscope,":[74],"rotation":[76],"sensors.":[78],"K-fold":[79],"cross-validation":[80],"as":[81,83],"well":[82],"out-of-bag":[84],"error":[85],"was":[86,102,133,152],"used":[87],"for":[88],"model":[89],"selection":[90],"validation":[92],"purposes.":[93],"Several":[94],"features":[95,167],"created":[97],"which":[99],"subset":[101],"identified":[103],"through":[104],"minimum":[106],"redundancy":[107],"maximum":[108],"relevance":[109],"method.":[110,178],"Data":[111],"sensors":[116],"found":[118,143],"provide":[120],"important":[121],"information":[122],"distinguish":[124],"between":[125],"modes.":[127],"The":[128,137],"performance":[129],"evaluated":[134],"compared.":[136],"RF":[138],"SVM":[140],"produce":[145],"best":[147],"performance.":[148],"Furthermore,":[149],"an":[150],"effort":[151],"made":[153],"new":[157],"additional":[158],"feature":[159],"entails":[161],"creating":[162],"combination":[164],"other":[166],"by":[168],"adopting":[169],"simulated":[171],"annealing":[172],"algorithm":[173]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":46},{"year":2021,"cited_by_count":59},{"year":2020,"cited_by_count":36},{"year":2019,"cited_by_count":34},{"year":2018,"cited_by_count":26},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
