{"id":"https://openalex.org/W7117448123","doi":"https://doi.org/10.1145/3714394.3756210","title":"A Combined Machine Learning and Deep Learning Framework for Transportation Mode Recognition from Smartphone Data","display_name":"A Combined Machine Learning and Deep Learning Framework for Transportation Mode Recognition from Smartphone Data","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W7117448123","doi":"https://doi.org/10.1145/3714394.3756210"},"language":null,"primary_location":{"id":"doi:10.1145/3714394.3756210","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756210","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3714394.3756210","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119272915","display_name":"Piotr Dusza","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Piotr Dusza","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0003-8062-615X","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078623536","display_name":"R Bla\u017cej","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Ryszard Blazej","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0004-4291-9143","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115461508","display_name":"Jeremiasz Potoczny","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Jeremiasz Potoczny","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0006-4383-1738","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121494910","display_name":"Paulina Wor","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Paulina Wor","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0004-9264-3093","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121514793","display_name":"Oliwia Rewer","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Oliwia Rewer","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0008-1155-6588","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121484790","display_name":"Kajetan Ginter","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Kajetan Ginter","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0008-8892-1539","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jakub Sikora","orcid":"https://orcid.org/0009-0007-5966-9701"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Jakub Sikora","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0009-0007-5966-9701","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015611403","display_name":"Mateusz Danio\u0142","orcid":"https://orcid.org/0000-0003-2363-7912"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Mateusz Daniol","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0000-0003-2363-7912","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035104412","display_name":"Marcin Str\u0105czkiewicz","orcid":"https://orcid.org/0000-0002-8703-4451"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Marcin Straczkiewicz","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland"],"raw_orcid":"https://orcid.org/0000-0002-8703-4451","affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77052239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"918","last_page":"923"},"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":0.8719000220298767,"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":0.8719000220298767,"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":0.027899999171495438,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.019500000402331352,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/deep-learning","display_name":"Deep learning","score":0.7409999966621399},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6741999983787537},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5871000289916992},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5228000283241272},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.47940000891685486},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4677000045776367},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.45829999446868896},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.32919999957084656}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7724999785423279},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7409999966621399},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6741999983787537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6445000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6151999831199646},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5228000283241272},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.45829999446868896},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25929999351501465},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3714394.3756210","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756210","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3714394.3756210","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714394.3756210","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2084439158","https://openalex.org/W2103532308","https://openalex.org/W2108328714","https://openalex.org/W2607850459","https://openalex.org/W2752228365","https://openalex.org/W2783323081","https://openalex.org/W2883766876","https://openalex.org/W2907123474","https://openalex.org/W3200144996","https://openalex.org/W4321596496","https://openalex.org/W4402722024","https://openalex.org/W4403334207"],"related_works":[],"abstract_inverted_index":{"Smartphone-based":[0],"transportation":[1,50,121],"mode":[2,85,122],"recognition":[3,123],"enables":[4],"scalable":[5],"assessment":[6],"of":[7],"mobility":[8],"behaviors":[9],"in":[10,25,52],"naturalistic":[11],"settings.":[12],"However,":[13],"classification":[14],"remains":[15],"challenging":[16],"due":[17],"to":[18,45,101],"sensor":[19,58,63],"noise,":[20],"contextual":[21],"confounders,":[22],"and":[23,40,49,66,75,119],"variability":[24],"human":[26],"movement.":[27],"Our":[28],"team,":[29],"KMET,":[30],"proposed":[31],"a":[32],"multimodal":[33],"framework":[34],"that":[35,106],"integrates":[36],"feature-based":[37],"gradient":[38],"boosting":[39],"deep":[41,111],"learning":[42,112],"time-series":[43],"classifiers":[44],"distinguish":[46],"eight":[47],"locomotion":[48],"modes":[51],"the":[53,90],"Sussex-Huawei":[54],"Locomotion":[55],"dataset.":[56],"Raw":[57],"data":[59],"were":[60,69,87],"preprocessed":[61],"with":[62,110],"fusion.":[64],"Time-":[65],"frequency-domain":[67],"features":[68,109],"extracted":[70],"for":[71],"XGBoost,":[72],"while":[73],"TimesNet":[74],"MLSTM-FCN":[76],"networks":[77],"learned":[78],"representations":[79],"directly":[80],"from":[81],"raw":[82],"time-series.":[83],"Transportation":[84],"predictions":[86],"aggregated":[88],"using":[89],"HIVE-COTE":[91],"2.0.":[92],"The":[93],"final":[94],"model":[95],"achieved":[96],"an":[97,116],"F1":[98],"score":[99],"equal":[100],"0.957.":[102],"These":[103],"findings":[104],"indicate":[105],"combining":[107],"handcrafted":[108],"might":[113],"serve":[114],"as":[115],"ecologically":[117],"valid":[118],"robust":[120],"approach.":[124]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-12-29T00:00:00"}