{"id":"https://openalex.org/W7138183283","doi":"https://doi.org/10.48550/arxiv.2603.13903","title":"Distributed Acoustic Sensing for Urban Traffic Monitoring: Spatio-Temporal Attention in Recurrent Neural Networks","display_name":"Distributed Acoustic Sensing for Urban Traffic Monitoring: Spatio-Temporal Attention in Recurrent Neural Networks","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138183283","doi":"https://doi.org/10.48550/arxiv.2603.13903"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117497564","display_name":"Izhan Fakhruzi","orcid":"https://orcid.org/0000-0002-6968-4477"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fakhruzi, Izhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032915531","display_name":"Manuel T\u00edtos","orcid":"https://orcid.org/0000-0002-8279-2341"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Titos, Manuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680911","display_name":"Carmen Ben\u00edtez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ben\u00edtez, Carmen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129693614","display_name":"Luz Garc\u00eda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garc\u00eda, Luz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.7258999943733215,"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":0.7258999943733215,"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/T10205","display_name":"Advanced Fiber Optic Sensors","score":0.05000000074505806,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.014299999922513962,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/scalability","display_name":"Scalability","score":0.6133999824523926},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5949000120162964},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4291999936103821},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42080000042915344},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.33169999718666077},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32260000705718994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869999766349792},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6133999824523926},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5949000120162964},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.430400013923645},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4291999936103821},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42080000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.366100013256073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.29179999232292175},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25060001015663147},{"id":"https://openalex.org/C204673680","wikidata":"https://www.wikidata.org/wiki/Q1628107","display_name":"Airfield traffic pattern","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.6633337140083313,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Effective":[0],"urban":[1,189],"traffic":[2,21,45,56,155,181],"monitoring":[3,57,182],"is":[4],"essential":[5],"for":[6,43],"improving":[7],"mobility,":[8],"enhancing":[9],"safety,":[10],"and":[11,82,87,107,114,125,142,178],"supporting":[12],"sustainable":[13],"cities.":[14],"Distributed":[15],"Acoustic":[16],"Sensing":[17],"(DAS)":[18],"enables":[19],"large-scale":[20],"observation":[22],"by":[23,137],"transforming":[24],"existing":[25],"fiber-optic":[26],"infrastructure":[27],"into":[28],"dense":[29],"arrays":[30],"of":[31,40,117,134,176,185],"vibration":[32],"sensors.":[33],"However,":[34],"modeling":[35],"the":[36,71,95,121,146,174],"high-resolution":[37],"spatio-temporal":[38],"structure":[39],"DAS":[41],"data":[42],"reliable":[44],"event":[46],"recognition":[47,103],"remains":[48],"challenging.":[49],"This":[50],"study":[51],"presents":[52],"a":[53,66],"real-world":[54],"DAS-based":[55,180],"experiment":[58],"conducted":[59],"in":[60],"Granada,":[61],"Spain,":[62],"where":[63],"vehicles":[64],"cross":[65],"fiber":[67],"deployed":[68],"perpendicular":[69],"to":[70,79,98],"roadway.":[72],"Recurrent":[73],"neural":[74],"networks":[75],"(RNNs)":[76],"are":[77,91],"employed":[78],"model":[80,126],"intra-":[81],"inter-event":[83],"temporal":[84,88,143],"dependencies.":[85],"Spatial":[86],"attention":[89,118],"mechanisms":[90],"systematically":[92],"integrated":[93],"within":[94],"RNN":[96],"architecture":[97],"analyze":[99],"their":[100],"impact":[101],"on":[102],"performance,":[104],"parameter":[105],"efficiency,":[106],"interpretability.":[108],"Results":[109],"show":[110],"that":[111],"an":[112],"appropriate":[113],"complementary":[115],"placement":[116],"modules":[119],"improves":[120],"balance":[122],"between":[123],"accuracy":[124],"complexity.":[127],"Attention":[128],"heatmaps":[129],"provide":[130],"physically":[131],"meaningful":[132],"interpretations":[133],"classification":[135],"decisions":[136],"highlighting":[138],"informative":[139],"spatial":[140,151],"locations":[141,159],"segments.":[144],"Furthermore,":[145],"proposed":[147],"SA-bi-TA":[148],"configuration":[149],"demonstrates":[150],"transferability,":[152],"successfully":[153],"recognizing":[154],"events":[156],"at":[157],"sensing":[158,190],"different":[160],"from":[161],"those":[162],"used":[163],"during":[164],"training,":[165],"with":[166],"only":[167],"moderate":[168],"performance":[169],"degradation.":[170],"These":[171],"findings":[172],"support":[173],"development":[175],"scalable":[177],"interpretable":[179],"systems":[183],"capable":[184],"operating":[186],"under":[187],"heterogeneous":[188],"conditions.":[191]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
