{"id":"https://openalex.org/W7225626277","doi":"https://doi.org/10.5281/zenodo.24084525","title":"Chronos-Traffic: An AI-Driven Framework for Spatio-Temporal Traffic Forecasting and Anomaly Detection","display_name":"Chronos-Traffic: An AI-Driven Framework for Spatio-Temporal Traffic Forecasting and Anomaly Detection","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225626277","doi":"https://doi.org/10.5281/zenodo.24084525"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24084525","doi":"https://doi.org/10.5281/zenodo.24084525","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24084525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24084525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161314194","display_name":"Shaheer Babar","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I59225215","display_name":"Bahria University","ror":"https://ror.org/02v8d7770","country_code":"PK","type":"education","lineage":["https://openalex.org/I59225215"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Babar, Shaheer","raw_affiliation_strings":["Bahria University Karachi Campus"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bahria University Karachi Campus","institution_ids":["https://openalex.org/I59225215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161314194"],"corresponding_institution_ids":["https://openalex.org/I59225215"],"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":0.9994999766349792,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/traffic-forecasting","display_name":"traffic prediction","score":0.8730000257492065},{"id":"https://openalex.org/keywords/spatiotemporal-prediction","display_name":"spatiotemporal prediction","score":0.9089999794960022},{"id":"https://openalex.org/keywords/road-anomaly-detection","display_name":"road anomaly detection","score":0.7870000004768372},{"id":"https://openalex.org/keywords/road-condition-classification","display_name":"road condition classification","score":0.8679999709129333},{"id":"https://openalex.org/keywords/construction-zones","display_name":"construction zones","score":0.6060000061988831},{"id":"https://openalex.org/keywords/traffic-accidents","display_name":"traffic accidents","score":0.9150000214576721},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":0.8600000143051147},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.7459999918937683},{"id":"https://openalex.org/keywords/ridge-regression","display_name":"ridge regression","score":0.7689999938011169},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.984000027179718},{"id":"https://openalex.org/keywords/intelligent-traffic-monitoring","display_name":"intelligent traffic monitoring","score":0.7429999709129333}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7592999935150146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903000235557556},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5457000136375427},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.492900013923645},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3718000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35429999232292175},{"id":"https://openalex.org/C33499554","wikidata":"https://www.wikidata.org/wiki/Q1417134","display_name":"Dashboard","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24084525","doi":"https://doi.org/10.5281/zenodo.24084525","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24084525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24084526","doi":"https://doi.org/10.5281/zenodo.24084526","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24084526","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24084525","doi":"https://doi.org/10.5281/zenodo.24084525","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24084525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8970999717712402,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"},{"score":0.875,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4415306960","https://openalex.org/W4382449675","https://openalex.org/W7164481730","https://openalex.org/W7129062776","https://openalex.org/W4416707310","https://openalex.org/W4409365780","https://openalex.org/W7126437669","https://openalex.org/W7125526192","https://openalex.org/W4403484874","https://openalex.org/W4412786996"],"abstract_inverted_index":{"Chronos-Traffic":[0],"presents":[1,107],"an":[2,108],"AI-driven":[3],"framework":[4,44],"for":[5,32,96,111,118],"spatio-temporal":[6,129],"traffic":[7,18,55,97,113],"analysis,":[8],"forecasting,":[9,98],"and":[10,40,62,93,102,115,126],"road":[11,34],"anomaly":[12,64],"detection.":[13],"The":[14,43,84],"system":[15],"combines":[16],"historical":[17],"data":[19],"analysis":[20],"using":[21],"regression-based":[22],"forecasting":[23,57,130],"models":[24],"with":[25,49],"a":[26,46,50,67,78],"MobileNetV2-based":[27],"convolutional":[28],"neural":[29],"network":[30],"(CNN)":[31],"classifying":[33],"conditions":[35],"into":[36],"normal":[37],"traffic,":[38],"construction":[39],"accident":[41],"categories.":[42],"integrates":[45],"FastAPI":[47],"backend":[48],"Streamlit":[51],"dashboard":[52],"to":[53],"provide":[54],"visualization,":[56],"results,":[58],"model":[59],"performance":[60,76],"evaluation":[61,71],"image-based":[63],"detection":[65],"through":[66,121],"unified":[68],"interface.":[69],"Experimental":[70],"demonstrates":[72],"the":[73,100],"system's":[74],"classification":[75],"on":[77],"held-out":[79],"dataset":[80],"of":[81],"819":[82],"images.":[83],"study":[85],"also":[86],"evaluates":[87],"Ridge":[88],"Regression":[89],"against":[90],"Random":[91],"Forest":[92],"Persistence":[94],"baselines":[95],"discussing":[99],"results":[101],"current":[103],"limitations.":[104],"This":[105],"work":[106],"integrated":[109],"prototype":[110],"intelligent":[112],"monitoring":[114],"highlights":[116],"opportunities":[117],"future":[119],"improvements":[120],"larger":[122],"datasets,":[123],"independent":[124],"validation,":[125],"more":[127],"advanced":[128],"techniques.":[131]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}