{"id":"https://openalex.org/W4391235367","doi":"https://doi.org/10.1109/access.2024.3358453","title":"Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion","display_name":"Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391235367","doi":"https://doi.org/10.1109/access.2024.3358453"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3358453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3358453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414067.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414067.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100424542","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0003-3366-3137"},"institutions":[{"id":"https://openalex.org/I4210113261","display_name":"Jiangxi Transportation Research Institute","ror":"https://ror.org/026xefd52","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113261"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Wang","raw_affiliation_strings":["Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China","institution_ids":["https://openalex.org/I4210113261"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032398964","display_name":"Tongbin Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113261","display_name":"Jiangxi Transportation Research Institute","ror":"https://ror.org/026xefd52","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113261"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongbin Liu","raw_affiliation_strings":["Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China","institution_ids":["https://openalex.org/I4210113261"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063866219","display_name":"Yong\u2010Jie Lu","orcid":"https://orcid.org/0000-0001-6174-6621"},"institutions":[{"id":"https://openalex.org/I4210113261","display_name":"Jiangxi Transportation Research Institute","ror":"https://ror.org/026xefd52","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113261"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Lu","raw_affiliation_strings":["Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China","institution_ids":["https://openalex.org/I4210113261"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108896467","display_name":"Huawen Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113261","display_name":"Jiangxi Transportation Research Institute","ror":"https://ror.org/026xefd52","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113261"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawen Wan","raw_affiliation_strings":["Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China","institution_ids":["https://openalex.org/I4210113261"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101913421","display_name":"Peng Huang","orcid":"https://orcid.org/0000-0003-2948-7169"},"institutions":[{"id":"https://openalex.org/I4210113261","display_name":"Jiangxi Transportation Research Institute","ror":"https://ror.org/026xefd52","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113261"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Huang","raw_affiliation_strings":["Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Road Network Operation Management Company, Jiangxi Provincial Transportation Investment Group, Nanchang, China","institution_ids":["https://openalex.org/I4210113261"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072062044","display_name":"Fang\u2010Ming Deng","orcid":"https://orcid.org/0000-0002-1156-0930"},"institutions":[{"id":"https://openalex.org/I13985625","display_name":"East China Jiaotong University","ror":"https://ror.org/05x2f1m38","country_code":"CN","type":"education","lineage":["https://openalex.org/I13985625"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangming Deng","raw_affiliation_strings":["School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China"],"raw_orcid":"https://orcid.org/0000-0002-1156-0930","affiliations":[{"raw_affiliation_string":"School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China","institution_ids":["https://openalex.org/I13985625"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.4017,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78128396,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"18694","last_page":"18702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9972000122070312,"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":0.9972000122070312,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.994700014591217,"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/T10370","display_name":"Traffic and Road Safety","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7576466202735901},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6603385806083679},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6548658013343811},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5888976454734802},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5508940815925598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5448039770126343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5402334332466125},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4663030207157135},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45076900720596313},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.44773218035697937},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4398634433746338},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.24310263991355896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7576466202735901},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6603385806083679},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6548658013343811},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5888976454734802},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5508940815925598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5448039770126343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5402334332466125},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4663030207157135},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45076900720596313},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.44773218035697937},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4398634433746338},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.24310263991355896},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3358453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3358453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414067.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5c7abcb4f30547039f8700ffc9552d26","is_oa":true,"landing_page_url":"https://doaj.org/article/5c7abcb4f30547039f8700ffc9552d26","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 18694-18702 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3358453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3358453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414067.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G4446008309","display_name":null,"funder_award_id":"52167008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306108","display_name":"U.S. Department of Transportation","ror":"https://ror.org/02xfw2e90"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391235367.pdf","grobid_xml":"https://content.openalex.org/works/W4391235367.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1979037819","https://openalex.org/W1988014195","https://openalex.org/W2040870580","https://openalex.org/W2067660066","https://openalex.org/W2108192761","https://openalex.org/W2138537374","https://openalex.org/W2170465906","https://openalex.org/W2385766905","https://openalex.org/W2517873857","https://openalex.org/W2528799109","https://openalex.org/W2900877045","https://openalex.org/W2903401114","https://openalex.org/W2908389900","https://openalex.org/W2997643818","https://openalex.org/W2999349943","https://openalex.org/W3041192002","https://openalex.org/W3080189037","https://openalex.org/W3080344202","https://openalex.org/W3091643781","https://openalex.org/W4309927922","https://openalex.org/W4318464599","https://openalex.org/W4321373849","https://openalex.org/W4377000627","https://openalex.org/W4379185525","https://openalex.org/W4381143881","https://openalex.org/W4383695784","https://openalex.org/W4387245341","https://openalex.org/W7007909381"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724","https://openalex.org/W2241444561","https://openalex.org/W1502951582"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,89,94],"improve":[3,96],"the":[4,25,39,55,60,64,73,76,91,97,100,108,113,119,126,133,140,146],"prediction":[5,14,77,92,101,114,147,158],"accuracy,":[6],"this":[7],"paper":[8],"proposes":[9],"a":[10],"traffic":[11],"accident":[12],"risk":[13],"method":[15],"of":[16,63,68,75,99,116],"tunnel":[17,69],"based":[18,31],"on":[19,32],"multi-source":[20,65],"heterogeneous":[21,66],"data":[22,67,120,134],"fusion.":[23,53],"Firstly,":[24],"feature":[26,47],"extraction":[27],"and":[28,38,49,83,125,139],"coding":[29],"model":[30,78,111],"Gabor":[33],"cloud":[34,41],"image":[35,42],"is":[36,43,70,87,122,129,136,143,153],"constructed,":[37],"unstructured":[40],"enhanced":[44],"by":[45,52],"amplitude":[46],"level":[48],"then":[50],"encoded":[51],"Secondly,":[54],"long":[56],"sequence":[57],"formed":[58],"after":[59],"sample":[61],"concatenation":[62],"used":[71],"as":[72],"input":[74],"GRU":[79],"(Gate":[80],"Recurrent":[81],"Unit),":[82],"AdaBoost":[84],"(Adaptive":[85],"Boosting)":[86],"introduced":[88],"learn":[90],"results":[93,105],"further":[95],"robustness":[98],"model.":[102],"The":[103],"experimental":[104],"show":[106],"that":[107],"proposed":[109],"GRU-AdaBoost":[110],"achieves":[112],"accuracy":[115,148],"84.59%":[117],"when":[118],"volume":[121,135],"3":[123,144],"years":[124],"time":[127,141],"interval":[128,142],"5":[130],"weeks.":[131],"When":[132],"1":[137],"year":[138],"weeks,":[145],"can":[149],"reach":[150],"80.85%,":[151],"which":[152],"9.61%":[154],"higher":[155],"than":[156],"traditional":[157],"models.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
