{"id":"https://openalex.org/W2724687879","doi":"https://doi.org/10.1109/icis.2017.7959979","title":"A hybrid approach for urban expressway traffic incident duration prediction with Cox regression and random survival forests models","display_name":"A hybrid approach for urban expressway traffic incident duration prediction with Cox regression and random survival forests models","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2724687879","doi":"https://doi.org/10.1109/icis.2017.7959979","mag":"2724687879"},"language":"en","primary_location":{"id":"doi:10.1109/icis.2017.7959979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7959979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-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/A5032657937","display_name":"Axiang Ke","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Axiang Ke","raw_affiliation_strings":["School of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100634507","display_name":"Zhen Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Gao","raw_affiliation_strings":["School of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102719437","display_name":"Rongjie Yu","orcid":"https://orcid.org/0000-0003-4782-0279"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongjie Yu","raw_affiliation_strings":["Ministry of Education, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062105127","display_name":"Min Wang","orcid":"https://orcid.org/0000-0001-7961-2576"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Wang","raw_affiliation_strings":["School of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100336644","display_name":"Xuesong Wang","orcid":"https://orcid.org/0000-0001-8046-3213"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuesong Wang","raw_affiliation_strings":["Ministry of Education, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032657937"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.9136,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7632589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"113","last_page":"118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.998199999332428,"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":0.9977999925613403,"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/duration","display_name":"Duration (music)","score":0.6414333581924438},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.6361851692199707},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.6029758453369141},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5578598976135254},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5216022729873657},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4642971158027649},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4562046527862549},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4532046616077423},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4187725782394409},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.38893646001815796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20889967679977417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1050008237361908}],"concepts":[{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.6414333581924438},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.6361851692199707},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.6029758453369141},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5578598976135254},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5216022729873657},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4642971158027649},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4562046527862549},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4532046616077423},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4187725782394409},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.38893646001815796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20889967679977417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1050008237361908},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis.2017.7959979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7959979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7599999904632568}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W58077054","https://openalex.org/W1975185440","https://openalex.org/W1995840715","https://openalex.org/W2010039425","https://openalex.org/W2024263276","https://openalex.org/W2030276587","https://openalex.org/W2052825782","https://openalex.org/W2056425734","https://openalex.org/W2085934079","https://openalex.org/W2087660514","https://openalex.org/W2098642267","https://openalex.org/W2107482271","https://openalex.org/W2145198235","https://openalex.org/W2164434252","https://openalex.org/W2287585202","https://openalex.org/W2294889300","https://openalex.org/W2374266822","https://openalex.org/W2387532678","https://openalex.org/W2390880716","https://openalex.org/W3099478002","https://openalex.org/W3124440722","https://openalex.org/W3147894994","https://openalex.org/W6602373921"],"related_works":["https://openalex.org/W2348837382","https://openalex.org/W3191198889","https://openalex.org/W2048488252","https://openalex.org/W4289884158","https://openalex.org/W4288365262","https://openalex.org/W2940614149","https://openalex.org/W2787485953","https://openalex.org/W3217432596","https://openalex.org/W4386715265","https://openalex.org/W1995617853"],"abstract_inverted_index":{"Traffic":[0],"incidents":[1],"such":[2],"as":[3,128],"crashes":[4],"have":[5,177],"significant":[6,115,124,178],"impacts":[7,179],"on":[8,25,60,180],"urban":[9,19,66],"expressway":[10],"operation.":[11],"The":[12,55,163],"roadside":[13],"service":[14],"and":[15,42,79,94,112,145,158,174,183],"operational":[16],"efficiency":[17],"of":[18,88,118],"expressways":[20],"could":[21],"be":[22],"improved":[23],"based":[24,59],"a":[26,35,104],"well-developed":[27],"incident":[28,51,62,119,168,181,195],"duration":[29,52,196],"prediction":[30,190,197],"model.":[31,54,135],"In":[32,100],"this":[33],"study,":[34],"hybrid":[36,102,185],"approach":[37,186],"that":[38,167],"combines":[39],"Cox":[40,105],"regression":[41,106],"random":[43,132],"survival":[44,133],"forests":[45,134],"algorithm":[46],"is":[47,57,90,108],"developed":[48],"to":[49,110,153],"establish":[50],"analysis":[53,164],"study":[56],"conducted":[58],"traffic":[61,70,77,194],"data":[63],"from":[64],"Shanghai":[65],"expressways.":[67],"For":[68],"each":[69],"incident,":[71],"information":[72],"about":[73],"the":[74,95,101,114,131,137,155,184],"road":[75],"geometry,":[76],"operation,":[78],"weather":[80],"conditions":[81],"was":[82],"collected":[83],"for":[84,92,98,130],"experiments,":[85],"where":[86],"80%":[87],"sample":[89],"used":[91,127,152],"training":[93],"rest":[96],"20%":[97],"validation.":[99],"model,":[103],"model":[107,156,187],"predeveloped":[109],"investigate":[111],"identify":[113],"contributing":[116],"factors":[117,125],"duration.":[120],"Then,":[121],"these":[122],"identified":[123],"are":[126,151],"inputs":[129],"Finally,":[136],"statistical":[138],"measurements":[139],"including":[140],"mean":[141,147],"absolute":[142],"error":[143,149],"(MAE)":[144],"normalized":[146],"square":[148],"(NMS)":[150],"measure":[154],"performance":[157],"compare":[159],"with":[160],"other":[161,175],"models.":[162],"results":[165],"show":[166],"type,":[169],"location,":[170],"affected":[171],"lane":[172],"numbers":[173],"attributes":[176],"duration,":[182],"provides":[188],"better":[189],"accuracy":[191],"over":[192],"traditional":[193],"methods.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}