{"id":"https://openalex.org/W2989010797","doi":"https://doi.org/10.1145/3357384.3357829","title":"Deep Dynamic Fusion Network for Traffic Accident Forecasting","display_name":"Deep Dynamic Fusion Network for Traffic Accident Forecasting","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2989010797","doi":"https://doi.org/10.1145/3357384.3357829","mag":"2989010797"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357829","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357829","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","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/A5091518548","display_name":"Chao Huang","orcid":"https://orcid.org/0009-0003-3740-4500"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chao Huang","raw_affiliation_strings":["JD Digits, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Digits, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022275632","display_name":"Chuxu Zhang","orcid":"https://orcid.org/0000-0002-8349-7926"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuxu Zhang","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101406872","display_name":"Peng Dai","orcid":"https://orcid.org/0000-0002-3992-0934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Dai","raw_affiliation_strings":["JD Digits, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Digits, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085032007","display_name":"Liefeng Bo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liefeng Bo","raw_affiliation_strings":["JD Digits, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Digits, Mountain View, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091518548"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7534,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.89460231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2673","last_page":"2681"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.994700014591217,"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/T10370","display_name":"Traffic and Road Safety","score":0.9919999837875366,"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/interpretability","display_name":"Interpretability","score":0.8176308870315552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7888588905334473},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6232450008392334},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6165943145751953},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5298022627830505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.521988034248352},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5213143229484558},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4978935718536377},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.477493017911911},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47744542360305786},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4536452293395996},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4472874701023102},{"id":"https://openalex.org/keywords/dynamic-data","display_name":"Dynamic data","score":0.4381178915500641},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4311951696872711},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.4197178781032562},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4175108075141907},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12524089217185974},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.11321061849594116},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10816258192062378},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10258319973945618}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8176308870315552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7888588905334473},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6232450008392334},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6165943145751953},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5298022627830505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.521988034248352},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5213143229484558},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4978935718536377},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.477493017911911},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47744542360305786},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4536452293395996},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4472874701023102},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.4381178915500641},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4311951696872711},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.4197178781032562},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4175108075141907},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12524089217185974},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.11321061849594116},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10816258192062378},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10258319973945618},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3357829","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357829","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W25572007","https://openalex.org/W569478347","https://openalex.org/W1571935154","https://openalex.org/W1614435007","https://openalex.org/W1902237438","https://openalex.org/W1964215189","https://openalex.org/W1988580225","https://openalex.org/W1990444226","https://openalex.org/W2090786926","https://openalex.org/W2099560956","https://openalex.org/W2127176025","https://openalex.org/W2129129488","https://openalex.org/W2153207204","https://openalex.org/W2153635508","https://openalex.org/W2198346408","https://openalex.org/W2301541953","https://openalex.org/W2302086703","https://openalex.org/W2475334473","https://openalex.org/W2494197057","https://openalex.org/W2537810077","https://openalex.org/W2593390416","https://openalex.org/W2605350416","https://openalex.org/W2613328025","https://openalex.org/W2624190409","https://openalex.org/W2725922920","https://openalex.org/W2735643103","https://openalex.org/W2742940593","https://openalex.org/W2743289629","https://openalex.org/W2763067454","https://openalex.org/W2777938864","https://openalex.org/W2788114581","https://openalex.org/W2808862972","https://openalex.org/W2895806569","https://openalex.org/W2897825916","https://openalex.org/W2963403868","https://openalex.org/W2964057288","https://openalex.org/W4299689471"],"related_works":["https://openalex.org/W2066625485","https://openalex.org/W2006096650","https://openalex.org/W2611311248","https://openalex.org/W160456666","https://openalex.org/W3042974105","https://openalex.org/W3015583272","https://openalex.org/W577118248","https://openalex.org/W2029170852","https://openalex.org/W4387294133","https://openalex.org/W1971084186"],"abstract_inverted_index":{"Traffic":[0],"accident":[1,89],"forecasting":[2],"is":[3,18],"a":[4,57,83,103,108,133,174],"vital":[5],"part":[6],"of":[7,22,29,69,73,102,163],"intelligent":[8],"transportation":[9],"systems":[10],"in":[11,82],"urban":[12],"sensing.":[13],"However,":[14],"predicting":[15],"traffic":[16,88],"accidents":[17],"not":[19],"trivial":[20],"because":[21],"two":[23],"key":[24],"challenges:":[25],"i)":[26],"the":[27,40,66,71,100,145,161,182],"complexities":[28],"external":[30,80,118],"factors":[31,81],"which":[32],"are":[33],"presented":[34],"with":[35,46,99],"heterogeneous":[36,79],"data":[37,153],"structures;":[38],"ii)":[39],"complex":[41],"sequential":[42],"transition":[43],"regularities":[44],"exhibited":[45],"time-dependent":[47],"and":[48,107,126],"high-order":[49],"inter-correlations.":[50],"To":[51],"address":[52],"these":[53],"challenges,":[54],"we":[55,130,159,171],"develop":[56,132],"deep":[58,74],"Dynamic":[59],"Fusion":[60],"Network":[61],"framework":[62,165],"(DFN),":[63],"to":[64,112,139,180],"explore":[65],"central":[67],"theme":[68],"improving":[70],"ability":[72],"neural":[75,136],"network":[76,137],"on":[77,151,177],"modeling":[78],"fully":[84],"dynamic":[85],"manner":[86],"for":[87,120],"forecasting.":[90],"Specifically,":[91],"DFN":[92],"first":[93],"develops":[94],"an":[95],"integrative":[96],"architecture,":[97],"i.e.,":[98],"cooperation":[101],"context-aware":[104],"embedding":[105],"module":[106],"hierarchical":[109],"fusion":[110],"network,":[111],"effectively":[113],"transferring":[114],"knowledge":[115],"from":[116,144,155],"different":[117],"units":[119],"spatial-temporal":[121],"pattern":[122],"learning":[123],"across":[124],"space":[125],"time.":[127],"After":[128],"that,":[129],"further":[131],"temporal":[134,146],"aggregation":[135],"layer":[138],"automatically":[140],"capture":[141],"relevance":[142],"scores":[143],"dimension.":[147],"Through":[148],"extensive":[149],"experiments":[150],"real-world":[152],"collected":[154],"New":[156],"York":[157],"City,":[158],"validate":[160],"effectiveness":[162],"our":[164],"against":[166],"various":[167],"competitive":[168],"methods.":[169],"Besides,":[170],"also":[172],"provide":[173],"qualitative":[175],"analysis":[176],"prediction":[178],"results":[179],"show":[181],"model":[183],"interpretability.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}