{"id":"https://openalex.org/W4415622881","doi":"https://doi.org/10.1145/3764924.3770894","title":"ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity","display_name":"ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity","publication_year":2025,"publication_date":"2025-10-28","ids":{"openalex":"https://openalex.org/W4415622881","doi":"https://doi.org/10.1145/3764924.3770894"},"language":"en","primary_location":{"id":"doi:10.1145/3764924.3770894","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3764924.3770894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Intelligence for Smart and Connected Communities","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.08051","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095770339","display_name":"Mahmuda Sultana Mimi","orcid":"https://orcid.org/0009-0007-8534-3633"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mahmuda Sultana Mimi","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0007-8534-3633","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, Texas, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117855305","display_name":"Md Monzurul Islam","orcid":"https://orcid.org/0009-0007-3670-6100"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Monzurul Islam","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0007-3670-6100","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, Texas, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116335722","display_name":"Anannya Ghosh Tusti","orcid":"https://orcid.org/0009-0001-5102-5182"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anannya Ghosh Tusti","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0001-5102-5182","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, Texas, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098934039","display_name":"Shriyank Somvanshi","orcid":"https://orcid.org/0009-0008-3723-0607"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shriyank Somvanshi","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0008-3723-0607","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, Texas, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053621729","display_name":"Subasish Das","orcid":"https://orcid.org/0000-0002-1671-2753"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subasish Das","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, Texas, USA"],"raw_orcid":"https://orcid.org/0000-0002-1671-2753","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, Texas, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088393129","display_name":"Xinyue Ye","orcid":"https://orcid.org/0000-0001-8838-9476"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyue Ye","raw_affiliation_strings":["Geography, University of Alabama, Tuscaloosa, Alabama, USA"],"raw_orcid":"https://orcid.org/0000-0001-8838-9476","affiliations":[{"raw_affiliation_string":"Geography, University of Alabama, Tuscaloosa, Alabama, USA","institution_ids":["https://openalex.org/I17301866"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5095770339"],"corresponding_institution_ids":["https://openalex.org/I13511017"],"apc_list":null,"apc_paid":null,"fwci":1.1543,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81255391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.2669999897480011,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.2669999897480011,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.16580000519752502,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.04809999838471413,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crash","display_name":"Crash","score":0.751800000667572},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6661999821662903},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.4982999861240387},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.46140000224113464},{"id":"https://openalex.org/keywords/spatial-network","display_name":"Spatial network","score":0.4372999966144562},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4334999918937683},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.4250999987125397},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4068000018596649}],"concepts":[{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.751800000667572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7232000231742859},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6661999821662903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.501800000667572},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.4982999861240387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47380000352859497},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C53471067","wikidata":"https://www.wikidata.org/wiki/Q7574076","display_name":"Spatial network","level":2,"score":0.4372999966144562},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4334999918937683},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.35580000281333923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C112953755","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph drawing","level":3,"score":0.3188000023365021},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3764924.3770894","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3764924.3770894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Intelligence for Smart and Connected Communities","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.08051","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.08051","pdf_url":"https://arxiv.org/pdf/2506.08051","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.08051","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.08051","pdf_url":"https://arxiv.org/pdf/2506.08051","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2554179569","https://openalex.org/W2808862972","https://openalex.org/W2945434604","https://openalex.org/W2964335123","https://openalex.org/W2966720510","https://openalex.org/W2989610764","https://openalex.org/W3092125438","https://openalex.org/W3095946637","https://openalex.org/W3115057995","https://openalex.org/W3127056839","https://openalex.org/W3207006537","https://openalex.org/W4244678544","https://openalex.org/W4292940387","https://openalex.org/W4312570539","https://openalex.org/W4315779632","https://openalex.org/W4328008037","https://openalex.org/W4360603550","https://openalex.org/W4382239616","https://openalex.org/W4388472067","https://openalex.org/W4390988128","https://openalex.org/W4396771716","https://openalex.org/W4398181022","https://openalex.org/W4401596232","https://openalex.org/W4402192457","https://openalex.org/W4403053017","https://openalex.org/W4403128425","https://openalex.org/W4404069928","https://openalex.org/W4404129120"],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"the":[1,121,187,199,209,223],"spatial":[2,48,112,212],"and":[3,19,37,46,71,97,131,164,174,217],"temporal":[4,132],"dynamics":[5],"of":[6,54,195,211],"automated":[7],"vehicle":[8],"(AV)":[9],"crash":[10,40,58,66,86,138,172,229],"severity":[11,41,173],"is":[12,123],"critical":[13],"for":[14],"advancing":[15],"urban":[16],"mobility":[17],"safety":[18],"infrastructure":[20],"planning.":[21],"In":[22],"this":[23],"work,":[24],"we":[25,74],"introduce":[26],"ST-GraphNet,":[27,180],"a":[28,51,81,99,141,183,192],"spatio-temporal":[29,95,225],"graph":[30,78,83,101,122,148],"neural":[31,149],"network":[32,150],"framework":[33],"designed":[34],"to":[35,170],"model":[36,202],"predict":[38,175],"AV":[39,228],"by":[42],"using":[43,140],"both":[44],"finegrained":[45,201],"region-aggregated":[47],"graphs.":[49],"Using":[50],"balanced":[52],"dataset":[53],"2,352":[55],"real-world":[56],"AV-related":[57],"reports":[59],"from":[60,137],"Texas":[61],"(2024),":[62],"including":[63,128,134],"geospatial":[64],"coordinates,":[65],"timestamps,":[67],"SAE":[68],"automation":[69],"levels,":[70],"narrative":[72],"descriptions,":[73],"construct":[75],"two":[76],"complementary":[77],"representations:":[79],"(1)":[80],"fine-grained":[82],"with":[84,125],"individual":[85],"events":[87],"as":[88,154],"nodes,":[89],"where":[90,102],"edges":[91],"are":[92,104],"defined":[93],"via":[94],"proximity;":[96],"(2)":[98],"coarse-grained":[100,188],"crashes":[103],"aggregated":[105],"into":[106],"Hexagonal":[107],"Hierarchical":[108],"Spatial":[109],"Indexing":[110],"(H3)-based":[111],"cells,":[113],"connected":[114],"through":[115],"hexagonal":[116],"adjacency.":[117],"Each":[118],"node":[119],"in":[120,221],"enriched":[124],"multimodal":[126],"data,":[127],"semantic,":[129],"spatial,":[130],"attributes,":[133],"textual":[135],"embeddings":[136],"narratives":[139],"pretrained":[142],"Sentence-BERT":[143],"model.":[144],"We":[145],"evaluate":[146],"various":[147],"(GNN)":[151],"architectures,":[152],"such":[153],"Graph":[155,159],"Convolutional":[156],"Networks":[157,161,168],"(GCN),":[158],"Attention":[160],"(GAT),":[162],"NodeFormer,":[163],"Dynamic":[165],"Hypergraph":[166],"Neural":[167],"(DHGNN),":[169],"classify":[171],"high-risk":[176],"regions.":[177],"Our":[178],"proposed":[179],"which":[181],"utilizes":[182],"DHGNN":[184],"backbone":[185],"on":[186],"H3":[189],"graph,":[190],"achieves":[191],"test":[193,204],"accuracy":[194],"98.81%,":[196],"substantially":[197],"outperforming":[198],"best":[200],"(61.4%":[203],"accuracy).":[205],"These":[206],"findings":[207],"highlight":[208],"effectiveness":[210],"aggregation,":[213],"dynamic":[214],"message":[215],"passing,":[216],"multi-modal":[218],"feature":[219],"integration":[220],"capturing":[222],"complex":[224],"patterns":[226],"underlying":[227],"severity.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2025-10-10T00:00:00"}