{"id":"https://openalex.org/W4417283660","doi":"https://doi.org/10.1145/3748636.3763215","title":"TrafficNetQA: Question Answering Datasets for Evaluating LLM Performance on Traffic Network Files","display_name":"TrafficNetQA: Question Answering Datasets for Evaluating LLM Performance on Traffic Network Files","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4417283660","doi":"https://doi.org/10.1145/3748636.3763215"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3763215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763215","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763215","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763215","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Donghoon Kwon","orcid":"https://orcid.org/0009-0000-4325-4527"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donghoon Kwon","raw_affiliation_strings":["University of Minnesota, Minneapolis, Minnesota, USA"],"raw_orcid":"https://orcid.org/0009-0000-4325-4527","affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008022291","display_name":"Seungmo Kang","orcid":"https://orcid.org/0000-0002-9435-5835"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungmo Kang","raw_affiliation_strings":["Korea University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-9435-5835","affiliations":[{"raw_affiliation_string":"Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082634431","display_name":"Seongjin Choi","orcid":"https://orcid.org/0000-0001-7140-537X"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seongjin Choi","raw_affiliation_strings":["University of Minnesota, Minneapolis, Minnesota, USA"],"raw_orcid":"https://orcid.org/0000-0001-7140-537X","affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36529151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1142","last_page":"1145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.25870001316070557,"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":0.25870001316070557,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.14630000293254852,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.08940000087022781,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6455000042915344},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5634999871253967},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5296000242233276},{"id":"https://openalex.org/keywords/xml","display_name":"XML","score":0.5252000093460083},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4837000072002411}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7588000297546387},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6455000042915344},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5634999871253967},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C8797682","wikidata":"https://www.wikidata.org/wiki/Q2115","display_name":"XML","level":2,"score":0.5252000093460083},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47040000557899475},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42489999532699585},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4018999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C171730128","wikidata":"https://www.wikidata.org/wiki/Q5227290","display_name":"Data file","level":2,"score":0.2687999904155731}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3763215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763215","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763215","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748636.3763215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3763215","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3763215","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1174314008","display_name":null,"funder_award_id":"RS-2021-NR059048","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G30685149","display_name":null,"funder_award_id":"BK21 FOUR","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G8922690072","display_name":null,"funder_award_id":"RS-2020-NR049594","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G933291084","display_name":null,"funder_award_id":"RS-2020-NR049594","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417283660.pdf","grobid_xml":"https://content.openalex.org/works/W4417283660.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W2903709398","https://openalex.org/W4392798406"],"related_works":[],"abstract_inverted_index":{"We":[0,131],"propose":[1],"TrafficNetQA,":[2],"a":[3,97],"benchmark":[4],"to":[5,26,33,66,72,88,153],"quantitatively":[6],"evaluate":[7],"how":[8],"well":[9],"Large":[10],"Language":[11],"Models":[12],"(LLMs)":[13],"understand":[14,74],"transportation-related":[15],"traffic":[16,27,123],"networks.":[17],"While":[18],"there":[19],"is":[20],"increasing":[21],"attention":[22],"on":[23],"applying":[24],"LLMs":[25,42,60],"operation":[28],"and":[29,109,125,144,146,164],"management,":[30],"their":[31,80,151],"ability":[32,152],"reason":[34],"over":[35],"structured":[36],"network":[37,110,156,165],"data":[38],"remains":[39],"underexplored.":[40],"As":[41],"are":[43,61,171],"being":[44],"positioned":[45],"as":[46,77],"agents":[47],"in":[48,115,150,168],"complex":[49],"decision-making":[50,81],"contexts,":[51],"this":[52,90,94,169],"understanding":[53],"becomes":[54],"increasingly":[55],"critical.":[56],"In":[57],"other":[58],"words,":[59],"now":[62],"expected":[63],"not":[64],"only":[65],"perform":[67],"task-specific":[68],"execution,":[69],"but":[70],"also":[71],"structurally":[73],"road":[75],"networks":[76,113],"part":[78],"of":[79,136],"role.":[82],"However,":[83],"no":[84],"standard":[85],"dataset":[86,101],"exists":[87],"assess":[89],"ability.":[91],"TrafficNetQA":[92],"bridges":[93],"gap":[95],"through":[96],"question":[98],"answering":[99],"(QA)":[100],"covering":[102],"three":[103],"areas:":[104],"basic":[105],"feature":[106],"retrieval,":[107],"pathfinding,":[108],"comprehension.":[111],"The":[112],"used":[114,167],"our":[116],"experiments":[117],"include":[118],"XML":[119],"files":[120,127,157,166],"from":[121,129],"the":[122,133],"simulation(SUMO)":[124],"OSM":[126],"extracted":[128],"OpenStreetMap.":[130],"benchmarked":[132],"zero-shot":[134],"capabilities":[135],"state-of-the-art":[137],"models":[138],"including":[139],"Llama,":[140],"Gemma,":[141],"Qwen,":[142],"GPT,":[143],"Gemini,":[145],"found":[147],"clear":[148],"limitations":[149],"interpret":[154],"raw":[155],"without":[158],"explicit":[159],"guidance.":[160],"All":[161],"QA":[162],"datasets":[163],"research":[170],"released":[172],"at":[173],"https://github.com/UMN-Choi-Lab/TrafficNetQA.":[174]},"counts_by_year":[],"updated_date":"2026-06-20T22:02:38.213706","created_date":"2025-12-12T00:00:00"}