{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:26:49Z","timestamp":1784172409019,"version":"3.55.0"},"reference-count":167,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92470204"],"award-info":[{"award-number":["92470204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1109\/tbdata.2026.3668685","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T20:50:44Z","timestamp":1772225444000},"page":"1083-1101","source":"Crossref","is-referenced-by-count":4,"title":["A Survey of Large Language Models for Traffic Forecasting: Methods and Applications"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3520-0129","authenticated-orcid":false,"given":"Qingqing","family":"Long","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9566-3127","authenticated-orcid":false,"given":"Shuai","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2092-3841","authenticated-orcid":false,"given":"Ning","family":"Cao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2528-2435","authenticated-orcid":false,"given":"Zhicheng","family":"Ren","sequence":"additional","affiliation":[{"name":"Aurora Innovation, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-3714","authenticated-orcid":false,"given":"Xiao","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Wisconsin&#x2013;Madison, Madison, WI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9657-951X","authenticated-orcid":false,"given":"Wei","family":"Ju","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6382-1960","authenticated-orcid":false,"given":"Chen","family":"Fang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4530-5516","authenticated-orcid":false,"given":"Zhihong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model","author":"Liu","year":"2024"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-09422-z"},{"key":"ref3","article-title":"Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding","author":"Wu","year":"2024"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0493"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2023.08.012"},{"key":"ref6","article-title":"Measuring massive multitask language understanding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref7","article-title":"SciRerankBench: Benchmarking rerankers towards scientific retrieval-augmented generated LLMs","author":"Chen","year":"2025"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01438"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40231-1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1803"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref12","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.890"},{"key":"ref14","article-title":"Large language model agent: A survey on methodology, applications and challenges","author":"Luo","year":"2025"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1800"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.350"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-45427-4"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.424"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2922597"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3678717.3691232"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-78148-1"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-24469-y"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41932-6"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2018.2806634"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033614"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-05896-x"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/s20247209"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.009"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709379"},{"key":"ref30","article-title":"Urban generative intelligence (UGI): A foundational platform for agents in embodied city environment","author":"Xu","year":"2023"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27758"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2024.102153"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1141"},{"issue":"70","key":"ref34","first-page":"1","article-title":"Scaling instruction-finetuned language models","volume":"25","author":"Chung","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1177\/03611981251367699"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3469578"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679628"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403150"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3390\/app14177455"},{"key":"ref40","article-title":"Towards urban general intelligence: A review and outlook of urban foundation models","author":"Zhang","year":"2024"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671453"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3913"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671992"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671578"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657840"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-industry.104"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00698"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.commtr.2024.100150"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3508471"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref51","article-title":"RoBERTa: A robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"issue":"8","key":"ref52","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref53","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref54","article-title":"Vicuna: An open-source chatbot impressing GPT-4 with 90%* chatGPT quality","author":"Chiang","year":"2023"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3821637"},{"key":"ref56","article-title":"Deep learning for trajectory data management and mining: A survey and beyond","author":"Chen","year":"2024"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671451"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-65668-2_20"},{"key":"ref59","article-title":"Large language models for forecasting and anomaly detection: A systematic literature review","author":"Su","year":"2024"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102786"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3528116"},{"key":"ref62","article-title":"Position: What can large language models tell us about time series analysis","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Jin","year":"2024"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2840722"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2024.03.006"},{"key":"ref65","article-title":"TPLLM: A traffic prediction framework based on pretrained large language models","author":"Ren","year":"2024"},{"key":"ref66","article-title":"TrafficGPT: Towards multi-scale traffic analysis and generation with spatial-temporal agent framework","author":"Ouyang","year":"2024"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/tiv.2024.3484528"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/MDM61037.2024.00025"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC58415.2024.10920138"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.commtr.2025.100170"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3678717.3691308"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CLNLP64123.2024.00026"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3342137"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3418522"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CAI59869.2024.00277"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3557915.3561026"},{"key":"ref77","article-title":"Where would I go next? Large language models as human mobility predictor","author":"Wang","year":"2023"},{"key":"ref78","article-title":"Beyond imitation: Generating human mobility from context-aware reasoning with large language models","author":"Shao","year":"2024"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3626357"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/MDM61037.2024.00060"},{"key":"ref81","article-title":"Urbandit: A foundation model for open-world urban spatio-temporal learning","author":"Yuan","year":"2024"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736878"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.98"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737375"},{"key":"ref85","article-title":"Large language model for participatory urban planning","author":"Zhou","year":"2024"},{"key":"ref86","article-title":"Planning, living and judging: A multi-agent LLM-based framework for cyclical urban planning","author":"Ni","year":"2024"},{"key":"ref87","article-title":"PlanGPT: Enhancing urban planning with tailored language model and efficient retrieval","author":"Zhu","year":"2024"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645378"},{"key":"ref89","article-title":"From single agent to multi-agent: Improving traffic signal control","author":"Tislenko","year":"2024"},{"key":"ref90","article-title":"LLM-assisted light: Leveraging large language model capabilities for human-mimetic traffic signal control in complex urban environments","author":"Wang","year":"2024"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/JRFID.2024.3384289"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2026.3674284"},{"key":"ref93","article-title":"TrafficsafetyGPT: Tuning a pre-trained large language model to a domain-specific expert in transportation safety","author":"Zheng","year":"2023"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2024.11.009"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1145\/3681765.3698467"},{"key":"ref96","article-title":"ChatGPT is on the horizon: Could a large language model be suitable for intelligent traffic safety research and applications?","author":"Zheng","year":"2023"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645620"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/896"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.147"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1201\/9781003616719-7"},{"key":"ref101","first-page":"18546","article-title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","volume":"2024","author":"Cao","year":"2024"},{"key":"ref102","article-title":"Towards graph contrastive learning: A survey and beyond","author":"Ju","year":"2024"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"ref104","article-title":"Lora: Low-rank adaptation of large language models","author":"Hu","year":"2021"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2338"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115738"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106207"},{"key":"ref108","article-title":"OpenCity: A scalable platform to simulate urban activities with massive LLM agents","author":"Yan","year":"2024"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1145\/3369871"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00101"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/508"},{"key":"ref112","article-title":"Multivariate time-series imputation with disentangled temporal representations","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.07.140"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-4067-x"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3983-7"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-022-3825-4"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02190-8"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2474"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0861"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3882"},{"key":"ref121","article-title":"Enhancing traffic prediction with textual data using large language models","author":"Huang","year":"2024"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE65448.2025.00334"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2474"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3957"},{"key":"ref125","article-title":"Using large language models in public transit systems, San Antonio as a case study","author":"Jonnala","year":"2024"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2018.01.001"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1145\/3681771.3699917"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1145\/3331651.3331653"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1080\/17538947.2021.1952324"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v26i1.8212"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2797937"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.04.005"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102916"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1141"},{"key":"ref135","article-title":"Genai-powered multi-agent paradigm for smart urban mobility: Opportunities and challenges for integrating large language models (LLMs) and retrieval-augmented generation (RAG) with intelligent transportation systems","author":"Xu","year":"2024"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.3390\/vehicles7010011"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1145\/3385809"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115190"},{"key":"ref139","article-title":"Enhancing traffic prediction with textual data using large language models","author":"Huang","year":"2024"},{"key":"ref140","article-title":"FSTLLM: Spatio-temporal LLM for few shot time series forecasting","volume-title":"Proc. 42nd Int. Conf. Mach. Learn.","author":"Jiang","year":"2025"},{"key":"ref141","article-title":"$s^{2}$s2 ip-llm: Semantic space informed prompt learning with llm for time series forecasting","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Pan","year":"2024"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1922"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34082"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1877"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645434"},{"key":"ref146","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wu","year":"2021"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.13136"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-022-00748-y"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-04067-5"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.14778\/3430915.3430924"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1145\/3610904"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1177\/03611981211039843"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1145\/3381006"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2022.867046"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2024.3395350"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1145\/3511904"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3215326"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3570705"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1145\/3768163"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1145\/3709153"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.52202\/075280-3293"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2021.03.007"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482000"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1145\/3532611"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.52202\/075280-3060"},{"key":"ref166","first-page":"2021","article-title":"Reinforcement learning benchmarks for traffic signal control","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Ault"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i10.33163"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/11603878\/11415630.pdf?arnumber=11415630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:06:21Z","timestamp":1783973181000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11415630\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":167,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2026.3668685","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.174495271.19469544\/v1","asserted-by":"object"}]},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]}}}