{"doi":"10.1609/aaai.v37i8.26121","title":"Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty","abstract":"This paper addresses the challenges in accurate and real-time traffic congestion prediction under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction framework that delivers higher accuracy and lower latency than SOTA solutions. While traditional solutions face the dilemma from the trade-off between algorithm complexity and computational efficiency, our Ising-based method breaks away from the trade-off leveraging the Ising model's strong expressivity and the Ising machine's strong computation power. In particular, Ising-Traffic formulates traffic prediction under uncertainty into two Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machines and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98X speedups and 5% accuracy improvement over SOTA.","journal":"Proceedings of the AAAI Conference on Artificial Intelligence","year":2023,"id":389511,"datarank":0.519860385419959,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":0.0,"self_endowment_contribution":0.519860385419959,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9519,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1159895,"name":"Anshujit Sharma","orcid":"0000-0003-2025-0392","position":1,"is_corresponding":false},{"id":1160684,"name":"Jerry Yao-Chieh Hu","orcid":null,"position":2,"is_corresponding":false},{"id":1159896,"name":"Zhuo Liu","orcid":"0000-0003-0961-7149","position":3,"is_corresponding":false},{"id":456802,"name":"Ang Li","orcid":"0000-0003-3734-9137","position":4,"is_corresponding":false},{"id":1159897,"name":"Liu Han","orcid":"0000-0002-2008-4489","position":5,"is_corresponding":false},{"id":1159898,"name":"Michael Huang","orcid":"0000-0001-9799-2920","position":6,"is_corresponding":false},{"id":1160685,"name":"Tony Geng","orcid":null,"position":7,"is_corresponding":false},{"id":1159894,"name":"Zhenyu Pan","orcid":"0009-0009-2805-6018","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:18:27.054415Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}