{"type":"ScholarlyArticle","title":"DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks","authors":[{"name":"Yan Lin","slug":"yan-lin","given":"Yan","family":"Lin","affiliation":""},{"name":"Jilin Hu","slug":"jilin-hu","given":"Jilin","family":"Hu","affiliation":""},{"name":"Shengnan Guo","slug":"shengnan-guo","given":"Shengnan","family":"Guo","affiliation":""},{"name":"Christian S. Jensen","slug":"christian-s-jensen","given":"Christian S.","family":"Jensen","affiliation":""},{"name":"Youfang Lin","slug":"youfang-lin","given":"Youfang","family":"Lin","affiliation":""},{"name":"Huaiyu Wan","slug":"huaiyu-wan","given":"Huaiyu","family":"Wan","affiliation":""}],"abstract":"Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees.\r\nWe study the problem of time-varying microscopic weight completion. During a time slot, the available weights typically cover only some road segments. Weight completion recovers distributions for the weights of every road segment at the current time slot.\r\nThis problem involves two challenges: (i) contending with two layers of sparsity, where weights are missing at both the network layer (many road segments lack weights) and the segment layer (a segment may have insufficient weights to enable accurate distribution estimation); and (ii) achieving a weight distribution representation that is closed-form and can capture complex conditions flexibly, including heavy tails and multiple clusters.\r\n\r\nTo address these challenges, we propose DiSGMM that combines sparsity-aware embeddings with spatiotemporal modeling to leverage sparse known weights alongside learned segment properties and long-range correlations for distribution estimation. DiSGMM represents distributions of microscopic weights as learnable Gaussian mixture models, providing closed-form distributions capable of capturing complex conditions flexibly. Experiments on two real-world datasets show that DiSGMM can outperform state-of-the-art methods.","doi":"10.24963/ijcai.2026/315","url":"https://ijcai.org/proceedings/2026/315","pdf":"https://ijcai.org/proceedings/2026/0315.pdf","markdown":"https://ijcai.org/proceedings/2026/315.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/315","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":315,"pages":{"first":2834,"last":2842},"keywords":["Data Mining: Mining spatial and/or temporal data","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}