{"type":"ScholarlyArticle","title":"PROB-EMOE: A Probabilistic Ensemble Mixture-of-Experts Framework for Metro Network Expansion Forecasting","authors":[{"name":"Fangyi Ding","slug":"fangyi-ding","given":"Fangyi","family":"Ding","affiliation":""},{"name":"Zhan Zhao","slug":"zhan-zhao","given":"Zhan","family":"Zhao","affiliation":""},{"name":"Zhi Li","slug":"zhi-li","given":"Zhi","family":"Li","affiliation":""},{"name":"Xudong Guo","slug":"xudong-guo","given":"Xudong","family":"Guo","affiliation":""},{"name":"Ning Zhang","slug":"ning-zhang","given":"Ning","family":"Zhang","affiliation":""},{"name":"Yamin Wang","slug":"yamin-wang","given":"Yamin","family":"Wang","affiliation":""},{"name":"Yihong Tang","slug":"yihong-tang","given":"Yihong","family":"Tang","affiliation":""}],"abstract":"Forecasting Origin-Destination (OD) demand for new metro lines is critical for sustainable infrastructure planning but faces spatiotemporal out-of-distribution challenges. Existing models often struggle to capture heterogeneous interaction patterns in changing topologies and overlook inherent uncertainty and over-dispersion issues. To bridge these gaps, we propose PROB-EMOE, a planning-oriented probabilistic framework tailored for network expansion. To ensure robust generalization, we design a Mixture-of-Experts (MoE) predictor that integrates diverse expert views to capture heterogeneous demand patterns across changing topologies. To quantify extrapolation uncertainty, our framework functions as a unified probabilistic system by synergizing Deep Ensembles with a probabilistic output, effectively quantifying both data and model uncertainty. Through a systematic investigation of likelihood families, we empirically demonstrate that the Negative Binomial distribution offers the optimal fit in this context. Extensive experiments on a multi-year Shenzhen metro dataset demonstrate that our approach achieves state-of-the-art predictive performance and provides the sharpest calibrated uncertainty intervals. The framework has been deployed in a metropolitan smart-data platform to support risk-aware investment decisions.","doi":"10.24963/ijcai.2026/792","url":"https://ijcai.org/proceedings/2026/792","pdf":"https://ijcai.org/proceedings/2026/0792.pdf","markdown":"https://ijcai.org/proceedings/2026/792.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/792","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":"AI and Social Good","track":"AI and Social Good","number":792,"pages":{"first":7120,"last":7128},"keywords":["Uncertainty in AI: Uncertainty in AI","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Data Mining: Data Mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}