{"type":"ScholarlyArticle","title":"ORBIT: Optimal Recommendation Framework for Boarding with Interpretable Timelines","authors":[{"name":"Joonseong Kang","slug":"joonseong-kang","given":"Joonseong","family":"Kang","affiliation":""},{"name":"Seung Ha Hwang","slug":"seung-ha-hwang","given":"Seung Ha","family":"Hwang","affiliation":""},{"name":"Jaehun Bang","slug":"jaehun-bang","given":"Jaehun","family":"Bang","affiliation":""},{"name":"Jiyoung Ko","slug":"jiyoung-ko","given":"Jiyoung","family":"Ko","affiliation":""},{"name":"Subeen Park","slug":"subeen-park","given":"Subeen","family":"Park","affiliation":""},{"name":"Jeffrey Gennari","slug":"jeffrey-gennari","given":"Jeffrey","family":"Gennari","affiliation":""}],"abstract":"Determining when to leave for the airport is a complex problem shaped by flight delay risk, traffic, weather, airport congestion, and passenger-specific constraints. Existing services rely on isolated delay estimates or simple travel time calculations, failing to capture real-time context. We propose ORBIT, a decision-support system that generates personalized leave-by recommendations by integrating predictive models with real-time operational signals. The system combines user input normalization, real-time data acquisition, Transformer-based delay prediction, and LLM-based reasoning to jointly account for statistical forecasts and dynamic factors like congestion, previous-leg propagation, weather, traffic, and airport processing times. Instead of a standalone delay estimate, ORBIT produces an actionable and interpretable departure plan. We implement ORBIT as an interactive system and demonstrate its applicability. A video demo is available at https://youtu.be/fZX42jceIM4.","doi":"10.24963/ijcai.2026/971","url":"https://ijcai.org/proceedings/2026/971","pdf":"https://ijcai.org/proceedings/2026/0971.pdf","markdown":"https://ijcai.org/proceedings/2026/971.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/971","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":"Demo Track","track":"Demo Track","number":971,"pages":{"first":8423,"last":8426},"keywords":["AI: Multidisciplinary Topics and Applications","AI: Planning and Scheduling"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}