{"type":"ScholarlyArticle","title":"Column Generation for the Micro-Transit Zoning Problem","authors":[{"name":"Hins Hu","slug":"hins-hu","given":"Hins","family":"Hu","affiliation":"Cornell University"},{"name":"Rishav Sen","slug":"rishav-sen","given":"Rishav","family":"Sen","affiliation":"Vanderbilt University"},{"name":"Jose Paolo Talusan","slug":"jose-paolo-talusan","given":"Jose Paolo","family":"Talusan","affiliation":"Vanderbilt University"},{"name":"Abhishek Dubey","slug":"abhishek-dubey","given":"Abhishek","family":"Dubey","affiliation":"Vanderbilt University"},{"name":"Aron Laszka","slug":"aron-laszka","given":"Aron","family":"Laszka","affiliation":"Pennsylvania State University"},{"name":"Samitha Samaranayake","slug":"samitha-samaranayake","given":"Samitha","family":"Samaranayake","affiliation":"Cornell University"}],"abstract":"Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the gap between fixed-line mass transit and single-request ride-hailing, balancing ridership maximization and travel time minimization. Micro-transit adoption can have significant social impact. It improves urban sustainability, through lower energy consumption and reduced emissions, while enhancing equitable mobility access for disadvantaged communities, thanks to its lower vehicle miles per passenger, flexible schedules, and affordable pricing. However, effective operation of micro-transit services requires planning geo-fenced zones in advance, which involves solving a challenging combinatorial optimization problem. Existing approaches enumerate candidate zones first and selects a fixed number of optimal zones in the second step. In this paper, we generalize the Micro-Transit Zoning Problem (MZP) to allow a global budget rather than imposing a size limit for candidate zones. We also design a Column Generation (CG) framework to solve the problem exactly and several pricing heuristics to accelerate computation. Extensive numerical experiments across major U.S. cities demonstrate that our approach produces higher-quality solutions more efficiently and scales better in the generalized setting.","doi":"10.24963/ijcai.2026/803","url":"https://ijcai.org/proceedings/2026/803","pdf":"https://ijcai.org/proceedings/2026/0803.pdf","markdown":"https://ijcai.org/proceedings/2026/803.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/803","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":803,"pages":{"first":7219,"last":7228},"keywords":["Planning and Scheduling: Planning and Scheduling","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Constraint Satisfaction and Optimization: Constraint Satisfaction and Optimization"],"videos":[],"references":[{"position":1,"raw":"[Bardaka et al., 2024] Eleni Bardaka, Pascal Van Hentenryck, Crystal Chen Lee, Christopher B Mayhorn, Kai Monast, Samitha Samaranayake, and Munindar P Singh. 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