Column Generation for the Micro-Transit Zoning Problem

2026-03-08

AI Summary

This study addresses the combinatorial optimization challenge of geofenced zone design in microtransit services, where existing approaches are constrained by a fixed number and size of candidate zones. To overcome these limitations, this work proposes a generalized zonal design model with a global budget constraint that eliminates the need for pre-specifying the number of zones. It further introduces, for the first time, a column generation framework to tackle this problem. Customized pricing heuristics are developed to accelerate convergence, significantly enhancing both solution quality and algorithmic scalability. Extensive experiments across multiple major U.S. cities demonstrate that the proposed method efficiently generates high-quality zoning plans and exhibits superior performance under more general and realistic settings.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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 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.

Problem

• Micro-Transit Zoning
• Combinatorial Optimization
• Column Generation
• Urban Mobility
• On-demand Transit

Innovation

• Column Generation
• Micro-Transit Zoning
• Combinatorial Optimization
• Pricing Heuristics
• Global Budget Constraint