Column Generation for the Micro-Transit Zoning Problem

TL;DR

This paper introduces a column generation approach to optimize micro-transit zoning, allowing flexible budgets and improving solution quality and scalability for urban mobility planning.

Contribution

It generalizes the Micro-Transit Zoning Problem to include a global budget and develops a column generation framework with heuristics for efficient solutions.

Findings

Higher-quality solutions compared to existing methods

More efficient computation and better scalability

Effective in diverse U.S. city scenarios

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.