Optimal Micro-Transit Zoning via Clique Generation and Integer Programming

2025-09-14

AI Summary

This study addresses the microtransit service area optimization problem under budgetary and policy constraints. We propose a two-stage partitioning method based on a sharedness graph model: first, we innovatively adapt the sharedness graph—originally developed for dynamic ridepooling—to static spatial partitioning, introducing a diameter-constrained geographical proximity criterion; second, we design an improved clique-generation algorithm to efficiently enumerate large-scale candidate service areas in Stage I, and formulate a weighted maximum coverage integer program to select the optimal subset in Stage II. Evaluated on real-world data from Chattanooga, our approach improves demand coverage by 27.03% over baseline methods; experiments on synthetic datasets demonstrate up to 49.5% improvement. The method significantly enhances urban mobility service efficiency and resource–demand alignment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial 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: The sharing economy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Micro-transit services offer a promising solution to enhance urban mobility and access, particularly by complementing existing public transit. However, effectively designing these services requires determining optimal service zones for these on-demand shuttles, a complex challenge often constrained by operating budgets and transit agency priorities. This paper presents a novel two-phase algorithmic framework for designing optimal micro-transit service zones based on the objective of maximizing served demand. A key innovation is our adaptation of the shareability graph concept from its traditional use in dynamic trip assignment to the distinct challenge of static spatial zoning. We redefine shareability by considering geographical proximity within a specified diameter constraint, rather than trip characteristics. In Phase 1, the framework employs a highly scalable algorithm to generate a comprehensive set of candidate zones. In Phase 2, it formulates the selection of a specified number of zones as a Weighted Maximum Coverage Problem, which can be efficiently solved by an integer programming solver. Evaluations on real-world data from Chattanooga, TN, and synthetic datasets show that our framework outperforms a baseline algorithm, serving 27.03% more demand in practice and up to 49.5% more demand in synthetic settings.

Problem

• Designing optimal micro-transit service zones
• Maximizing served demand under budget constraints
• Adapting shareability graphs for spatial zoning

Innovation

• Two-phase algorithmic framework for zoning
• Shareability graph adaptation with geographical proximity
• Integer programming solving Weighted Maximum Coverage