Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia

TL;DR

This paper presents a constraint programming model for optimizing paratransit route planning and scheduling, demonstrating improved service levels and practical benefits in a Savannah case study.

Contribution

It introduces a novel CP-based approach for joint route and shift optimization, offering a practical, flexible, and competitive alternative to AI methods.

Findings

Significantly increases requests served compared to current practices.

Achieves a 5% improvement by flexible shift start times.

Demonstrates competitiveness with AI-accelerated frameworks.

Abstract

Paratransit services are vital for individuals who cannot use fixed-route public transit, including those with disabilities. Optimizing these services is essential for transit agencies to deliver high-quality service efficiently. This paper introduces a Constraint Programming (CP) model to jointly optimize route planning and shift scheduling for paratransit operations, along with practical guidance for real-world implementation. A case study in Savannah, Georgia, demonstrates that the new approach is competitive with a recently proposed, highly effective AI-accelerated column generation framework, and significantly increases the number of requests served compared to current practices. The method is also easier to implement and provides an inherently practical solution for transportation planners. CP further provides the flexibility to optimize schedules without requiring shifts to start exactly on the hour, yielding an additional 5% improvement in the number of requests served.