Optimizing Demand-Responsive Paratransit Operations: A Mixed Integer Programming Approach

TL;DR

This paper develops a mixed integer programming model to optimize demand-responsive paratransit operations, aiming to reduce waiting times and costs, and proposes a new Uber-like service model for non-emergency medical transportation.

Contribution

It introduces a novel optimization framework for demand-responsive paratransit, integrating public and ride-sharing options to enhance service quality and efficiency.

Findings

Reduced rider waiting times in simulations

Lowered operational costs through optimized routing

Potential for improved service accessibility

Abstract

The traditional demand-responsive paratransit system plays an important role in connecting people to health care, particularly those who are carless, low-income, senior, underinsured/uninsured, or who have a disability. However, the existing paratransit system usually has low service quality due to long waiting times, low operation frequencies, and high costs. In order to improve the service quality, we propose to design a new demand-responsive paratransit system that offers public, Uber-like options for non-emergency medical transportation. Mixed integer programming models are thus developed to optimize the system operations with the objectives of minimizing user waiting times for riders as well as operating costs for operators. The results produced in this paper will assist local departments of transportation and transit agencies as they consider operational strategies to meet non-emergency medical transport needs.