Incorporating equity in the vehicle rebalancing operations of dockless micromobility services

Abstract

Dockless micromobility services, including shared bicycles and scooters, are emerging as sustainable travel alternatives in many cities. The optimal operation of these services, however, often depends on rebalancing operations that redistribute micromobility vehicles to service area locations with less than desired vehicle levels. Existing rebalancing models typically prioritize operational efficiency or business objectives, such as relocating vehicles to maximize served demand or profits. This study contributes a rebalancing model that incorporates the goal of improving equity-in-access to dockless micromobility through rebalancing operations. Specifically, a two-step approach is proposed to optimize the rebalancing operations of dockless micromobility services according to efficiency and equity objectives. In the first step, an optimization model is used to find micromobility vehicle distributions that maximize system-level efficiency and equity performance indicators across a specified time horizon. In the second step, a multi-objective pick-up and delivery problem is used to develop vehicle relocation plans aimed at achieving the optimal distributions determined in the first step. Numerical examples are presented to illustrate the application of the proposed methods. As part of the numerical tests, machine learning-based models trained using real-world data were shown to accurately predict equity-based performance indicators for a dockless e-scooter service in Puerto Rico.