# NERO: Nested Rebalancing Optimization for Mobility on Demand

## TL;DR

This paper introduces a scalable hierarchical rebalancing optimization method for Mobility-on-Demand services, significantly reducing computational complexity while maintaining service quality, demonstrated on real-world taxi data.

## Contribution

A novel hierarchical rebalancing approach that exponentially decreases computational complexity with layered regions, suitable for large-scale MoD systems.

## Key findings

- Reduces computational time with more layers
- Maintains bounded error in rebalancing accuracy
- Effective on real-world taxi trip data

## Abstract

Mobility-on-Demand (MoD) services, such as taxi-like services, are promising applications. Rebalancing the vehicle locations against customer requests is a key challenge in the services because imbalance between the two worsens service quality (e.g., longer waiting times). Previous work would be hard to apply to large-scale MoD services because of the computational complexity. In this study, we develop a scalable approach to optimize rebalancing policy in stages from coarse regions to fine regions hierarchically. We prove that the complexity of our method decreases exponentially with increasing number of layers, while the error is bounded. We numerically confirmed that the method reduces computational time by increasing layers with a little extra travel time using a real-world taxi trip dataset.