Putting Ridesharing to the Test: Efficient and Scalable Solutions and the Power of Dynamic Vehicle Relocation

TL;DR

This paper presents a comprehensive evaluation of modular algorithms for large-scale, real-time ridesharing, demonstrating scalable solutions and the significant benefits of dynamic vehicle relocation in improving service quality.

Contribution

It introduces a modular design methodology (CAR) for ridesharing optimization, evaluates 14 diverse algorithms across multiple metrics, and highlights the effectiveness of lightweight relocation schemes.

Findings

Lightweight relocation schemes can improve Quality of Service by up to 50%.

Identifies scalable, on-device algorithms that perform well across various metrics.

Provides the largest, most comprehensive evaluation of ridesharing algorithms to date.

Abstract

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 12 metrics related to global efficiency, complexity, passenger, driver, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, driver or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to $50\%$, and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.