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

arXiv: https://arxiv.org/abs/1912.08066 · HTML: https://mlsanity.com/paper/1912.08066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Can Vibe Coding Beat Graduate CS Students? An LLM vs. Human Coding Tournament on Market-driven Strategic Planning](https://mlsanity.com/paper/2511.20613/) (2025)
- [Improving Multi-agent Coordination by Learning to Estimate Contention](https://mlsanity.com/paper/2105.04027/) (2021)
- [JointMatch: A Unified Heterogeneous Graph Neural Solver for Large-Scale Ride-Sharing Matching](https://mlsanity.com/paper/2609.20200/) (2026)

## Abstract (arXiv)

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.