Putting Ridesharing to the Test: Efficient and Scalable Solutions and the Power of Dynamic Vehicle Relocation
This work addresses the problem of efficient and scalable ridesharing optimization for platforms, drivers, and passengers, though it appears incremental as it builds on existing algorithmic components.
The paper tackles the optimization of large-scale, real-time ridesharing systems by proposing a modular design methodology called Component Algorithms for Ridesharing (CAR), evaluating 14 algorithms across 12 metrics and demonstrating that lightweight relocation schemes can improve Quality of Service by up to 50%.
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.