Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

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 10 metrics related to global efficiency, complexity, passenger, 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, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to $$50\%$$ 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers

“…A key area of research in this field revolves around designing matching policies that effectively pair drivers with riders. Several studies have been conducted on this topic, see, e.g., (Danassis et al, 2022;Curry et al, 2019;Ashlagi et al, 2019;Lowalekar et al, 2018;Bei & Zhang, 2018;Dickerson et al, 2018;Zhao et al, 2019;Tong et al, 2021). Most of the existing work in this domain focuses on either enhancing system efficiency or improving user satisfaction, or both.…”

Exploring Computational Complexity Of Ride-Pooling Problems

“…Most notably: Alonso-Mora et al,2017 (11), Santi et al (10) and de Ruijter et al 2020 (12), they either (a) only consider the limited number of shared ride requests within the same time-window, (b) only consider the search space of lower degree with less combinatorial shared trips without performing efficient graph search, (c) only relying on the heuristics which limit the potential of ride-sharing in real-world scenarios. While most ride-pooling solutions (13) apply a real-time approach when requests are not known in advance but appear in real-time. In practice, requests are often 'batched' in the fixed interval (e.g., 5 or 10 minutes) and pooled later for efficiency.…”