arXiv subjects

Paul Havard Duclos

Publications and source records attributed to Paul Havard Duclos.

2 recordsLinked to original sources

arXiv2026

Non-Exclusive Notifications for Ride-Hailing at Lyft II: Simulations and Marketplace Analysis

Ride-hailing platforms increasingly face uncertain driver acceptance, which makes traditional one-to-one \emph{exclusive dispatch} (ED) less efficient: rejections and timeouts force sequential retries and lengthen rider wait times, which in turn creates friction in the marketplace. \emph{Non-exclusive dispatch} (NED) mitigates this friction by broadcasting a request to multiple drivers in parallel. While NED can reduce latency, it introduces new design challenges---most notably, how to choose notification sets and how to resolve driver contention (when multiple drivers accept the same ride). In this paper---the second in a two-part collaboration with Lyft---we develop a theoretically grounded framework to evaluate the long-run performance and marketplace effects of transitioning from ED to NED. We bridge theory and practice by combining (i) an optimization model that formulates NED as a constrained welfare maximization problem with (ii) large-scale discrete-event simulations on proprietary Lyft traces and (iii) a stylized macroscopic equilibrium model. Across simulation and equilibrium analysis, we find that NED improves key fulfillment metrics relative to ED: it reduces match time (and hence rider reneging) while increasing both the number and the average quality of completed matches. We also quantify the speed--quality trade-off between two common contention resolution rules, \emph{First-Accept} and \emph{Best-Accept}: First-Accept maximizes speed and throughput, whereas Best-Accept is required to maximize per-match quality. Finally, we show that slightly conservative notification heuristics can improve long-run efficiency by avoiding excessive locking of high-value drivers and preserving future availability.

cs.GT↗

arXiv2026

Non-Exclusive Notifications for Ride-Hailing at Lyft I: Single-Cycle Approximation Algorithms

Ride-hailing platforms increasingly rely on non-exclusive notifications--broadcasting a single request to multiple drivers simultaneously--to mitigate inefficiencies caused by uncertain driver acceptance. In this paper, the first in a two-part collaboration with Lyft, we formally model the Notification Set Selection Problem for a single decision cycle, where the platform determines the optimal subset of drivers to notify for each incoming ride request. We analyze this combinatorial optimization problem under two contention-resolution protocols: First Acceptance (FA), which prioritizes speed by assigning the ride to the first responder, and Best Acceptance (BA), which prioritizes match quality by selecting the highest-valued accepting driver. We show that welfare maximization under both mechanisms is NP-hard, ruling out a polynomial-time exact algorithm unless P=NP. Despite this hardness, we establish several positive algorithmic results. For FA, we develop a Polynomial Time Approximation Scheme (PTAS) for the single-rider problem. For the general matching setting, we provide a (1-1/e)-approximation algorithm that applies to both FA and BA. Along the way, we show that the FA valuation function constitutes a novel discrete choice model with structural properties of independent interest. For BA, we further prove that the welfare objective is monotone and submodular and exploit additional structure to obtain an approximation guarantee that surpasses the classical (1-1/e) barrier. Finally, when acceptance probabilities are independent of the drivers, we show that the BA problem can be solved exactly in polynomial time through a linear programming formulation. We evaluate our algorithms on synthetic instances and on instances calibrated using real ride-sharing data from Lyft, demonstrating strong empirical performance.

cs.DS↗