# Ridesharing with Driver Location Preferences

## TL;DR

This paper develops a mechanism for ridesharing platforms that accounts for drivers' location preferences, incentivizing truthful reporting and consistent service, thereby improving revenue and efficiency especially under supply constraints.

## Contribution

It introduces a novel mechanism that incentivizes truthful preference reporting and service provision, achieving near-optimal revenue in complex demand-supply scenarios.

## Key findings

- Mechanism achieves first-best revenue with unconstrained supply.
- Simulation shows improved performance over existing mechanisms.
- Mechanism maintains high efficiency under supply constraints.

## Abstract

We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.