# A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency

arXiv: https://arxiv.org/abs/2302.14358 · HTML: https://mlsanity.com/paper/2302.14358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Design and Analysis of Switchback Experiments](https://mlsanity.com/paper/2009.00148/) (2020)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Time series experiments and causal estimands: exact randomization tests and trading](https://mlsanity.com/paper/1706.07840/) (2017)
- [Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation](https://mlsanity.com/paper/1907.06584/) (2019)
- [Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms](https://mlsanity.com/paper/2105.08791/) (2021)
- [Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms](https://mlsanity.com/paper/2102.05805/) (2021)
- [Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace](https://mlsanity.com/paper/2202.05118/) (2022)
- [Spatio-Temporal Pricing for Ridesharing Platforms](https://mlsanity.com/paper/1801.04015/) (2018)
- [Learn to Earn: Enabling Coordination within a Ride Hailing Fleet](https://mlsanity.com/paper/2006.10904/) (2020)
- [Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ridesharing Platforms](https://mlsanity.com/paper/2104.14740/) (2021)
- [Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2211.03240/) (2022)

## Built on by (most cited first, 1 of 1)

- [GeOT: A spatially explicit framework for evaluating spatio-temporal predictions](https://mlsanity.com/paper/2410.11709/) (2024)

## Abstract (arXiv)

Ridesharing platforms are a type of two-sided marketplace where ``supply-demand balance'' is critical for market efficiency and yet is complex to define and analyze. We present a unified analytical framework based on the graph-based equilibrium metric (GEM) for quantifying the supply-demand spatiotemporal state and efficiency of a ridesharing marketplace. GEM was developed as a generalized Wasserstein distance between the supply and demand distributions in a ridesharing market and has been used as an evaluation metric for algorithms expected to improve supply-demand alignment. Building upon GEM, we develop SD-GEM, a dual-perspective (supply- and demand-side) representation of rideshare market equilibrium. We show that there are often disparities between the two views and examine how this dual-view leads to the notion of market efficiency, in which we propose novel statistical tests for capturing improvement and explaining the underlying driving factors.