A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 28 Feb 2023.
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15 of 37 references.
References · 15
- 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
- 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
- 2020Design and Analysis of Switchback Experimentscited by 37
- 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
- 2019Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matchingcited by 26
- 2021Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learningcited by 22
- 2017Time series experiments and causal estimands: exact randomization tests and tradingcited by 21
- 2019Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendationcited by 16
- 2021Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platformscited by 16
- 2021Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platformscited by 11
- 2022Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplacecited by 11
- 2018Spatio-Temporal Pricing for Ridesharing Platformscited by 7
3 more
- 2020Learn to Earn: Enabling Coordination within a Ride Hailing Fleetcited by 3
- 2021Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ridesharing Platformscited by 2
- 2022Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learningcited by 2
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