Research map / Bandits and online learning / Online convex optimization and games
Real-Time Driver-Request Assignment in Ridesourcing
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use threshold-based real-time waiting and matching decisions that account for driver arrivals and request quitting | MULTI-REQ · Synthetic and real-world ridesourcing data | empirical competitive ratio: 10% - 20% | Helped |
| Use adaptive real-time assignment instead of fixed-window batch matching | MULTI-REQ · Synthetic data, especially when CL and TL are small | empirical competitive ratio | Helped |
| Use batch matching without the DMDQN matching-pool policy | Batch-w and DMDQN-w · Synthetic and real-world data | empirical competitive ratio | Helped |
Closest papers by meaning
- Balancing Efficiency and Fairness in On-Demand Ridesourcing (NeurIPS 2019 · similarity 0.88)
- Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours (AAAI 2020 · similarity 0.87)
- Neural Approximate Dynamic Programming for On-Demand Ride-Pooling (AAAI 2020 · similarity 0.87)
- Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems (NeurIPS 2025 · similarity 0.82)
- NondBREM: Nondeterministic Offline Reinforcement Learning for Large-Scale Order Dispatching (AAAI 2024 · similarity 0.81)
- i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance (AAAI 2024 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.