# Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare

arXiv: https://arxiv.org/abs/2307.02637 · HTML: https://mlsanity.com/paper/2307.02637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 76 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [The Riemannian Geometry of Deep Generative Models](https://mlsanity.com/paper/1711.08014/) (2017)
- [Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing](https://mlsanity.com/paper/2010.12001/) (2020)
- [Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning](https://mlsanity.com/paper/1804.00079/) (2018)
- [Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction](https://mlsanity.com/paper/1905.06335/) (2019)
- [Universal Sentence Representation Learning with Conditional Masked Language Model](https://mlsanity.com/paper/2012.14388/) (2020)
- [Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective](https://mlsanity.com/paper/1404.4391/) (2014)
- [Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control](https://mlsanity.com/paper/2108.10315/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand](https://mlsanity.com/paper/2202.07147/) (2022)
- [Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems](https://mlsanity.com/paper/2011.04222/) (2020)
- [Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding](https://mlsanity.com/paper/1908.05161/) (2019)
- [Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning](https://mlsanity.com/paper/2005.01627/) (2020)
- [Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation](https://mlsanity.com/paper/1902.08850/) (2019)
- [Data-Driven Robust Taxi Dispatch under Demand Uncertainties](https://mlsanity.com/paper/1603.06263/) (2016)
- [Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand](https://mlsanity.com/paper/2211.14983/) (2022)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media](https://mlsanity.com/paper/1402.6010/) (2014)
- [Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks](https://mlsanity.com/paper/1901.10821/) (2019)

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

- [Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks](https://mlsanity.com/paper/2503.24325/) (2025)

## Abstract (arXiv)

Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challenges for routing algorithms. We propose a learning framework for an autonomous fleet of taxis that leverages event data from the internet to predict demand surges and generate cooperative routing policies. We achieve this through a combination of two major components: (i) a demand prediction framework that uses textual event information in the form of events' descriptions and reviews to predict event-driven demand surges over street intersections, and (ii) a scalable multiagent reinforcement learning framework that leverages demand predictions and uses one-agent-at-a-time rollout combined with limited sampling certainty equivalence to learn intersection-level routing policies. For our experimental results we consider real NYC ride share data for the year 2022 and information for more than 2000 events across 300 unique venues in Manhattan. We test our approach with a fleet of 100 taxis on a map with 2235 street intersections. Our experimental results demonstrate that our method learns routing policies that reduce wait time overhead per serviced request by 25% to 75%, while picking up 1% to 4% more requests than other model-based RL frameworks and classical methods in operations research.