# Data-Driven Robust Taxi Dispatch under Demand Uncertainties

arXiv: https://arxiv.org/abs/1603.06263 · HTML: https://mlsanity.com/paper/1603.06263/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2016.

## Built on by (most cited first, 7 of 7)

- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [Taxi dispatching strategies with compensations](https://mlsanity.com/paper/2401.11553/) (2024)
- [Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare](https://mlsanity.com/paper/2307.02637/) (2023)
- [Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach](https://mlsanity.com/paper/2310.09071/) (2023)
- [CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement](https://mlsanity.com/paper/2310.18215/) (2023)
- [Streamlining Advanced Taxi Assignment Strategies based on Legal Analysis](https://mlsanity.com/paper/2401.12324/) (2024)
- [CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee](https://mlsanity.com/paper/2504.13961/) (2025)

## Abstract (arXiv)

In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in real-time. They provide knowledge of system models on passenger demand and mobility patterns for efficient taxi dispatch and coordination strategies. Such approaches face new challenges: how to deal with uncertainties of predicted customer demand while fulfilling the system's performance requirements, including minimizing taxis' total idle mileage and maintaining service fairness across the whole city; how to formulate a computationally tractable problem. To address this problem, we develop a data-driven robust taxi dispatch framework to consider spatial-temporally correlated demand uncertainties. The robust vehicle dispatch problem we formulate is concave in the uncertain demand and convex in the decision variables. Uncertainty sets of random demand vectors are constructed from data based on theories in hypothesis testing, and provide a desired probabilistic guarantee level for the performance of robust taxi dispatch solutions. We prove equivalent computationally tractable forms of the robust dispatch problem using the minimax theorem and strong duality. Evaluations on four years of taxi trip data for New York City show that by selecting a probabilistic guarantee level at 75%, the average demand-supply ratio error is reduced by 31.7%, and the average total idle driving distance is reduced by 10.13% or about 20 million miles annually, compared with non-robust dispatch solutions.