# Multi-Objective Vehicle Rebalancing for Ridehailing System using a Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2007.06801 · HTML: https://mlsanity.com/paper/2007.06801/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models](https://mlsanity.com/paper/2109.12422/) (2021)
- [Multi Agent Path Finding using Evolutionary Game Theory](https://mlsanity.com/paper/2212.02010/) (2022)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)

## Abstract (arXiv)

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric demand, over a large city with multiple nodes (representing neighborhoods). We assume that the passengers queue up at every node until they are matched with a vehicle. The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled. The resulting SMDP appears to be difficult to solve for closed-form expression for the rebalancing strategy. As a result, we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP. The trained policy is compared with other well-known algorithms for rebalancing, which are designed to address other objectives (such as to minimize demand drop probability) for the ridehailing problem.