# Predictive Positioning and Quality Of Service Ridesharing for Campus Mobility On Demand Systems

arXiv: https://arxiv.org/abs/1609.08116 · HTML: https://mlsanity.com/paper/1609.08116/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance](https://mlsanity.com/paper/2305.02209/) (2023)

## Abstract (arXiv)

Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system.A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29% and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.