# Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times

arXiv: https://arxiv.org/abs/2208.14852 · HTML: https://mlsanity.com/paper/2208.14852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1509.03985/) (2015)
- [Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging](https://mlsanity.com/paper/2012.02636/) (2020)
- [Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning](https://mlsanity.com/paper/1912.03408/) (2019)

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

- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)

## Abstract (arXiv)

In ridepooling systems with electric fleets, charging is a complex decision-making process. Most electric vehicle (EV) taxi services require drivers to make egoistic decisions, leading to decentralized ad-hoc charging strategies. The current state of the mobility system is often lacking or not shared between vehicles, making it impossible to make a system-optimal decision. Most existing approaches do not combine time, location and duration into a comprehensive control algorithm or are unsuitable for real-time operation. We therefore present a real-time predictive charging method for ridepooling services with a single operator, called Idle Time Exploitation (ITX), which predicts the periods where vehicles are idle and exploits these periods to harvest energy. It relies on Graph Convolutional Networks and a linear assignment algorithm to devise an optimal pairing of vehicles and charging stations, in pursuance of maximizing the exploited idle time. We evaluated our approach through extensive simulation studies on real-world datasets from New York City. The results demonstrate that ITX outperforms all baseline methods by at least 5% (equivalent to 70,000 for a 6,000 vehicle operation) per week in terms of a monetary reward function which was modeled to replicate the profitability of a real-world ridepooling system. Moreover, ITX can reduce delays by at least 4.68% in comparison with baseline methods and generally increase passenger comfort by facilitating a better spread of customers across the fleet. Our results also demonstrate that ITX enables vehicles to harvest energy during the day, stabilizing battery levels and increasing resilience to unexpected surges in demand. Lastly, compared to the best-performing baseline strategy, peak loads are reduced by 17.39% which benefits grid operators and paves the way for more sustainable use of the electrical grid.