# Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services

arXiv: https://arxiv.org/abs/2403.07964 · HTML: https://mlsanity.com/paper/2403.07964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 12 Mar 2024.

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

- [Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.06136/) (2022)
- [A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles](https://mlsanity.com/paper/2003.12873/) (2020)
- [Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles](https://mlsanity.com/paper/2108.04036/) (2021)
- [A Review on AI Algorithms for Energy Management in E-Mobility Services](https://mlsanity.com/paper/2309.15140/) (2023)
- [Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning](https://mlsanity.com/paper/2312.07371/) (2023)
- [Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search](https://mlsanity.com/paper/2111.02149/) (2021)

## Built on by (most cited first, 2 of 2)

- [Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis](https://mlsanity.com/paper/2507.10382/) (2025)
- [Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications](https://mlsanity.com/paper/2508.16135/) (2025)

## Abstract (arXiv)

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-Learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 seconds, while Q-learning reaches an optimal time cost in 20 seconds on average. For a run-time of 2 seconds, Q-learning still achieves a better optimal time cost with a 20% reduction over MMEC-ACO's time cost.