Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset
<|reference_start|>Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset: The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for data-driven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, data-driven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions.<|reference_end|>
@article{ding2024data-driven,
    title={Data-driven Energy Consumption Modelling for Electric Micromobility
  using an Open Dataset},
    author={Yue Ding, Sen Yan, Maqsood Hussain Shah, Hongyuan Fang, Ji Li,
  Mingming Liu},
    journal={arXiv preprint arXiv:2403.17632},
    year={2024},
    doi={10.1109/ITEC60657.2024.10599070},
    archivePrefix={arXiv},
    eprint={2403.17632},
    primaryClass={cs.AI cs.CY cs.LG}
}