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.

翻译：日益严峻的交通拥堵与环境恶化问题凸显了在城市空间中采纳电动出行解决方案的至关重要性。特别是电动滑板车和电动自行车等微型电动出行工具，在此转型中扮演着关键角色，为城市通勤者提供了可持续的替代方案。然而，这些工具的能耗模式是影响其在实际场景中有效性的关键因素，对于行程规划和增强用户使用信心至关重要。为此，近期研究采用了针对特定出行工具和条件定制的物理模型，但由于缺乏用于全面模型评估与验证的开放数据集，这些模型在实际场景中的泛化能力和有效性面临挑战。为填补这一空白，本研究提出了一个在爱尔兰都柏林收集的开放数据集，专门为电动滑板车和电动自行车的能耗建模研究而设计。此外，我们基于该数据集，利用一组代表性的机器学习算法，对能耗建模进行了全面分析，并将其性能与作为基线的当代数学模型进行了比较。结果表明，在能耗估计方面，数据驱动模型相较于相应的数学模型具有显著优势。具体而言，基于对数据集在特定假设下的深入分析，数据驱动模型在准确性上优于物理模型，电动自行车的提升幅度高达83.83%，电动滑板车高达82.16%。