  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.
