Summary of Data-driven Energy Consumption Modelling For Electric Micromobility Using An Open Dataset, by Yue Ding et al.
Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset
by Yue Ding, Sen Yan, Maqsood Hussain Shah, Hongyuan Fang, Ji Li, Mingming Liu
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: The escalating challenges of traffic congestion and environmental degradation highlight the need for embracing E-Mobility solutions in urban spaces, particularly micro E-Mobility tools like E-scooters and E-bikes. These tools offer sustainable alternatives for urban commuters, but their energy consumption patterns are critical for trip planning and boosting user confidence. Recent studies have utilized physical models customized for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a lack 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. We also provide a comprehensive analysis of energy consumption modelling based on the dataset using machine learning algorithms and compare their performance against mathematical models as a baseline. Our results demonstrate that data-driven models outperform physical models in estimating energy consumption by up to 83.83% for E-Bikes and 82.16% for E-Scooters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: The world is facing big problems with traffic jams and pollution. To help solve these issues, researchers are working on electric bikes and scooters that people can use in cities. But to make sure these vehicles are really helping, we need to know how much energy they use. Right now, there aren’t many good ways to figure this out because we don’t have enough data. So, our team collected some data from Dublin, Ireland, about electric bikes and scooters. We then used special computer programs called machine learning algorithms to analyze the data and see which ones work best. What we found is that these computer programs can do a much better job of predicting how much energy these vehicles use than other methods. |
Keywords
* Artificial intelligence * Boosting * Generalization * Machine learning
