Summary of Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ensy), by Amirhossein Parsi et al.
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)
by Amirhossein Parsi, Melina Jafari, Sina Sabzekar, Zahra Amini
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel ensemble-based approach called Ensemble Synthesizer (ENSY) is proposed to enhance the accuracy of mode choice dataset classification. This method leverages probability distribution for data augmentation, tailored specifically for minority class enhancement in transportation planning. ENSY demonstrates significant improvements, quadrupling the F1 score of minority classes and improving overall accuracy by nearly 3%. The approach outperforms other augmentation techniques, showcasing its robustness and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ENSY is a new way to improve how we predict what mode people will choose for transportation. Right now, these predictions aren’t very good because the models struggle with small groups of data that don’t match typical patterns. ENSY helps by making fake copies of the data that are more like real data. This makes the model better at predicting what those minority groups will do. The results show that ENSY is really good, making it a useful tool for planners who want to make better decisions about transportation. |
Keywords
» Artificial intelligence » Classification » Data augmentation » F1 score » Probability
