Summary of Data-driven Transfer Learning Framework For Estimating Turning Movement Counts, by Xiaobo Ma et al.
Data-Driven Transfer Learning Framework for Estimating Turning Movement Counts
by Xiaobo Ma, Hyunsoo Noh, Ryan Hatch, James Tokishi, Zepu Wang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The researchers propose a novel framework using transfer learning to estimate turning movement counts (TMCs) at intersections. They leverage traffic controller event-based data, road infrastructure data, and point-of-interest (POI) data to improve the accuracy of TMC estimates. The proposed model outperforms eight state-of-the-art regression models in terms of Mean Absolute Error and Root Mean Square Error on 30 intersections in Tucson, Arizona. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make a better way to count how many cars turn at each intersection. They want to use data from traffic lights, road maps, and popular places like restaurants or parks. This will help cities manage traffic better and keep people safe. They’re testing their new method on 30 intersections in Tucson and it’s doing really well compared to other methods. |
Keywords
» Artificial intelligence » Regression » Transfer learning
