Summary of Graph Based Traffic Analysis and Delay Prediction, by Gabriele Borg and Charlie Abela
Graph Based Traffic Analysis and Delay Prediction
by Gabriele Borg, Charlie Abela
First submitted to arxiv on: 28 Oct 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 This paper addresses traffic congestion in Malta, the EU’s most densely populated country with rapid vehicle growth. The authors present MalTra, a comprehensive traffic dataset comprising realistic trips made by the public over 200 days. They also describe their methodology for generating syntactic data to complete the dataset. The paper compares results from existing research using the Q-Traffic dataset and three models: ARIMA, STGCN, and DCRNN. The authors found that the DCRNN model outperforms STGCN with a mean absolute error (MAE) of 3.98 and root mean squared error (RMSE) of 7.78. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic congestion in Malta is a pressing issue due to its high population density and rapid vehicle growth. To tackle this problem, researchers created MalTra, a comprehensive traffic dataset, and used three models: ARIMA, STGCN, and DCRNN. The study found that the DCRNN model performed better than the others. |
Keywords
» Artificial intelligence » Mae
