Summary of Scalable Learning Of Segment-level Traffic Congestion Functions, by Shushman Choudhury et al.
Scalable Learning of Segment-Level Traffic Congestion Functions
by Shushman Choudhury, Abdul Rahman Kreidieh, Iveel Tsogsuren, Neha Arora, Carolina Osorio, Alexandre Bayen
First submitted to arxiv on: 9 May 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 This paper proposes a data-driven framework to identify traffic congestion functions at global and local scales. The framework learns a single black-box function from pooled traffic data, which can be applied to any segment in the metropolitan area. The authors train a feed-forward neural network on this dataset and evaluate its performance on observed segments, generalization to unobserved segments, and prediction of segment attributes using a large-scale dataset covering multiple cities worldwide. Compared to traditional model-based functions, the proposed framework shows competitive results for highway roads but room for improvement on arterial roads. The approach also demonstrates strong generalization capabilities across cities and road types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to understand traffic congestion patterns around the world. Researchers combined data from different parts of cities into one big dataset, then used it to train a special kind of computer model. This model can be applied to any part of the city and helps predict when and where traffic will be congested. The results show that this approach is good at understanding traffic patterns in some areas but needs improvement in others. The paper also shows that this approach works well across different cities and types of roads. |
Keywords
» Artificial intelligence » Generalization » Neural network
