Summary of Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network, by Ayad Ghany Ismaeel et al.
Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network
by Ayad Ghany Ismaeel, Krishnadas Janardhanan, Manishankar Sankar, Yuvaraj Natarajan, Sarmad Nozad Mahmood, Sameer Alani, Akram H. Shather
First submitted to arxiv on: 24 Jan 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 paper proposes a novel approach to classifying traffic patterns in smart cities using deep recurrent neural networks. By combining convolutional and recurrent layers, the model effectively captures dynamic and sequential features of traffic patterns, outperforming existing methods in terms of accuracy, precision, recall, and F1 score. The proposed model achieves a precision of up to 95% when evaluated on a real-world traffic pattern dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called deep recurrent neural networks to help smart cities manage their traffic better. It’s like having a super smart traffic cop that can look at what’s happening with the traffic right now and in the past to figure out what will happen next. The new way of doing things is really good, it works well and makes sure that the predictions are accurate. |
Keywords
* Artificial intelligence * F1 score * Precision * Recall
