Summary of Leverage Multi-source Traffic Demand Data Fusion with Transformer Model For Urban Parking Prediction, by Yin Huang et al.
Leverage Multi-source Traffic Demand Data Fusion with Transformer Model for Urban Parking Prediction
by Yin Huang, Yongqi Dong, Youhua Tang, Li Li
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 proposed framework integrates spatial-temporal deep learning with multi-source data fusion for effective parking availability prediction. It leverages K-means clustering to establish parking cluster zones and extracts traffic demand characteristics from various transportation modes connected to parking lots. Compared with different machine learning, deep learning, and traditional statistical models, the Transformer model outperforms others in terms of MSE, MAE, and MAPE. This approach has potential for developing parking availability prediction systems that provide accurate and timely information for drivers and urban planners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method to predict where there will be parking spots available in cities. Right now, predicting parking is not very good because it doesn’t take into account things like how many cars are on the road at the same time. This paper shows that by using a special type of artificial intelligence and combining data from different sources, they can make more accurate predictions about where there will be parking spots available. This could help people find places to park their cars faster and reduce traffic congestion. |
Keywords
» Artificial intelligence » Clustering » Deep learning » K means » Machine learning » Mae » Mse » Transformer
