Summary of Fairtp: a Prolonged Fairness Framework For Traffic Prediction, by Jiangnan Xia et al.
FairTP: A Prolonged Fairness Framework for Traffic Prediction
by Jiangnan Xia, Yu Yang, Jiaxing Shen, Senzhang Wang, Jiannong Cao
First submitted to arxiv on: 18 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 A novel framework for prolonged fair traffic prediction is proposed in this paper, which addresses the issue of biased decisions by transportation authorities due to imbalanced data. The existing approaches primarily focus on improving overall accuracy, neglecting the critical problem of fairness in traffic prediction. The uneven deployment of traffic sensors across urban areas results in imbalanced data, causing prediction models to perform poorly in certain regions and leading to unfair decision-making. To address this gap, FairTP is introduced, which incorporates a state identification module to classify sensors’ states as either “sacrifice” or “benefit,” enabling prolonged fairness-aware predictions. Additionally, a state-guided balanced sampling strategy is proposed to further enhance fairness, addressing performance disparities among regions with uneven sensor distributions. Experimental results on two real-world datasets demonstrate that FairTP significantly improves prediction fairness while minimizing accuracy degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach to traffic prediction that makes sure it’s fair for everyone. Right now, the way we predict traffic is biased towards certain areas or times of day. This can lead to unfair decisions about how to manage traffic. The proposed framework, FairTP, tries to fix this by identifying when sensors are “sacrificing” accuracy in one area and “benefiting” in another. It also uses a special way to balance the data so that predictions are fair across different regions. |
