Summary of Dutytte: Deciphering Uncertainty in Origin-destination Travel Time Estimation, by Xiaowei Mao et al.
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
by Xiaowei Mao, Yan Lin, Shengnan Guo, Yubin Chen, Xingyu Xian, Haomin Wen, Qisen Xu, Youfang Lin, Huaiyu Wan
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 DutyTTE, a travel time estimation (TTE) approach that accurately quantifies uncertainty in travel times by predicting the most likely path and assessing travel time uncertainty along it. The task involves generating the most likely path that aligns with ground truth, and modeling the impact of travel time on overall uncertainty under varying conditions. To address these challenges, the authors introduce a deep reinforcement learning method to improve alignment between predicted paths and ground truth, and propose a mixture of experts guided uncertainty quantification mechanism to capture travel time uncertainty for each segment under varying contexts. The results are calibrated using Hoeffding’s upper-confidence bound to provide statistical guarantees. Experiments on two real-world datasets demonstrate the superiority of DutyTTE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Travel time estimation is important because it helps us understand how long it takes to get from one place to another. But, there’s a problem: we don’t always know exactly when we’ll arrive. That’s why scientists want to estimate not just the travel time, but also how likely it is that we’ll be on time or late. To do this, they need to predict the most likely path and figure out how much uncertainty is involved in each step of the journey. The paper proposes a new method called DutyTTE that uses deep learning and some fancy math to solve these problems. |
Keywords
» Artificial intelligence » Alignment » Deep learning » Mixture of experts » Reinforcement learning
