Summary of Sptte: a Spatiotemporal Probabilistic Framework For Travel Time Estimation, by Chen Xu et al.
SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
by Chen Xu, Qiang Wang, Lijun Sun
First submitted to arxiv on: 27 Nov 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 spatiotemporal probabilistic framework, called SPTTE, to estimate travel time uncertainty and account for correlations between multiple trips. The authors tackle the challenge of modeling the temporal variability of multi-trip travel time distributions by formulating the estimation task as a spatiotemporal stochastic process regression problem with fragmented observations. SPTTE incorporates an RNN-based temporal Gaussian process parameterization to regularize sparse observations and capture temporal dependencies, as well as a prior-based heterogeneity smoothing strategy to correct unreliable learning caused by unevenly distributed trips. The proposed method outperforms state-of-the-art deterministic and probabilistic methods by over 10.13% on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPTTE is a new way to estimate travel time uncertainty that takes into account the connections between different trips. Right now, there are limited amounts of data about when people travel, which makes it hard to accurately predict travel times. To fix this, SPTTE uses a special kind of machine learning called recurrent neural networks (RNNs) to look at patterns in the data and make better predictions. The authors tested SPTTE on real-world data and found that it worked much better than other methods. |
Keywords
» Artificial intelligence » Machine learning » Regression » Rnn » Spatiotemporal
