Summary of A Time-inhomogeneous Markov Model For Resource Availability Under Sparse Observations, by Lukas Rottkamp et al.
A Time-Inhomogeneous Markov Model for Resource Availability under Sparse Observations
by Lukas Rottkamp, Matthias Schubert
First submitted to arxiv on: 18 Apr 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 proposed paper presents a novel approach for predicting the future states of monitored resources in smart city applications, such as modern routing algorithms. By exploiting spatio-temporal information about stationary resources like parking bays or charging stations, the model can predict changes in their availability within a given time interval. To accommodate sparse observations, the authors develop time-inhomogeneous discrete Markov models that blend recent observations with historic data and provide probabilistic estimates for future states. The proposed method, which combines a modified Baum-Welch algorithm, is evaluated on real-world datasets of parking bay availability, showing promising results compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how to predict what’s happening in smart cities, like where people will park their cars or charge their electric vehicles. To do this, we need to look at patterns in when and where these things happen over time. But sometimes, we don’t have a complete record of all the data – it might be missing or only collected irregularly. To solve this problem, the authors create new models that can use what little information we do have to make good predictions about the future. |
