Summary of Individual Bus Trip Chain Prediction and Pattern Identification Considering Similarities, by Xiannan Huang et al.
Individual Bus Trip Chain Prediction and Pattern Identification Considering Similarities
by Xiannan Huang, Yixin Chen, Quan Yuan, Chao Yang
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to predicting future bus trip chains based on those from similar days, effectively capturing complex relationships between trips that existing time-series methods cannot express. By defining key similarity patterns and developing a similarity function, the authors construct a graph where each day is represented as a node, and edge weights reflect the similarity between days. This allows for semi-supervised classification on a graph to predict bus trip chains. The approach achieves state-of-the-art prediction results on a real-world dataset of 10,000 bus users. Analysis of the similarity function’s parameters reveals interesting bus usage patterns, clustering users into three types: repeat-dominated, evolve-dominate, and repeat-evolve balanced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps public transit operators better predict what buses will be needed in the future by looking at how people travel on similar days. It’s like looking at a map to figure out where people tend to go. The authors developed a new way to do this that works really well and can group people into different types based on their travel habits. |
Keywords
» Artificial intelligence » Classification » Clustering » Semi supervised » Time series
