Summary of A Survey Of Route Recommendations: Methods, Applications, and Opportunities, by Shiming Zhang et al.
A Survey of Route Recommendations: Methods, Applications, and Opportunities
by Shiming Zhang, Zhipeng Luo, Li Yang, Fei Teng, Tianrui Li
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Route recommendation is a crucial aspect of intelligent transportation, significantly impacting citizens’ travel habits. The paper presents a comprehensive review of route recommendation work based on urban computing, categorized into three parts: methodology-wise, application-wise, and current problems and challenges. The survey highlights the development of smart and efficient travel routes using big data, possibly multi-modal, and explores the historical relations and advancements in traditional machine learning and deep learning methods. Additionally, it presents novel applications related to route recommendation within urban computing scenarios and envisions promising research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Route recommendation is a way to help people plan their travels better. The paper looks at all the different ways researchers have tried to do this using big data and computers. It breaks down these approaches into three parts: how they did it, what they used it for, and what’s still missing. This helps other researchers see what’s currently happening in this area and where it might go next. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Multi modal
