Summary of Discovering Cyclists’ Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning, by Kezhou Ren et al.
Discovering Cyclists’ Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning
by Kezhou Ren, Meihan Jin, Huiming Liu, Yongxi Gong, Yu Liu
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 a novel framework to quantify and interpret cyclists’ complex visual preferences when making route decisions, leveraging maximum entropy deep inverse reinforcement learning (MEDIRL) and explainable artificial intelligence (XAI). The framework is implemented in Shenzhen’s Bantian Sub-district, using dockless-bike-sharing (DBS) trajectory and street view images (SVIs) to estimate the cycling reward function. The results show that cyclists prioritize safety, street enclosure, and cycling comfort when making route decisions, with complex nonlinear effects of street visual elements on their preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cycling is good for our health and cities. To make it easier and safer, we need to understand why people choose certain routes. This paper uses new AI tools to figure out what makes cyclists happy. They used real data from Shenzhen to train a special computer model that can predict what route a cyclist will take based on what they see. The results show that most cyclists care about safety, having a clear path, and feeling comfortable while riding. By understanding these preferences, city planners can design streets that make cycling easier and more enjoyable. |
Keywords
» Artificial intelligence » Reinforcement learning
