# Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.03148 · HTML: https://mlsanity.com/paper/2409.03148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 6 of 6)

- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.06136/) (2022)
- [Evaluating MEDIRL: A Replication and Ablation Study of Maximum Entropy Deep Inverse Reinforcement Learning for Human Social Navigation](https://mlsanity.com/paper/2406.00968/) (2024)

## Abstract (arXiv)

Cycling has gained global popularity for its health benefits and positive urban impacts. To effectively promote cycling, early studies have extensively investigated the relationship between cycling behaviors and environmental factors, especially cyclists' preferences when making route decisions. However, these studies often struggle to comprehensively describe detailed cycling procedures at a large scale due to data limitations, and they tend to overlook the complex nature of cyclists' preferences. To address these issues, we propose a novel framework aimed to quantify and interpret cyclists' complicated visual preferences by leveraging maximum entropy deep inverse reinforcement learning(MEDIRL)and explainable artificial intelligence(XAI). Implemented in Bantian Sub-district, Shenzhen, we adapt MEDIRL model for efficient estimation of cycling reward function by integrating dockless-bike-sharing(DBS) trajectory and street view images(SVIs), which serves as a representation of cyclists' preferences for street visual environments during routing. In addition, we demonstrate the feasibility and reliability of MEDIRL in discovering cyclists' visual preferences. We find that cyclists focus on specific street visual elements when making route decisions, which can be summarized as their attention to safety, street enclosure, and cycling comfort. Further analysis reveals the complex nonlinear effects of street visual elements on cyclists' preferences, offering a cost-effective perspective on streetscapes design. Our proposed framework advances the understanding of individual cycling behaviors and provides actionable insights for urban planners to design bicycle-friendly streetscapes that prioritize cyclists' preferences.