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

arXiv 2409.03148 ↗First posted 5 Sep 2024cs.LG cs.HCcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 43 references that are in the map. First posted on arXiv 5 Sep 2024.

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6 of 43 references.

References · 6

  1. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  2. 2020Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic informationcited by 58
  3. 2020LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protectioncited by 57
  4. 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
  5. 2022Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approachcited by 8
  6. 2024Evaluating MEDIRL: A Replication and Ablation Study of Maximum Entropy Deep Inverse Reinforcement Learning for Human Social Navigationcited by 1

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  1. 2026Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
  2. 2022A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
  3. 2026From Steering to Pedalling: Do Autonomous Driving VLMs Generalize to Cyclist-Assistive Spatial Perception and Planning?
  4. 2025Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
  5. 2022Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning
  6. 2025Generalizable Trajectory Prediction via Inverse Reinforcement Learning with Mamba-Graph Architecture
  7. 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
  8. 2025Street design and driving behavior: evidence from a large-scale study in Milan, Amsterdam, and Dubai