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March 22, 2026Transportation Research Part A Policy and PracticeOpen Access

Desirable bikeshare routes: Nonlinear impacts of micro-level street environments

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Authors

YCYuxuan CaiQSQiwei SongYCYiming Cheng

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Overview

Uncovers nonlinear influences of street environments on bikeshare preferences, suggesting new design strategies for safer cycling routes.

Key Points

  • The aim is to understand how micro-level street environments affect cyclists' route choices and satisfaction.
  • Analyzed over 4,000 bikeshare trajectories from Lime Dockless Bikeshare.
  • Developed a Desirable Bikeshare Index (DBI) to assess route preferences.
  • Used computer vision to detect road damage from street view images.
  • Identified nonlinear associations using explainable machine learning algorithms.
  • Road damage detection accuracy was 98.4%.
  • Lush trees attract cyclists, while excess shrubs deter them.
  • Sidewalk presence is crucial, and road damage is common where cyclists congregate.
  • Perceived accessibility and richness positively influence route desirability.

Cite This Study

Cai et al. (2026) studied this question.

synapsesocial.com/papers/69bf86ecf665edcd009e910dhttps://doi.org/10.1016/j.tra.2026.104962
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Modeling the impact of street-level built environment on cyclists’ route choice using street view images and GPS data2025
  2. 2Cycling-friendly cities require favorable perceptions of streetscapes in China: the evidence in magnifying the benefits of accessibility and land use mix (Preprint)2024
  3. 3Pedaling through preferences: unraveling environmental drivers in cyclists’ route decisions for urban sustainability: a case study of Xiamen, China2026
  4. 4Why people cycle? Uncovering the mismatch between cycling visual environment and cycling behavior2026 · 1 citations
  5. 5How Do Street Landscapes Influence Cycling Preferences? Revealing Nonlinear and Interaction Effects Using Interpretable Machine Learning: A Case Study of Xiamen Island2025