Abstract

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing the various disturbances encountered during actual cycling and identifying segment-level problems for targeted interventions. To address these limitations, this study proposes a cycling-data-based framework for non-motorized lane information extraction and rideability assessment. The framework integrates cycling trajectories, first-person cycling videos, urban road networks, and points of interest (POIs) to extract information on road space, facility attributes, pavement conditions, visual environment, and static and dynamic disturbances, and further transforms this information into segment-level rideability assessment indicators. On this basis, an assessment system covering safety, comfort, attractiveness, and accessibility is constructed, and Wuhan is used as an empirical case study. Fuzzy C-means (FCM) clustering is then applied to identify six typical lane types and support differentiated governance strategies. The findings provide practical references for non-motorized lane planning, slow-traffic space improvement, and the management of motorized–non-motorized traffic conflicts.

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