Deciphering urban bike-sharing patterns: an in-depth analysis of natural environment and visual quality in New York's Citi bike system
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Date
2024-01-17
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Abstract
Bike-sharing offers a convenient and sustainable mode of transportation. Numerous studies have investigated the influence of temporal variations in the natural environment on cycling, as well as the impact of physical street characteristics like networks and infrastructures. However, few studies integrated and compared the effects of natural environment and street visual quality on cycling in the spatial dimension. As a case study, we focused on the impact of these two factors on Citi Bike system on weekdays and weekends in New York City, while accounting for sociodemographic and functional factors. This study employed machine learning and multiscale geographically weighted regression models at both station and neighborhood scales for a comprehensive analysis of their relationships. The results reveal that the natural environment factors, particularly visibility, are more important factors associated with bike-sharing use. Among the visual quality factors, motorized traffic has a negative impact on both weekday and weekend cycling. When considering geographical location, sky openness exhibits an unfavorable influence on weekday cycling in specific areas. By combining natural environment and visual quality factors, our study promotes optimal resource allocation and the development of bike-friendly cities.
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Bike-sharing usage, Weather conditions, Air quality, Visual quality, Machine learning, Multiscale geographically weighted regression