Previous studies on bikeability/cycling index have explored factors that influence cycling in cities, and developed indicators to characterize a bicycle-friendly environment. However, despite its strong influence on cycling behavior, cyclists’ exposure to traffic-related air pollution has been often disregarded. To close this knowledge gap, we propose a comprehensive bikeability index that comprises four sub-indices: accessibility, suitability, perceptibility, and prevailing air quality in the vicinity of cycling routes. We evaluate cyclists’ exposure to fine particulate matter and black carbon, and used open-source data, land-use regression models, deep neural networks and spatial analysis. The application of the proposed bikeability framework reveals that the inclusion of air quality makes a significant difference when calculating bikeability index in Singapore and hence it merits serious consideration. We believe that the newly developed framework will convince city planners to consider the importance of assessing cyclists’ exposure to airborne particles when planning cycling infrastructure.

先前关于可骑自行车性/骑自行车指数的研究探索了影响城市骑自行车的因素，并开发了表征自行车友好环境的指标。然而，尽管它对骑自行车的行为有很强的影响，但骑自行车者暴露于交通相关的空气污染中却常常被忽视。为了弥补这一知识鸿沟，我们提出了一个综合的可乘性指数，其中包括四个子指数：可访问性，适用性，可感知性以及自行车道附近的主要空气质量。我们评估骑车人暴露于细颗粒物和黑碳的情况，并使用了开源数据，土地利用回归模型，深度神经网络和空间分析。拟议的可骑自行车性框架的应用表明，在新加坡计算可骑自行车性指数时，空气质量的纳入具有显着差异，因此值得认真考虑。我们相信，新开发的框架将说服城市规划者在计划自行车基础设施时评估骑自行车者暴露于空气中的颗粒的重要性。