Understanding the role of street network configuration on commuter cycling can aid city planners in assessing and evaluating interventions to promote regular cycling into people's routines. Studies examining this relationship generally build models on observed cycling counts. While this provides valuable information, it could still result in an incomplete picture because some routes with advantages (e.g., good accessibility, amenities etc) may not have been utilised as expected because of other confounding factors (e.g., safety issues, motorised traffic) or vice-versa, leading to incorrect conclusions especially in the absence of data on these confounding factors. Thus, in this study, we argue that observing higher cycling flows on a route should not be the sole criteria to examine the role of street network configuration on the cycling patterns, especially when certain confounding factors have not been controlled for. We utilise data from the activity tracking app Strava for the city of Glasgow and compare the observed cycling intensities with the modelled cycling intensities where all the cyclists take the shortest routes. We estimated three linear regression models for: and . Street network configuration were incorporated using Space Syntax measures: Normalised Angular Choice and Normalised Angular Integration. The roles of these variables, along with route characteristics and natural environment factors, on commuter cyclists' trips are explored. Visual exploration and linear regression models indicate that cyclists deviated away from the well-integrated, straighter routes to aesthetically attractive routes with cycling infrastructure, and towards links within mixed land use and to the least deprived areas. These results are of interest to policy makers and assist in infrastructure planning.

中文翻译：

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使用众包数据重新审视街道网络配置对自行车通勤的作用


了解街道网络配置对通勤骑自行车的作用可以帮助城市规划者评估和评价干预措施，以促进定期骑自行车进入人们的日常生活。研究这种关系的研究通常会根据观察到的骑行次数建立模型。虽然这提供了有价值的信息，但它仍然可能导致不完整的情况，因为一些具有优势的路线（例如，良好的可达性、便利设施等）可能由于其他混杂因素（例如，安全问题、机动交通）而没有按预期使用，或者反之亦然，导致错误的结论，尤其是在缺乏这些混杂因素的数据的情况下。因此，在本研究中，我们认为观察路线上较高的自行车流量不应成为检查街道网络配置对自行车模式的作用的唯一标准，特别是在某些混杂因素尚未得到控制的情况下。我们利用格拉斯哥市活动跟踪应用程序 Strava 的数据，并将观察到的骑行强度与所有骑行者都选择最短路线的建模骑行强度进行比较。我们估计了 和 的三个线性回归模型。使用空间语法措施合并街道网络配置：标准化角度选择和标准化角度集成。探讨了这些变量以及路线特征和自然环境因素对通勤骑自行车者的出行的作用。视觉探索和线性回归模型表明，骑行者偏离了一体化良好、笔直的路线，转向具有自行车基础设施的美观路线，并转向混合土地利用和最贫困地区的联系。 这些结果引起了政策制定者的兴趣，并有助于基础设施规划。