Cities around the world are turning to non-motorized transport alternatives to help solve congestion and pollution issues. This paradigm shift demands on new infrastructure that serves and boosts local cycling rates. This creates the need for novel data sources, tools, and methods that allow us to identify and prioritize locations where to intervene via properly planned cycling infrastructure. Here, we define potential demand as the total trips of the population that could be supported by bicycle paths. To that end, we use information from a phone-based travel demand and the trip distance distribution from bike apps. Next, we use percolation theory to prioritize paths with high potential demand that benefit overall connectivity if a bike path would be added. We use Bogotá as a case study to demonstrate our methods. The result is a data science framework that informs interventions and improvements to an urban cycling infrastructure.

世界各地的城市都在寻求非机动交通替代方案，以帮助解决交通拥堵和污染问题。这种范式转变要求建立新的基础设施，以服务并提高当地的自行车骑行率。这产生了对新颖数据源，工具和方法的需求，这些数据源，工具和方法允许我们通过适当计划的自行车基础设施来确定要干预的位置并确定优先级。在这里，我们将潜在需求定义为自行车道可支持的总人口出行。为此，我们使用基于电话的出行需求信息和来自自行车应用程序的出行距离分布。接下来，我们将使用渗流理论对具有高潜在需求的路径进行优先级排序，如果将添加自行车路径，则会有利于总体连通性。我们以波哥大为例来说明我们的方法。