In 2018, the City of Kelowna entered into a license agreement with Dropbike to operate a dockless bike-share pilot in and around the downtown core. The bikes were tracked by the user's cell phone GPS through the Dropbike app. The City's Active Transportation team recognized that this GPS data could help understand the routes used by cyclists which would then inform decision-making for infrastructure improvements. Using OSMnx and NetworkX, the map of Kelowna was converted into a graph network to map inaccurate, infrequent GPS points to the nearest street intersection, calculate the potential paths taken by cyclists and count the number of trips by street segment though the comparison of different path-finding models. Combined with the data from four counters around downtown, a mixed effects statistical model and a least squares optimization were used to estimate a relationship between the different traffic patterns of the bike-share and counter data. Using this relationship based on sparse data input from physical counting stations and bike share data, estimations and visualizations of the annual daily bicycle volume in downtown Kelowna were produced. The analysis, modelling and visualization helped to better understand how the bike network was being used in the urban center, including non-traditional routes such as laneways and highway crossings.

翻译：2018年，基洛纳市与Dropbike签订许可协议，在市中心及周边区域开展无桩共享单车试点项目。自行车通过用户手机GPS经Dropbike应用进行追踪。该市活跃交通团队认识到，这些GPS数据有助于了解骑行者使用的路线，从而为基础设施改造决策提供依据。利用OSMnx和NetworkX，将基洛纳地图转换为图网络，将不精确、低频的GPS点映射至最近街道交叉口，通过比较不同路径查找模型来推算骑行者可能路径，并统计各路段行程次数。结合市中心四个计数器的数据，采用混合效应统计模型和最小二乘优化，估算共享单车与计数器数据不同交通模式间的关系。基于物理计数站点稀疏数据输入与共享单车数据的这种关系，生成了基洛纳市中心年平均日自行车流量的估算值与可视化结果。分析、建模与可视化有助于更深入理解城市中心区自行车网络的使用情况，包括巷道和公路交叉口等非常规路径。