Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

中文翻译：

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使用智能手机和蓝牙信标收集具有人口代表性的自行车骑行 GPS 数据，以了解自行车骑行活动和模式


骑自行车的 GPS 数据提供了详细的见解和个人层面的出行信息，这对于了解骑自行车的出行行为、提高交通安全和公平性以及开发模型以高时空分辨率估计自行车路线的选择和数量至关重要。然而，大规模的针对自行车的 GPS 数据收集研究并不频繁，许多现有研究缺乏强大的空间和/或时间覆盖范围，或者受到抽样偏差的影响，导致这些数据缺乏代表性。此外，在没有人工干预的情况下，从持续收集的原始 GPS 数据中准确检测骑行行程仍然是一项挑战。本研究提出了一种新颖的 GPS 数据收集方法，方法是利用智能手机应用程序与连接到参与者自行车的蓝牙信标的组合。在最少的启发式后处理的帮助下，我们的方法将数据收集限制在不需要参与者干预的情况下骑自行车进行的旅行，在调查参与、隐私挑战、参与者工作量和强大的自行车骑行行程检测之间仔细优化。我们的方法用于收集澳大利亚大墨尔本地区 673 名成年人的 19,782 次自行车旅行，时间跨度为 8 个月和 3 个季节。收集的数据集在人口统计学（性别、职业和就业类型）、时间和空间模式方面代表了潜在的成人骑自行车人口。男性、中年和老年人的平均出行长度（中位数 = 4.8 公里）、持续时间（中位数 = 20.9 分钟）和骑自行车的频率（中位数 = 2.7 次/周）更高。 “感兴趣但担心”的骑手（使用 Geller 类型学分类）骑得更频繁，而“强壮无畏”和“热情和自信”组骑得更远，时间更长。参与者在没有自行车基础设施的道路/街道上骑行的距离超过一半，而在越野路径和自行车道上花费的时间分别为 24% 和 17%。这个具有人口代表性的数据集将在城市规划和政策制定的背景下发挥关键作用。