Promoting active travel (AT) requires informed transportation decisions relating to issues such as planning, safety, and infrastructure, tailored to user needs. To facilitate such a process, a new stream of research has emerged that utilizes social fitness networks (SFNs), such as Strava, to obtain user AT data. However, SFN datasets exhibit bias towards certain types of users (e.g., fitness-oriented cyclists, males, young users). In order to overcome this limitation, we tested a Public Participation Geographic Information System (PPGIS) as a primary data collection method and post-hoc tool to evaluate and complement SFNs by paying special attention to cycling data. In particular, PPGIS was administered in the City of Glasgow via 816 participants partitioned into Strava and non-Strava users. Spatial and contextual information was determined from the collected data. Statistical analyses demonstrate that PPGIS non-Strava users were distinct from PPGIS Strava users, as the latter tend to be associated with certain characteristics (i.e., younger, more fitness-oriented, proficient in smart devices), indicating the need for a complementary dataset. Spatially, a weak correlation (r = 0.22) between the route choices of PPGIS cyclist users and those of Strava indicates inconsistencies between the two; however, the origins and destinations were moderately positively correlated (r = 0.53 and r = 0.48, respectively). Further, statistically significant differences were observed between PPGIS Strava users and PPGIS non-Strava users, inferring the need for a supplementary tool. Our findings imply that PPGIS is a promising tool to fill these gaps.

促进主动旅行 (AT) 需要根据用户需求量身定制与规划、安全和基础设施等问题相关的明智交通决策。为了促进这一过程，出现了一种新的研究流，它利用社交健身网络 (SFN)，例如 Strava，来获取用户 AT 数据。然而，SFN 数据集对某些类型的用户（例如，面向健身的自行车手、男性、年轻用户）表现出偏见。为了克服这一限制，我们测试了公众参与地理信息系统 (PPGIS) 作为主要数据收集方法和事后工具，通过特别关注自行车数据来评估和补充 SFN。特别是，PPGIS 在格拉斯哥市通过划分为 Strava 和非 Strava 用户的 816 名参与者进行管理。根据收集的数据确定空间和上下文信息。统计分析表明，PPGIS 非 Strava 用户与 PPGIS Strava 用户不同，因为后者往往与某些特征相关（即更年轻、更注重健身、精通智能设备），表明需要补充数据集。在空间上，PPGIS 自行车用户的路线选择与 Strava 的路线选择之间的弱相关性 (r = 0.22) 表明两者之间存在不一致；然而，出发地和目的地呈中度正相关（分别为 r = 0.53 和 r = 0.48）。此外，在 PPGIS Strava 用户和 PPGIS 非 Strava 用户之间观察到统计上的显着差异，推断需要补充工具。我们的发现意味着 PPGIS 是填补这些空白的有前途的工具。