This study uses GPS data of 1461 participants at a planned special event organized in Oshkosh, Wisconsin named AirVenture to characterize their spatio-temporal activity participation behavior. The GPS data is used to derive activity sequences for participants and study the attractiveness of various activities at the event site. A validation procedure is proposed using aerial photos, from which crowd density is estimated and compared to heatmaps of GPS data. A machine learning clustering approach is used to group participants into market segments on the basis of their activity sequences. The results show a prevalence of 6 behavioral groups with statistical tests confirming significant differences related to movement and time use. Finally, a multinomial logit model is formulated, demonstrating that age, prior visitation, and attendance plan (daily vs. weekly) affect the typological behavior. The results reveal valuable insights that can help special event organizers with related marketing and planning strategies.

本研究使用在威斯康星州奥什科什举办的名为 AirVenture 的计划特别活动中 1461 名参与者的 GPS 数据来表征他们的时空活动参与行为。GPS 数据用于推导参与者的活动序列，并研究活动现场各种活动的吸引力。提出了使用航拍照片的验证程序，从中估计人群密度并将其与 GPS 数据的热图进行比较。机器学习聚类方法用于根据参与者的活动序列将参与者分组到细分市场。结果显示 6 个行为组的普遍性，统计测试证实了与运动和时间使用相关的显着差异。最后，制定了一个多项 logit 模型，证明了年龄、先前的访问、和出勤计划（每天与每周）影响类型行为。结果揭示了宝贵的见解，可以帮助特殊活动组织者制定相关的营销和规划策略。