ABSTRACT Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3 min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.

中文翻译：

---

使用混合时空聚类算法和随机森林模型从原始 GPS 数据中识别行程终点：上海案例研究

摘要 与传统调查相比，智能手机被认为是旅行行为研究的首选设备。但主要的挑战是从 GPS 数据中提取候选停靠点以及行程结束与噪声的区别。本文开发了用于 GPS 数据收集和旅行日记验证的居民旅行调查系统 (RTSS)，然后使用两步法确定旅行终点。在第一步中，提出了一种基于密度的时空聚类算法来从轨迹中提取候选停靠点。第二步，应用随机森林模型来区分行程终点和模式转移点。结果表明，聚类算法达到了96.2%的准确率、99.6%的召回率、3 min内的平均时间绝对误差、30米内的平均偏移距离。行程终点识别综合准确率为99.2%。两步法在旅行终点识别方面表现良好，提高了旅行调查系统的效率。