Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data
Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data
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DOI：
10.1016/j.eswa.2022.116734
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Zhenxing Yao;Fei Yang;Yudong Guo;P. Jin;Y. Li
中科院分区：
文献类型：
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作者：
Zhenxing Yao;Fei Yang;Yudong Guo;P. Jin;Y. Li
作为一种广泛使用的便携式探头，搭载全球导航卫星系统（GNSS）的智能手机可以持续追踪个人的出行轨迹，具有进行行程终点信息识别的潜力。现有的研究大多使用基于规则的方法，具有特定的停留时间和距离阈值来进行行程终点检测，这些方法高度依赖于专家经验，并且缺乏不同交通环境的通用性。一些基于密度的空间聚类方法在GNSS信号缺失、交通拥堵、短时间停留和重复访问同一地点等情况下也存在问题。因此，本文提出了一种利用智能手机GNSS定位数据进行行程终点识别的两步方法。首先，提出了一种基于时空密度的聚类算法（ST-DBCA）用于行程终点识别，该方法同时考虑了时空行程轨迹点密度，并且性能比传统聚类方法要好得多。其次，进一步提出了三种优化模型来优化识别结果，包括1）短时间停留优化模型，2）冗余停留优化模型，3）交通拥堵停留优化模型。在中国成都进行了现场测试，验证了所提方法的可行性和有效性。结果表明，不同出行目的下的平均出行终点识别准确率达到92.8%，到达和出发时间平均误差小于150 s。
As a widespread portable probe, smartphone equipped with Global Navigation Satellite System (GNSS) can continuously track individual’s travel trajectory, it is potential for trip end information identification. Existing studies mostly use rule-based methods with specific dwelling time and distance thresholds for trip end detection, these methods are highly dependent on expert experience and lack universality across different traffic environments. Some density-based spatial clustering methods also have issues in the case of GNSS signal missing, traffic congestion, short-time stays and repeat visits to the same place etc. Therefore, this paper proposes a two-step method for trip end identification by using smartphone GNSS positioning data. First, a spatial-temporal density-based clustering algorithm (ST-DBCA) is proposed for trip end identification, the method considers both spatial and temporal travel trajectory point density at the same time, and performs much better than traditional clustering methods. Second, three optimization models are further proposed to optimize the identification results, including 1) a short time stay optimization model, 2) a redundant stay optimization model, and 3) a traffic congestion stay optimization model. Field tests in Chengdu China are conducted to verify the feasibility and effectiveness of the proposed methods. Results show that the average trip end identification accuracy under different trip purposes reaches 92.8%, and the average errors of arrival and departure time are smaller than 150 s.