Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data
Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data
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DOI：
10.1177/03611981211031537
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发表时间：
2021-07
影响因子：
1.7
通讯作者：
Fei Yang;Yanchen Wang;P. Jin;Dingbang Li;Zhenxing Yao
中科院分区：
文献类型：
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作者：
Fei Yang;Yanchen Wang;P. Jin;Dingbang Li;Zhenxing Yao
手机数据已被证明在分析居民的出行模式方面是有价值的。现有的研究大多通过基于规则的算法或聚类算法来确定出行终点。这些方法很大程度上取决于主观经验和用户的交流行为。此外，受隐私政策的限制，这些方法的准确性难以评估。本文利用兴趣点数据来补充由于用户行为而产生的手机数据缺失信息。具体而言，提出了一种基于多维属性的随机森林出行终点识别模型。设计并与通信运营商进行了现场数据采集试验，实现了手机数据和真实行车信息的同步采集。用真实的出行信息对所提出的识别方法进行了实证评估。结果表明，总体检测精度和召回率分别达到95.2%和88.7%，平均距离误差为269 m，时间误差小于10 min。与基于规则的方法、聚类算法、朴素贝叶斯方法和支持向量机方法相比，该方法在准确率和一致性方面具有更好的性能。
Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.