Trip Purpose Identification from GPS Tracks
Trip Purpose Identification from GPS Tracks
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DOI：
10.3141/2405-03
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发表时间：
2014-01-01
影响因子：
1.7
通讯作者：
Axhausen, Kay W.
中科院分区：
文献类型：
--
作者：
Montini, Lara;Rieser-Schuessler, Nadine;Axhausen, Kay W.
旅行调查越来越多地利用全球定位系统数据，这些数据提供精确的路线和时间观测，并有可能减少答复负担。在这些调查中，旅行日记通常是自动构建的，其中对所采用的程序的研究一直集中在模式识别上。这里报告的研究的目标是提高旅行目的识别。分析使用了随机森林，这是一种已成功应用于模式识别的机器学习方法。该分析基于156名参与者收集的GPS轨迹和加速度计数据，这些参与者参加了2012年在瑞士完成的为期一周的旅行调查。结果表明，随机森林提供了强大的旅行目的分类。对于集合运行，正确预测的份额在80%到85%之间。不同的分类器设置是可能的，有时需要的应用程序上下文。分类器的训练集及其输入变量（特征集）以各种方式定义。本研究测试了四种相关设置。
Travel surveys are increasingly taking advantage of GPS data, which offer precise route and time observations and a potentially reduced response burden. In these surveys, travel diaries are usually constructed automatically where research on the employed procedures has been focused on mode identification. The goal of the research reported here was to improve trip purpose identification. The analysis used random forests, a machine-learning approach that had been successfully applied to mode identification. The analysis was based on GPS tracks and accelerometer data collected by 156 participants who took part in a 1-week travel survey in Switzerland that was completed in 2012. The results show that random forests provide robust trip purpose classification. For ensemble runs, the share of correct predictions was between 80% and 85%. Different setups of the classifier were possible and sometimes required by the application context. The training set and its input variables (feature set) of the classifier were defined in various ways. Four relevant setups were tested for this study.