Detecting activity type from GPS traces using spatial and temporal information
Detecting activity type from GPS traces using spatial and temporal information
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DOI：
10.18757/ejtir.2015.15.4.3103
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发表时间：
2015-09
影响因子：
1.7
通讯作者：
T. Feng;H. Timmermans
中科院分区：
文献类型：
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作者：
T. Feng;H. Timmermans
从GPS轨迹中检测活动类型一直是旅游调查中的重要课题。与推断运输模式相比，现有方法由于其假设的简单性和/或缺乏背景信息而在检测活动类型方面仍然相对不准确。为了减少这一差距，本文报告的结果，努力推断活动类型，将空间信息和汇总的时间信息。三种机器学习算法，贝叶斯信念网络，决策树和随机森林，被用来研究这些方法在检测活动类型的性能。该测试基于在荷兰Rijnmond地区收集的GPS跟踪和提示召回数据。结果表明，随机森林模型具有最高的精度。该模型结合了空间和时间信息，可以预测所使用数据集的活动类型，准确率为96.8%。这些研究结果预计将有利于研究使用全球定位系统技术收集活动旅行日记数据。
Detecting activity types from GPS traces has been important topic in travel surveys. Compared to inferring transport mode, existing methods are still relatively inaccurate in detecting activity types due to the simplicity of their assumptions and/or lack of background information. To reduce this gap, this paper reports the results of an endeavour to infer activity type by incorporating both spatial information and aggregated temporal information. Three machine learning algorithms, Bayesian belief network, decision tree and random forest, are used to investigate the performance of these approaches in detecting activity types. The test is based on GPS traces and prompted recall data, collected in the Rijnmond region, The Netherlands. Results show that the random forest model has the highest accuracy. The model incorporating spatial and temporal information can predict activity types with an accuracy of 96.8% for the used dataset. These findings are expected to benefit research on the use of GPS technology to collect activity-travel diary data.