A trip detection model for individual smartphone-based GPS records with a novel evaluation method
A trip detection model for individual smartphone-based GPS records with a novel evaluation method
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一种基于个人智能手机 GPS 记录的行程检测模型，采用新颖的评估方法
DOI：
10.1177/1687814017705066
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发表时间：
2017-06
影响因子：
2.1
通讯作者：
Juan Zhicai
中科院分区：
文献类型：
--
作者：
Wang Bao;Gao Linjie;Juan Zhicai
个人出行模式对交通分析和建模具有重要意义，而基于位置服务的深入应用的快速发展使得获取大规模定位数据成为可能。因此，开发适当的算法来从单个定位记录中识别行程/行程段是至关重要的。本文提出了一种基于智能手机采集的全球定位系统（GPS）瞬时记录的自动行程/行程段检测方法。该方法包括数据清洗与预处理、伪行程终点推断与剔除、行程组合等一系列步骤。该模型的结果与志愿者收集和验证的“地面实况”进行了比较。最后，从125名志愿者中识别出1954次旅行，总体检测准确率在97.5%和98.7%之间，置信水平为95%。此外，引入纯度来评价所提出的方法的性能。此外，瞬时速度随时间的积分在计算行程距离方面表现出优异的性能。
Personal travel pattern is significant to transportation analysis and modeling, and the rapid development of in-depth application of location-based services makes it possible to obtain large-scale positioning data. So, it is crucial to develop proper algorithm to identify trips/trip-segments from individual positioning records. This article presents an automatic trips/trip-segment detection method based on instantaneous Global Positioning System records collected by smartphones. The method consists of a series of procedures including data cleaning and pre-processing, inferring and removing pseudo trip ends, as well as trip combination. The result of the model has been compared with the “ground truth” collected and verified by volunteers. Finally, 1954 trips from 125 volunteers were identified and the overall detection accuracy is between 97.5% and 98.7% with a 95% confidence level. Besides, purity was introduced to evaluate the performance of the proposed method. In addition, the integration of instantaneous speed over time shows an excellent performance in calculating the trip distance.
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影响因子：
1.7
作者：
Safi, Hamid;Assemi, Behrang;Hickman, Mark
通讯作者：
Hickman, Mark
DOI：
10.3328/tl.2009.01.01.59-79
发表时间：
2009-01-01
影响因子：
2.8
作者：
Auld, Joshua;Williams, Chad;Nelson, Peter
通讯作者：
Nelson, Peter
DOI：
10.1016/j.trc.2013.11.005
发表时间：
2014-06-01
影响因子：
8.3
作者：
Nitsche, Philippe;Widhalm, Peter;Maurer, Peter
通讯作者：
Maurer, Peter
影响因子：
1.7
作者：
J. Wolf;S. Hallmark;M. Oliveira;R. Guensler;W. Sarasua
通讯作者：
J. Wolf;S. Hallmark;M. Oliveira;R. Guensler;W. Sarasua
DOI：
--
发表时间：
2014
期刊：
--
影响因子：
--
作者：
T. Maruyama;S. Mizokami;E. Hato
通讯作者：
T. Maruyama;S. Mizokami;E. Hato