App-based automatic collection of travel behaviour: A field study comparison with self-reported behaviour
App-based automatic collection of travel behaviour: A field study comparison with self-reported behaviour
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基于应用程序的旅行行为自动收集：与自我报告行为的实地研究比较
DOI：
10.1016/j.trip.2021.100501
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发表时间：
2021
影响因子：
--
通讯作者：
A. Ciccone
中科院分区：
文献类型：
--
作者：
Ingeborg Storesund Hesjevoll;A. Fyhri;A. Ciccone
智能手机应用程序为旅行行为研究带来了巨大的希望，但它们相对于传统方法的性能仍然没有得到很好的理解。本研究的目的是评估从一个完全自动的旅行模式检测移动的应用程序和传统的旅行行为调查的旅行行为之间的差异的幅度和方向。我们展示了使用该应用程序（sense.dat）四周的n = 230名参与者的数据。参与者还完成了为期一天的旅行日记和一周的回顾性报告骑自行车和步行在同一时期。应用程序和调查之间的对应关系因聚合水平和方式而异。总体而言，该应用程序记录的公里数，分钟数和非零行程天数比一天的调查要多得多，但按模式划分时，公共交通的情况并非如此。在个人层面上，该应用程序倾向于记录除公共交通以外的所有模式的受访者未自我报告的模式，这可能表明该应用程序捕获了用户可能忘记或故意遗漏的行程。对于自行车，汽车和步行，应用程序和调查登记（一天）距离和持续时间之间的斯皮尔曼相关性是中等的（r> 0.5）或强（r> 0.8），当基于两个数据源中的非零观测值时，以及当基于所有观测值时，中等或弱。对于为期一周的活跃交通方式报告，应用程序调查相关性低于一天的数据，特别是对于步行。
Smart phone apps hold great promise for travel behaviour research, but their performance relative to traditional methods is still not well understood. The aim of this study is to evaluate the magnitude and direction of differences between travel behaviour from a completely automatic travel mode detection mobile app and a traditional travel behaviour survey. We present data from n = 230 participants who used the app (sense.dat) for four weeks. Participants also completed a one-day travel diary and one-week retrospective account of cycling and walking in the same period. Correspondence between app and survey varied across levels of aggregation and modalities. Overall, the app recorded substantially more km, minutes and non-zero trip days than the one-day survey, but when split up by mode this was not true for public transport. On the individual level there was a tendency for the app to register modes not self-reported by the respondents for all modes except public transport, possibly indicating that the app captures trips that the user may have forgot or intentionally left out. For bike, car and foot, the Spearman correlations between app and survey registered (one-day) distances and durations were moderate (r> 0.5) or strong (r> 0.8) when based on observations that were non-zero in both data sources, and moderate or weak when based on all observations. For one-week reports of active transport modes, app-survey correlations were lower than for the one-day data, especially for foot.