Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto
This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Vision transformer-based multi-camera multi-object tracking framework for dairy cow monitoring
Activity and behaviour correlate with dairy cow health and welfare, making continual and accurate monitoring crucial for disease identification and farm productivity. Manual observation and frequent assessments are labor…
Multi-Object TrackingInstance SegmentationZero-Shot LearningDeriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twitter
Social media platforms, such as Twitter, provide a totally new perspective in dealing with the traffic problems and is anticipated to complement the traditional methods. The geo-tagged tweets can provide the Twitter user…
Econometric model of children participation in family dairy farming in the center of dairy farming, West Java Province, Indonesia
The involvement of children in the family dairy farming is pivotal point to reduce the cost of production input, especially in smallholder dairy farming. The purposes of the study are to analysis the factors that influen…
Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation
Automated cattle activity classification allows herders to continuously monitor the health and well-being of livestock, resulting in increased quality and quantity of beef and dairy products. In this paper, a sequential …
General ClassificationClustering Activity-Travel Behavior Time Series using Topological Data Analysis
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges tradition…
ClusteringSurveyTime SeriesTime Series Analysis+1