Learning and inferring transportation routines
Artificial Intelligence
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
Applications of location-based services: a selected review
Journal of Location Based Services
UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
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Transportation mode (TM) detection is one of the activity recognition tasks in ubiquitous computing. A number of previous studies have compared the performance of various classifiers for TM detection. However, the current study is the first work aiming to understand how TM detection performance is impacted by how the recorded location traces are segmented into data segments for training a classifier. In our preliminary experiments we examine three trace segmentation (TS) methods---Uniform Duration (UniDur), Uniform Number of Location Points (UniNP), and Uniform Distance (UniDis)---and compare their performance on detecting different transportation modes. The results indicate that while driving can be more accurately detected by using UniDis method, walking and bus can be more accurately detected by using UniDur method. This suggests that choosing a right TS method for training a TM classifier is an important step to accurately detect particular transportation modes.