Abstract

This is a portion of GPS trajectory dataset collected in (Microsoft Research Asia) GeoLife project. Each trajectory has a set of transportation mode labels, such as by driving, taking a bus, riding a bike and walking. There is a label file associated with each folder storing the trajectories of a user. Though this is only a part of the dataset used in the following papers, the scale of this released dataset can still support transportation mode learning.

A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude, height, speed and heading direction, etc. These trajectories were recorded by different GPS loggers or GPS-phones, and have a variety of sampling rates. 95 percent of the trajectories are logged in a dense representation, e.g., every 2~5 seconds or every 5~10 meters per point, while a few of them do not have such a high density being constrained by the devices.

Please cite the following three papers when using this GPS dataset.
[1] Yu Zheng, Like Liu, Longhao Wang, Xing Xie. Learning Transportation Modes from Raw GPS Data for Geographic Application on the Web, In Proceedings of International conference on World Wild Web (WWW 2008), Beijing, China. ACM Press: 247-256
[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312–321.
[3] Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, Wei-Ying Ma. Understanding transportation modes based on GPS data for Web applications. ACM Transaction on the Web. Volume 4, Issue 1, January, 2010. pp. 1-36.

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