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  1. Transactions on the Web (TWEB)
  2. ACM Transactions on the Web (TWEB) : Volume 4
  3. Issue 1, January 2010
  4. Understanding transportation modes based on GPS data for web applications
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ACM Transactions on the Web (TWEB) : Volume 10
ACM Transactions on the Web (TWEB) : Volume 9
ACM Transactions on the Web (TWEB) : Volume 8
ACM Transactions on the Web (TWEB) : Volume 7
ACM Transactions on the Web (TWEB) : Volume 6
ACM Transactions on the Web (TWEB) : Volume 5
ACM Transactions on the Web (TWEB) : Volume 4
Issue 4, September 2010
Issue 3, July 2010
Issue 2, April 2010
Issue 1, January 2010
Understanding transportation modes based on GPS data for web applications
A distributed service-oriented architecture for business process execution
Declarative specification and verification of service choreographiess
ACM Transactions on the Web (TWEB) : Volume 3
ACM Transactions on the Web (TWEB) : Volume 2
ACM Transactions on the Web (TWEB) : Volume 1

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Understanding transportation modes based on GPS data for web applications

Content Provider ACM Digital Library
Author Zheng, Yu Ma, Wei-Ying Chen, Yukun Xie, Xing Li, Quannan
Copyright Year 2010
Abstract User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
Starting Page 1
Ending Page 36
Page Count 36
File Format PDF
ISSN 15591131
e-ISSN 1559114X
DOI 10.1145/1658373.1658374
Volume Number 4
Issue Number 1
Journal ACM Transactions on the Web (TWEB)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-01-29
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword GPS trajectory GeoLife Spatial data mining Transportation modes Ubiquitous computing Understanding user behavior User mobility
Content Type Text
Resource Type Article
Subject Computer Networks and Communications
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