• UTRC II SUBMISSION SYSTEM
  • Careers
  • Contact
  • Login / Register

Search form

Home
  • Home
  • About
    • Welcome to the UTRC Site
    • Theme
    • Staff
    • Board of Directors
    • Press
    • Annual Report
    • Program Progress Performance Report
    • Newsletter
  • Research
    • Projects
    • RFPs
    • Submit Your Proposal
    • Funding Categories
      • UTRC Research Initiative
      • UTRC Advanced Technology Initiative
      • UTRC Faculty Development Mini-grants
      • UTRC Best Transportation Paper Competition
      • News
  • Publications
  • Directory
    • Consortium Universities
    • Partners
    • Principal Investigators
    • Staff
    • Board of Directors
  • Education
    • Where to Study
    • Transportation and Planning Doctoral Series
    • AITE Scholarships
    • UTRC Dissertation Grants
    • Summer Institute
    • September 11th Memorial Program
    • Technology Transfer and Training
    • Online Graduate Certificate Program
    • UTRC Travel Grants
    • Student Award Recipients
    • Apply For Scholarships
  • Events
    • Upcoming Events
    • Past Events
    • Visiting Scholar Seminar Series
  • Resources

Compression and Mining of GPS Trace Data: New Techniques and Applications for Transportation

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including how well they preserve the spatio-temporal information across numerous real-world datasets, execution times and various error metrics. Such comparisons are useful in identifying the most effective algorithms for various situations. We also provide recommendations for a hybrid algorithm which can leverage the strengths of various algorithms while mitigating their drawbacks.

Project Details

Author(s): 
Dr. Catherine T. Lawson
Dr. Jeong-Hyon Hwang
Dr. Sekharipuram S. Ravi
Universities: 
State University of New York (SUNY)
Publication Year: 
2011
Publication Type: 
Final Report
Project: 
Compression and Mining of GPS Trace Data: New Techniques and Applications for Transportation
Publication Category: 
Geotechnology
Operations & Traffic Management
Please subscribe to our Newsletter:

Get our newsletter

Please enter your email address to subscribe to our newsletter:

Contact Us

University Transportation Research Center
Marshak Hall - Science Building, Suite 910 
The City College of New York
138th Street & Convent Avenue ,New York, NY 10031