Skip to main content

Filtering Duplicated Location in Tracking Traffic Data

  • 1009 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 387)


Intelligent Transportation System (ITS) has been becoming an integral part of life in city, giving at the result of great impact by utilizing the communication, computing and sensor technologies to solve the relating problem of transportation such as traffic congestions. Traffic congestion is used to curse to citizen and an ongoing problem in almost urban areas. The purpose of this paper is mainly to provide the data without noises as much as possible to Traffic Detection System (TDS) based on GPS_enable Mobile phone. The system is constructed into two parts: Client side (Mobile device) and Cloud Backend Server. In this work, the process of transportation mode filtering is carried out on the Client side applying Moving Average Filtering method and then the filtering location duplicated data continues to work out on Server side based on the Client’s result. In order to solve the server side issue, the distance based clustering method, OPTICS: Ordering Point To Identify the Clustering Structure, is mainly utilized. Afterward, the accuracy of the system is measured by Purity, F-measurement and Entropy method. To execute closeness between GPS points, the distance between them is measured by using Haversine Formula.


  • Cloud
  • GPS
  • ITS
  • TDS
  • Traffic
  • Transportation mode

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: ACM UBICOMP 2003 (2003)

    Google Scholar 

  2. Liao, L., Fox, D., Kautz, H.: Learning and Inferring Transportation Routines. In: AAAI 2004 (2004)

    Google Scholar 

  3. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of Mobile Phone Sensing (September 2010)

    Google Scholar 

  4. Liu, J., Wolfson, O., Yin, H.: Extracting Semantic Location from Outdoor Positioning Systems. In: Int. Workshop on Managing Context Information and Semantics in Mobile Environments (MCISME) (2006)

    Google Scholar 

  5. Hightower, J., Consolvo, S., LaMarca, A., Smith, I., Hughes, J.: Learning and Recognizing the Places We Go. In: ACM Conference on Ubiquitous Computing (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Swe Swe Aung .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Aung, S.S., Naing, T.T. (2016). Filtering Duplicated Location in Tracking Traffic Data. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 387. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23203-4

  • Online ISBN: 978-3-319-23204-1

  • eBook Packages: EngineeringEngineering (R0)