Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A machine learning approach to improve the accuracy of GPS-based map-matching algorithms

Hashemi, M and Karimi, HA (2016) A machine learning approach to improve the accuracy of GPS-based map-matching algorithms. In: UNSPECIFIED.

[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or on close parallel roads. In this paper an artificial neural network (ANN) approach is explored to improve the segment identification accuracy of map-matching algorithms. The proposed ANN is continuously trained by using the horizontal shift imposed on GPS points and once it is trained, it will be used to correct raw GPS points before inputting them into the map-matching algorithm. Integrating the proposed ANN enabled an existing map-matching algorithm to find the correct segments for some of the GPS points where the original map-matching algorithm had failed to do so.


Social Networking:
Share |


Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Hashemi, M
Karimi, HAhkarimi@pitt.eduHKARIMI0000-0001-5331-5004
Date: 1 January 2016
Date Type: Publication
Journal or Publication Title: Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016
Page Range: 77 - 86
Event Type: Conference
DOI or Unique Handle: 10.1109/iri.2016.18
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781509032075
Date Deposited: 21 Jun 2017 15:38
Last Modified: 30 Mar 2021 10:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item