Detecting spatial and temporal route information of GPS traces

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16 Downloads (Pure)

Abstract

This paper aims at detecting route information of GPS traces to represent spatial and temporal information of trips. A Bayesian belief network model is used to calculate the probability of a road matching a GPS log point. The algorithm incorporates road network topology, distance from trace nodes to road segments, the angle between two lines, direction difference, accuracy of measured GPS log point, and position of roads. GPS data collected in the Eindhoven region, The Netherlands, is used to examine the performance of this algorithm. Results based on a small sample show that the algorithm has a good performance in both processing efficiency and prediction accuracy of correctly identified instances. Prediction accuracy using a small sample is 87.02 %.

Original languageEnglish
Title of host publicationGeoinformatics for Intelligent Transportation
EditorsIgor Ivan, Itzhak Benenson, Bin Jiang, Jiri Horák, James Haworth, Tomás Inspektor
Place of PublicationDordrecht
PublisherKluwer Academic Publishers
Pages61-75
Number of pages15
ISBN (Electronic)978-3-319-11463-7
ISBN (Print)9783319114620
DOIs
Publication statusPublished - 2015
Event11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014) - Ostrava, Czech Republic
Duration: 27 Jan 201429 Jan 2014
Conference number: 11

Publication series

NameLecture notes in geoinformation and cartography
PublisherSpringer International Publishing
Volume214
ISSN (Print)1863-2246

Conference

Conference11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014)
Abbreviated titleGIS Ostrava 2014
CountryCzech Republic
CityOstrava
Period27/01/1429/01/14

Keywords

  • Bayesian belief network
  • GPS
  • Map matching
  • Road network

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  • Cite this

    Feng, T., & Timmermans, H. J. P. (2015). Detecting spatial and temporal route information of GPS traces. In I. Ivan, I. Benenson, B. Jiang, J. Horák, J. Haworth, & T. Inspektor (Eds.), Geoinformatics for Intelligent Transportation (pp. 61-75). (Lecture notes in geoinformation and cartography; Vol. 214). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-11463-7_5