Detecting spatial and temporal route information of GPS traces

Tao Feng; Harry J P Timmermans

doi:10.1007/978-3-319-11463-7_5

Detecting spatial and temporal route information of GPS traces

Tao Feng
, Harry J P Timmermans

Urban Planning and Transportation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

19 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 language	English
Title of host publication	Geoinformatics for Intelligent Transportation
Editors	Igor Ivan, Itzhak Benenson, Bin Jiang, Jiri Horák, James Haworth, Tomás Inspektor
Place of Publication	Dordrecht
Publisher	Kluwer Academic Publishers
Pages	61-75
Number of pages	15
ISBN (Electronic)	978-3-319-11463-7
ISBN (Print)	9783319114620
DOIs	https://doi.org/10.1007/978-3-319-11463-7_5
Publication status	Published - 2015
Event	11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014) - Ostrava, Czech Republic Duration: 27 Jan 2014 → 29 Jan 2014 Conference number: 11

Publication series

Name	Lecture notes in geoinformation and cartography
Publisher	Springer International Publishing
Volume	214
ISSN (Print)	1863-2246

Conference

Conference	11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014)
Abbreviated title	GIS Ostrava 2014
Country/Territory	Czech Republic
City	Ostrava
Period	27/01/14 → 29/01/14

Keywords

Bayesian belief network
GPS
Map matching
Road network

Access to Document

10.1007/978-3-319-11463-7_5

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

@inproceedings{9817bb7a8767448e9f4919e1960058cc,

title = "Detecting spatial and temporal route information of GPS traces",

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 \%.",

keywords = "Bayesian belief network, GPS, Map matching, Road network",

author = "Tao Feng and Timmermans, \{Harry J P\}",

year = "2015",

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language = "English",

isbn = "9783319114620",

series = "Lecture notes in geoinformation and cartography",

publisher = "Kluwer Academic Publishers",

pages = "61--75",

editor = "Igor Ivan and Itzhak Benenson and Jiang, \{Bin \} and Jiri Hor{\'a}k and James Haworth and Tom{\'a}s Inspektor",

booktitle = "Geoinformatics for Intelligent Transportation",

address = "Netherlands",

note = "11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014), GIS Ostrava 2014 ; Conference date: 27-01-2014 Through 29-01-2014",

}

Feng, T & Timmermans, HJP 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 . Lecture notes in geoinformation and cartography, vol. 214, Kluwer Academic Publishers, Dordrecht, pp. 61-75, 11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014), Ostrava, Czech Republic, 27/01/14. https://doi.org/10.1007/978-3-319-11463-7_5

Detecting spatial and temporal route information of GPS traces. / Feng, Tao; Timmermans, Harry J P.
Geoinformatics for Intelligent Transportation . ed. / Igor Ivan; Itzhak Benenson; Bin Jiang; Jiri Horák; James Haworth; Tomás Inspektor. Dordrecht: Kluwer Academic Publishers, 2015. p. 61-75 (Lecture notes in geoinformation and cartography; Vol. 214).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Timmermans, Harry J P

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N2 - 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 %.

AB - 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 %.

KW - Bayesian belief network

KW - GPS

KW - Map matching

KW - Road network

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PB - Kluwer Academic Publishers

CY - Dordrecht

T2 - 11th Symposium on Geoinformatics for Intelligent Transportation (GIS Ostrava 2014)

Y2 - 27 January 2014 through 29 January 2014

ER -

Detecting spatial and temporal route information of GPS traces

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Keywords

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