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Trip extraction for traffic analysis using cellular network data
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0353-6284
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5961-5136
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6405-5914
Former Tele2.
2017 (English)In: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) / [ed] IEEE Italy Section, Naples: IEEE Press, 2017, p. 321-326Conference paper, Published paper (Refereed)
Abstract [en]

To get a better understanding of people’s mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low. 

Place, publisher, year, edition, pages
Naples: IEEE Press, 2017. p. 321-326
Keywords [en]
Global Positioning System, cellular radio, data communication, telecommunication traffic, Sweden, cellular network data, signalling data, traffic analysis, trip extraction, Antennas, Cellular networks, Data mining, Global Positioning System, Google, History, Spatial resolution
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-140906DOI: 10.1109/MTITS.2017.8005688ISI: 000426813700055ISBN: 978-1-5090-6484-7 (electronic)OAI: oai:DiVA.org:liu-140906DiVA, id: diva2:1141579
Conference
Models and Technologies for Intelligent Transportation Systems (MT-ITS), 26-28 June 2017, Naples, Italy
Projects
MOFT
Funder
VinnovaAvailable from: 2017-09-15 Created: 2017-09-15 Last updated: 2019-07-15Bibliographically approved
In thesis
1. Analysis of Travel Patterns from Cellular Network Data
Open this publication in new window or tab >>Analysis of Travel Patterns from Cellular Network Data
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Traffic planners are facing a big challenge with an increasing demand for mobility and a need to drastically reduce the environmental impacts of the transportation system at the same time. The transportation system therefore needs to become more efficient, which requires a good understanding about the actual travel patterns. Data from travel surveys and traffic counts is expensive to collect and gives only limited insights on travel patterns. Cellular network data collected in the mobile operators infrastructure is a promising data source which can provide new ways of obtaining information relevant for traffic analysis. It can provide large-scale observations of travel patterns independent of the travel mode used and can be updated easier than other data sources. In order to use cellular network data for traffic analysis it needs to be filtered and processed in a way that preserves privacy of individuals and takes the low resolution of the data in space and time into account. The research of finding appropriate algorithms is ongoing and while substantial progress has been achieved, there is a still a large potential for better algorithms and ways to evaluate them.

The aim of this thesis is to analyse the potential and limitations of using cellular network data for traffic analysis. In the three papers included in the thesis, contributions are made to the trip extraction, travel demand and route inference steps part of a data-driven traffic analysis processing chain. To analyse the performance of the proposed algorithms, a number of datasets from different cellular network operators are used. The results obtained using different algorithms are compared to each other as well as to other available data sources.

A main finding presented in this thesis is that large-scale cellular network data can be used in particular to infer travel demand. In a study of data for the municipality of Norrköping, the results from cellular network data resemble the travel demand model currently used by the municipality, while adding more details such as time profiles which are currently not available to traffic planners. However, it is found that all later traffic analysis results from cellular network data can differ to a large extend based on the choice of algorithm used for the first steps of data filtering and trip extraction. Particular difficulties occur with the detection of short trips (less than 2km) with a possible under-representation of these trips affecting the subsequent traffic analysis.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 32
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1845
National Category
Transport Systems and Logistics Communication Systems Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-157139 (URN)10.33984/lic.diva-157139 (DOI)liu-tek-lic 2019 (Local ID)9789176850558 (ISBN)liu-tek-lic 2019 (Archive number)liu-tek-lic 2019 (OAI)
Presentation
2019-06-12, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 10:15 (English)
Opponent
Supervisors
Available from: 2019-05-29 Created: 2019-05-29 Last updated: 2019-05-29Bibliographically approved

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Breyer, NilsGundlegård, DavidRydergren, Clas

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Citation style
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