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Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0003-0353-6284
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0001-6405-5914
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-5961-5136
2018 (Engelska)Ingår i: The Journal of urban technology, ISSN 1063-0732, E-ISSN 1466-1853, nr 2, s. 85-104Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows.

Ort, förlag, år, upplaga, sidor
Taylor & Francis, 2018. nr 2, s. 85-104
Nyckelord [en]
Cellular network data, route estimation, network assignment
Nationell ämneskategori
Transportteknik och logistik
Identifikatorer
URN: urn:nbn:se:liu:diva-144541DOI: 10.1080/10630732.2017.1386939ISI: 000437198100006OAI: oai:DiVA.org:liu-144541DiVA, id: diva2:1177906
Anmärkning

Funding agencies: Swedish Governmental Agency for Innovation Systems (VINNOVA)

Tillgänglig från: 2018-01-26 Skapad: 2018-01-26 Senast uppdaterad: 2019-05-29
Ingår i avhandling
1. Analysis of Travel Patterns from Cellular Network Data
Öppna denna publikation i ny flik eller fönster >>Analysis of Travel Patterns from Cellular Network Data
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2019. s. 32
Serie
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1845
Nationell ämneskategori
Transportteknik och logistik Kommunikationssystem Datavetenskap (datalogi) Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:liu:diva-157139 (URN)10.33984/lic.diva-157139 (DOI)liu-tek-lic 2019 (Lokalt ID)9789176850558 (ISBN)liu-tek-lic 2019 (Arkivnummer)liu-tek-lic 2019 (OAI)
Presentation
2019-06-12, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-05-29 Skapad: 2019-05-29 Senast uppdaterad: 2019-05-29Bibliografiskt granskad

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Breyer, NilsRydergren, ClasGundlegård, David

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