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Validation of Mobile Network Data in Producing Origin- Destination Matrices

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Validation of Mobile Network Data in Producing Origin- Destination Matrices Reetu Jormakka Validation of mobile network data in producing Origin- Destination matrices Thesis submitted for examination for the degree of Master of Science in Technology. Espoo 22.11.2019 Supervisor: Claudio Roncoli Instructor: Tapio Levä Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi Abstract of master's thesis Author Reetu Jormakka Title of the thesis Validation of mobile network data in producing Origin-Destination matrices Master programme Spatial Planning and Transportation Code ENG26 Engineering Thesis supervisor Claudio Roncoli Thesis advisor Tapio Levä Date 22.11.2019 Number of pages 93 Language English Abstract The rapid development in telecommunication networks during last years has made it possible to study human travel behaviour effectively from mobile network data. The combination of passive and active signalling events gathered by mobile network operators allow analysing movements of people with full longitudinal and spatial coverage. Therefore, recent years have seen an increasing interest in utilizing mobile network data in transportation studies, as an alternative or a complementary data source for conventional transport data. This study validates the capability of mobile network data to produce long-distance origin-destination matrices in Finland. Features that are being validated include trip counts, seasonal trip count changes and modal split. As reference data sources of the study, the National Travel Survey 2016, HELMET-transport demand model (Transport model by HSL) and LAM-data (automated traffic census) are used. Validation is done by analysing correlations between mobile network data and the reference data sources. By being able to demonstrate the validity and reliability of mobile network data usage in producing origin-destination matrices, cost-effectiveness and more accurate methods to gather information from long-distance transportation can be provided for the field in general. The overall results of the study are in line with the few similar related studies that have been conducted. The thesis work suggests that mobile network data is capable of producing more reliable trip counts from sparsely populated areas than the National Travel Survey. In addition, it seems to be more capable of capturing the high summer peak in long-distance travelling in Finland. The results regarding modal split are promising, but more studies regarding the modal detection will be needed. Keywords Mobile network data, Travel behaviour, Origin-Destination Matrix, ODM, The National Travel Survey Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi Diplomityön tiivistelmä Tekijä Reetu Jormakka Työn nimi Mobiiliverkkodatan käytön validointi lähtö-määränpää -matriisien luomisessa Maisteriohjelma Maankäytön suunnittelu ja Koodi ENG26 liikennetekniikka Työn valvoja Claudio Roncoli Työn ohjaaja Tapio Levä Päivämäärä 22.11.2019 Sivumäärä 93 Kieli englanti Tiivistelmä Matkapuhelinverkkojen viime vuosien nopea kehitys on mahdollistanut yhä tarkemman matkapuhelinten solupaikannuksen. Teleoperaattoreiden keräämä passiivisten ja aktiivisten matkapuhelinverkon signaalihavaintojen yhdistelmä mahdollistaa ihmisten liikkumiskäyttäytymisen tutkimisen kattavasti sekä ajallisesti että alueellisesti. Viime aikoina matkapuhelinverkkodatan hyödyntäminen liikennetutkimuksissa on tästä syystä herättänyt kasvavaa kiinnostusta perinteisten tiedonkeruumenetelmien korvaajana ja täydentäjänä. Tämä tutkimus validoi mobiiliverkkodatan käyttöä lähtö-määränpää -matriisien luomisessa Suomen pitkän matkan liikenteessä. Validoitavia ominaisuuksia ovat matkamäärät, matkamäärien vuodenajoittainen vaihtelu, sekä matkojen kulkumuotojakauma. Referenssiaineistona työssä käytetään Suomen henkilöliikennetutkimusta, HELMET-liikennemallia ja LAM-dataa. Validointi suoritetaan analysoimalla mobiiliverkkodatan ja referenssiaineistojen välisiä korrelaatioita. Osoittamalla mobiiliverkkodatan käytettävyys lähtö-määränpää - matriisien luomisessa, liikennesuunnittelun kustannustehokkuutta ja keinoja tarkemman tiedon keräämiseen pitkämatkaisesta liikkumisesta voidaan edistää. Työn tulokset ovat linjassa aiemman tutkimuksen kanssa. Tulokset näyttävät mobiiliverkkodatan olevan kykenevä tuottamaan lähtö-määränpää -tietoa haja- asutusalueilta luotettavammin kuin Henkilöliikennetutkimus. Lisäksi, mobiiliverkkodata näyttää pystyvän observoimaan kesän lomakauden matkapiikin tarkemmin kuin Henkilöliikennetutkimus. Tulokset mobiiliverkkodatan kulkumuototunnistukseen ovat lupaavia, mutta lisää tutkimusta tarvitaan näiden havaintojen vahvistamiseen. Avainsanat Matkapuhelinverkkodata, liikkumiskäyttäytyminen, Lähtö-määränpää - matriisi, Henkilöliikennetutkimus Contents 1 Introduction ....................................................................................................................... 7 1.1 Background .................................................................................................................. 7 1.2 Purpose of the Study .................................................................................................... 8 1.3 Framework of the study ............................................................................................... 9 1.4 Limitations of the Research Scope ............................................................................. 11 1.5 Structure of the Thesis ............................................................................................... 11 2 Theory .............................................................................................................................. 13 2.1 History and applications of mobile network data ...................................................... 13 2.2 Positioning capabilities of cellular mobile network ................................................... 14 2.3 Base Stations and Cells .............................................................................................. 15 2.4 Event-Driven and Network-Driven Mobile Network Data ........................................ 16 2.5 Cell Global Identity (CGI) and other location detection methods ............................. 17 2.6 User Assignment ........................................................................................................ 18 2.7 Trip and activity assignment ...................................................................................... 21 2.8 Origin-Destination Matrix .......................................................................................... 24 2.9 Representativeness ..................................................................................................... 25 2.10 Privacy...................................................................................................................... 27 2.11 Conventional Transport Data ................................................................................... 28 2.12 Comparison Between Conventional Transport Data and Mobile Network Data ..... 30 2.13 Modal Split ............................................................................................................... 32 3 Data & Data Processing ................................................................................................. 33 3.1 Telia’s Mobile Network Data Infrastructure .............................................................. 33 3.1.1 Physical infrastructure ......................................................................................... 34 3.1.2 Trip Generation ................................................................................................... 35 3.1.3 Geographical mapping ......................................................................................... 37 3.1.4 Extrapolation ....................................................................................................... 40 3.1.5 Aggregation and Anonymization ........................................................................ 42 3.1.6 Trip Duration Feature .......................................................................................... 43 3.1.7 Via-feature ........................................................................................................... 43 3.1.8 Data Model .......................................................................................................... 44 3.1.9 Limitations ........................................................................................................... 44 3.2 National Travel Survey .............................................................................................. 44 3.2.1 Data Gathering ..................................................................................................... 45 3.2.2 Representativeness .............................................................................................. 46 3.2.3 Data Model .......................................................................................................... 47 3.2.4 Limitations ........................................................................................................... 47 3.3 Other Reference Data Sources ................................................................................... 48 4 Methods ............................................................................................................................ 49 4.1 Project flow Chart ...................................................................................................... 49 4.2 Analysis parameters ..................................................................................................
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