flux vision - digitaltransport4africa · 8 orange business services data anonymisation 1 2 3 the...

1 Orange Business Services

Flux VisionAFD

November 9th 2017

Vers une plateforme de données numérique

collaborative et ouvertes pour améliorer le transport

public urbain en Afrique


SUMMARY

Introduction

1.Floating Mobile Data for Transportation: 3 main challenges

2.Orange Flux Vision Solution

3.Project with AFD – CETUD (Senegal) and perspectives in Africa


Orange Flux Vision Solution#1


Flux VisionReal time statistics on mobility patterns

Flux Vision converts in real time mobile network data into statistical indicators

Commercially mature product launched in several countries

Massive data inflow handling

• Capability to incorporate any kind of location data

Privacy compliant by design

• Irreversible anonymization through approved algorithms

A solution entirely designed by Orange

• 8 years and more of R&D

• Continuous technical roadmap

An extensive background in Radio Modelling and Coverage

• Accurate mobile signaling data localization

• Worldwide simulation expertise (>250 MNO’s)


Network Simulation: Spatial Uncertainity

Geo referencing Multiple technologies

GSM

Edge

LTE


Mobile Signaling: Temporal Uncertainity

Mobile 1

timeline

Mobile 2 Mobile 3

T0 T0+TS T0+2TS

Export of all detections

performed in the interval [T0;

T0+TS] to the data flow.

Export of all detections performed

in the interval [T0+TS; T0+2TS]

to the data flow.

Data Aggregation

T0+3TS

Mobile 1Mobile 2 Mobile 3Mobile 2

Detection performed Detection performed Detection performed

Mobile 1 detected

Mobile 2 detectedMobile 1 detected

Mobile 3 detected

Export of all detections performed

in the interval [T0+2TS; T0+3TS]

to the data flow.

Mobile 2 detected

Mobile 3 detected

No detection performed


Observable target behavior

Invisible Target behavior

1 2Rectify spatial-

temporal biases that are specific to the

mobile network

Extrapolate the number of mobiles to

the "standard" population

3Rectify the volume of "standard" people according to the observed

context

Counting considers

▪ Mobile clients from Orange▪ Foreign Sim cards

Roaming in Orange’s Network

To extrapolate the total population present on the area, rectification algorithms are applied to the collected data:

1. To compensate mobiles present but not captured2. To represent the "standard" population present in the area3. To avoid over or under-representations depending on the context

Data fine tuning and extrapolation


Data Anonymisation

1

2

3

The application of the text will be mandatory on 25 May 2018 in all the countries of

the European Union.

Processing of personal data such as pseudonymisation and profiling requires prior

consent from the data subject or some other legitimate basis.

Financial sanctions are foreseen for any breach of the regulation, the highest up to

4% of the annual global turnover.

The Regulation does not apply in the case of data that have been anonymized. 4

GDPR (General Data Protection Regulation)


Floating Mobile Data for Transportation#2


Some of our references

A mature commercial solution with more than 150 business customers

TransportationEvents

Retail and geomarketing Tourism


• Measure seasonal trends

• Sizing of logistical resources

• Study the impact of a particular event

Dynamics of travel demand

Exploitation Geomarketing and transports

Deep knowledge of transports demand

• Evaluate the pertinence of the existing transportation offer

• Plan the evolution of the infrastructure

• Feed mobility models

• Evaluate the attractiveness of my infrastructures

• Determine catchment areas

• Study Market shares and competing modes or networks

• Collect, analyze and verify real-time traffic data.

• Gather better information to better react

FMD for Transportation4 main axes around Transportation


Big Data, large and representative samples

Revealed preferences, in opposition to declaration :limited bias, but also limited information

Spatial temporal precision

Full Measure – all the time, everywhere

Modal split and trip purpose limitations

No additional investment, lower costs

Strengths Weaknesses

Strenghts and Weaknesses


What we deliver

Data visualizationTabular Files

Csv files

Somme de Volume Étiquettes de colonnes

Étiquettes de lignes 1 2 3 4 5 6 7 8 9 10 11

13/03/2017

1 1961 60 101 0 69 153 0 0 41 0

2 40 1794 50 0 0 91 0 0 0 0

3 132 50 3893 248 0 132 0 83 52 61 82

4 0 0 213 3971 0 0 40 62 104 41 133

5 0 0 0 0 1761 102 51 135 0 0 0

6 132 120 101 0 99 2758 0 0 0 0

7 0 50 0 0 99 0 2194 156 0 61

8 0 0 0 69 112 81 2953 0 133

9 0 71 0 0 0 0 0 1902 82 71

10 0 0 0 0 0 0 0 41 3093 174

11 0 0 0 0 0 218 62 164 4419

12 0 0 39 0 0 0 41 0 41 174

13 0 0 0 0 0 0 102 0

14 40 0 0 0 169 51 153 145 0 0 41

15 0 0 0 0 0 0 0 62 0 0

16 0 0 0 69 0 0 0 0 52 339 154

17 0 0 0 0 0

18 0 0 0 0 0 41

19 0 60 0 0 0 0 0 0 113 133

20 0 0

21 0 0 0 0 0

22 0 0 0 0 40 0 0 0 71

23 0 40 50 142 0 40 40 41 0 133 184

24 0 0 71 0 0 0 0 72 0 71 123

25 0 60 0 0 49 0 51 62 41 71 113

26 0 0

27 0 0 0 0 0 0

28 0 0

29 0 0

30 0

Total 13/03/2017 2305 2174 4610 4469 2315 3439 2650 4030 2254 4352 6107

14/03/2017

1 2002 39 131 0 80 144 0 0 0 0 0

2 0 1843 91 0 0 124 0 0 0 0 0

3 163 49 3851 233 0 124 62 72 62 103 92

4 71 49 326 3784 90 62 0 93 134 82 206

5 0 0 0 1727 62 0 114 0 61

6 102 149 71 50 110 2484 0 93 0 0 0

7 0 0 0 0 90 62 2245 124 0 82

8 40 0 0 50 80 82 103 2793 41 0 134

9 0 81 50 0 0 0 51 1708 82 92

10 0 0 0 0 51 0 41 124 3020 288

11 0 0 0 0 0 41 0 145 0 144 4379

12 0 0 0 60 0 41 51 103 0 82 355

13 0 0 0 0 0 0 0 154 51

14 40 0 0 0 70 51 134 134 0 0 41

15 0 0 0 0 0 51 41 0 51 0

16 0 0 40 70 0 0 0 114 433 175

17 0 0

18 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 175 159

20 0 0 0

21 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 72

23 0 0 50 136 0 82 82 41 0 92 185

24 0 39 81 0 0 0 41 0 41 82 195

25 0 49 0 70 60 0 0 83 62 82 61

26 0

27 0 0 0 0 0 0

OD Easily handled with Excel Pivot Tables

https://app.powerbi.com/view?r=eyJrIjoiMmMyMDJjZTgtYjRhNi00OWM0LThlM2EtMmE3Yjc0NWRjMmUyIiwidCI6IjUzM2JkOGRmLTc5Y2YtNDVhMS1hZDJiLTVhZDA1NGY1ZjBmMCIsImMiOjh9




AFD – CETUD Project#3


Strates de Dakar


Enjeux / Plus value apportée à Dakar

1

2

3

Actualiser les données de mobilité à Dakar et ainsi faciliter la planification des transports

sur le territoire :

• Compléter la connaissance des déplacements des franges périurbaines ;

• Connaître les évolutions de la mobilité lors d’événements spécifiques (ramadan,

inondation) ;

• Estimer les effets de la mise en place de la gare routière des Baux Maraîchers.

Établir le domaine de pertinence des données mobiles et les méthodologies qui

permettent de les intégrer dans des contextes similaires au Sénégal et ailleurs.

Mettre en place une méthodologie pour la tenue à jour de la connaissance de la mobilité

à Dakar entre deux enquêtes ménages déplacements (EMD) (intervalle 10-15 ans)

Participants


Origin Destination Matrix – Strates de Dakar

Area Visitor’s Origin

Angle Mousse 8358

Bambilor Dani(Nord) 1556

Bargny Extension (Est) 1666

Camberene Centre 7836

Camp le Clerc-Liberte VI Extension 26587

Champ de courses Arafat Gouye Mouride 6249

Cites Residentielles Littorales 12702

Dalifort 54062

Dieupeul 6364

Doro Aw 8744

DTK Ouest 18442

Extension Nord Ouest 4032

Fann Residence 7126

Fith Mith 14747

Grand Dakar Sud 3368

Gueule Tapee 7215

HLM 5-6 (Centre) 6227

Hors zone 4179

ICOTAF SOTIBA 55001

KM Ainoumady 16515

Catchment Area of the new Bus Station: Baux Maraîchers

Area An

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Mo

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Bam

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No

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Bar

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Exte

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(Est

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Cam

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Cam

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lerc

Ch

amp

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C

ou

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Cit

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Re

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Dal

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Die

up

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Do

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w

DTK

Ou

est

Exte

nsi

on

No

rd

Ou

est

08/09/2015

Angle Mousse 42 38 213 639 44 123 530 171 1071 1257 34

Bambilor Dani(Nord) 0 110 0 151 799 71 84 114 23 55 692

Bargny Extension (Est) 0 149 0 101 963 60 93 34 0 29 127

Camberene Centre 162 86 57 1475 84 117 397 265 484 219 36

Camp le Clerc-Liberte VI Extension 232 187 134 440 237 368 1022 5040 318 412 187

Champ de courses Arafat Gouye Mouride 0 616 664 0 215 158 230 88 42 62 3639

Cites Residentielles Littorales 55 92 103 55 454 232 1119 136 97 285 103

Dalifort 93 68 77 69 773 123 462 311 143 316 88

Dieupeul 34 44 31 81 3230 79 75 206 69 95 49

Doro Aw 705 58 33 524 788 82 191 663 206 2296 51

DTK Ouest 1399 138 93 296 942 228 458 1698 322 2606 112

Extension Nord Ouest 0 828 186 33 289 6112 108 163 79 0 68

0 10 000 20 000 30 000 40 000 50 000 60 000

Bambilor Dani(Nord)

Grand Dakar Sud

Hors zone

Champ de courses Arafat Gouye Mouride

Fann Residence

Camberene Centre

Doro Aw

Fith Mith

DTK Ouest

Dalifort

18 Orange Business ServicesOrange Footprint as Mobile Operator + Flux Vision as a Service to other Operators


Thank you!

For more information…

Marcelo Pimont Strambi

Flux Vision International Project Manager

[email protected]

mailto:[email protected]


Annex#

21 Orange Business ServicesOrange: Why the interest for people mobility?


Historical CEM (Customer Experience Management) approach

In a complex environment…


Statistical IndicatorsFlux Vision PlatformMobile Network

Flux Vision : how does it work?

Blurring, Hashing, Aggregation and Anonymization of

data on the fly

Technical fine-tuning and

extrapolation to the total population

Date Jour de la semaine Pays N Departement Continent Departement Region Visiteur Uniques sur Zone Analyse Categorie Visiteur Volume

11/11/2016 Vendredi France 31 Europe Haute-Garonne Midi-Pyrénées Jour Centre Commercial 2 Resident 34

11/11/2016 Vendredi France 31 Europe Haute-Garonne Midi-Pyrénées Mois Centre Commercial 1 Resident 37

11/11/2016 Vendredi France 31 Europe Haute-Garonne Midi-Pyrénées 3Mois Centre Commercial 1 Resident 37



















11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu Jour Centre Commercial 1 TouristeEtranger 54

11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu Jour Centre Commercial 2 TouristeEtranger 82

11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu Mois Centre Commercial 1 TouristeEtranger 47

11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu Mois Centre Commercial 2 TouristeEtranger 47

11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu 3Mois Centre Commercial 1 TouristeEtranger 47

11/11/2016 Vendredi Allemagne Inconnu Europe Inconnu Inconnu 3Mois Centre Commercial 2 TouristeEtranger 47

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu Jour Centre Commercial 1 TouristeEtranger 41

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu Jour Centre Commercial 2 TouristeEtranger 42

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu Mois Centre Commercial 1 TouristeEtranger 25

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu Mois Centre Commercial 2 TouristeEtranger 36

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu 3Mois Centre Commercial 1 TouristeEtranger 25

11/11/2016 Vendredi Belgique+Luxembourg Inconnu Europe Inconnu Inconnu 3Mois Centre Commercial 2 TouristeEtranger 36

Generic Tabular

Data

Interactive BI Tools

Origine / Destinations : examples

Origins distribution to area 14 Destinations distribution of people having

spent the night in area 10

Cartographic Analysis

Real-time data stream

Probes or CDR

CRM Database

Additional segmentations from the

MNO’s line of business data

Behavior Segmentation

Definitions

Network Simulation

Results

The system requires to know precisely the

analytics to be computed before data processing


Other Data Sources that we might use in our studies

Wi-Fi logs▪ Interface with the WIFI provider ▪ Geolocation of the visitors ▪ Creation of precise study areas

Mobile Apps GPS DataMultiple Application geolocated data

Qualitative research dataDeclarative Information via Apps

On-Site MeasuringAd-hoc testing of network communication

Open DataEx: Weather Data - Attendance impact and correlation analysis

Private DataQuantaFlow, Parking, Cameras…etc

Smart Cities and IOTConnected Devices data


Deep knowledge of transport demandInfrastructure and transport network planning

Some referencesKey elements of the technical solution

New functionalities

Link norm-atisLink BI

Brussels

Extrapolation models enrichmentprofessional mobiles / internationalroaming / M2M sim cards

Feedback for contextualization according to transport infrastructures

Main stakes• Estimation of the end to end demand: better

size the existing networks and optimize their

exploitation.

• Large samples. Reduced Costs.

• Flexibility on the analysis zoning

• Feed mobility models

• Accessibility and Environmental impact studies

Origin-Destination Matrix

• Territory divided in Origin and Destination zones• Multiple partitions, independent from administrative divisions.• Several levels of analysis (micro/macro)

• Volume of journeys from an origin to a destination with an immobility time

• Main overnight stay and activity area of the visitors of an area

• Segmentation of travel volumes by• Criteria that are measured before and during the movement

(modes of transport, travel purpose)• Criteria that are independent of the observed displacements

(socio-demographic)

Objectives

✓ Complementary data for new infrastructure development

✓ Modal share studies

✓ Mobility indicators standardization

Clients

Transport operators, Public transportation boards, Public authorities, Infrastructure Constructors and Concessions

http://preview.webgeoservices.com/ATEC/index.html

https://app.powerbi.com/view?r=eyJrIjoiY2UxYWFjMDMtNWFjMi00YzhjLWE5Y2EtZWY5NzZjOTdjYTI1IiwidCI6IjUzM2JkOGRmLTc5Y2YtNDVhMS1hZDJiLTVhZDA1NGY1ZjBmMCIsImMiOjh9


Dynamics of travel demandWhich seasonal impacts, following external events or as a result of changes in supply (new lines, construction work, etc.)

Main Stakes• Dimension logistic resources

• Feed prediction models

• Evaluate the impact on OD or modal shift dueto changes in transport supply

• Review and organize line schedules

• Negotiate with public and private actors usinga more precise basis

More frequent and richer mobile network locator eventsIncreased spatio-temporal accuracy and wider sample

Automated post-processingResults delivery in near real-time(France)

Evolution of indicators over time

• Day-to-day volumes of OD trips

• For any study period, or even permanently

• Presence and trips hour by hour

• According to time of arrival at destination

• Evolution of visitor / traveler profiles:

• Trip purpose (tourists, workers), origin, duration of stay


New functionalities

Objectives

✓ Station access planning

✓ Impact due to construction work in railway

✓ Impact of major events

Clients

Public transport authorities, Local authorities, Event organizers


ExploitationCollect, analyze and verify traffic data. Inform for action

Real-time counting

• Focus on transportation infrastructures and lines

• 1/30 minutes attendance of stations, lines, roads, railway sections and various places

• Inbound / Outbound Volumes

• Real-time monitoring of mobile groups moving in a coherent way on a rail network:

• Virtual beacons: passing, identification and passage counting

• Integration into your environment• KPI push by sFTP, csv or xml files

Main Stakes• Provide real-time reliable indicators.

• Warnings - Traveler Information

• Monitor service operations

• Adapt to the gradual rise of competition.

More frequent and richer mobile network locator eventsIncreased spatio-temporal accuracy and wider sample

Automated post-processingResults delivery in near real-time(France)

Enrichment with other data sources (GPS, Operations, etc.)


New functionalities

Objectives

✓ Inbound / Outbound Underground Stations

✓ 15 minutes attendance of a Train Station

✓ Tracking of regional trains

Clients

Railway operators, Public transportation boards


Geomarketing et transportsEvaluer l’attractivité de mes infrastructures

Catchment Areas and Competitive Analysis

• Territory cut out in customized isochrones• areas of interest / areas of origin

• Understanding the profile and behavior of travelers• Origin• Duration of Presence• Purpose• Socio-demo

• Evaluation of the impact of multi-media advertising campaigns

New Standard Offers

BI and Data Visualisation

Indoor Analysis (Wi-Fi, Dedicated Antennas)

Main stakes• Knowing the catchment areas of a railway

station, airport, etc.

• Study your market share, penetration rate and

competing modes or networks

• Segmentation of observed populations and

valuation of commercial spaces

• Integrate new services (car-sharing, car-pooling,

bicycling, etc.) with a global approach at stations

and transport nodes

• Valuation of the rental or advertising spaces of

the nodes / axis of a transport infrastructure

Distribution of passengers arriving at the airport into the multiple train stations


New functionalities

Objectives

✓ Place of residence of the travelers of an Airport

✓ Comparison of catchment areas between modes

✓ Impact of targeted publicity campaign

Clients

Airports, Road / Rail operators, Infrastructure managers


Zoning: The coverage results depend on the network architecture and antennas density

Areas of Interest Origin Destination or Catchment Areas

flux vision - digitaltransport4africa · 8 orange business services data anonymisation 1 2 3 the...

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