Flux Vision Transport - France O/D matrix sample 2019

Flux Vision relies on technical data coming from mobile networks and as such is as accurate as our capability to model the network's coverage. Large number mobile radio network technology specifics are key in the construction of the spatial and temporal analytics modeling technology used by Flux Vision. Flux Vision capitalizes a deep understanding of the implications of the evolution of the network settings and the way it communicates with the mobile device fleet (which is also in continuous evolution). This is the key element that allows Flux Vision to have a probabilistic approach for the location of mobile phone users and further build reliable mobility & attendance indicators, which are reflecting business vision, continuously alimented by experiences on market with customers.

• Anticipate user travel at the national level to adapt its resources more effectively

• Identify the different modes of transport (air, road, rail...) in detail in order to be in a complete multimodal scheme

• To have data complementary to existing data (ticketing, Tuesday and Thursday counts & other external data)

• Analyze passenger flows between zones and competitors’ business size & scope at high view

• Measure of business case to integrate new services

Example of table "OD_Totalperiod_Hours_3H" :

Period | Day | Hour | Origin | Destination| TripType | TravellersType | VolumeofTrips ----|----- Totalperiod | Weekday | [01h-07h[ | Angoulême | Champniers | Domestic | National | 973 Totalperiod | Weekday | [01h-07h[ | Angoulême | Montbron | Domestic | National | 190 Totalperiod | Weekday | [01h-07h[ | Angoulême | Roullet-Saint-Estèphe | Domestic | National | 793 Totalperiod | Weekday | [01h-07h[ | Bordeaux | Cenon | Domestic | International | 119 Totalperiod | Weekday | [01h-07h[ | Bordeaux | Cenon | Domestic | National | 3180

The total population is statistically represented thanks to our adjustment processes and not only Orange subscribers. The dataset includes annual averages made over the year 2019 (pre-Covid period). A trip is counted between two zones if the travelers have been observed at least 3 hours on both zones.

The dataset includes 13 files valuing :

• The volume of trips made between origin and destination zones by day type calculated over the full year 2019. OD_Totalperiod_3H

• The volume of trips made between the origin and destination zones by day type calculated over the full year 2019 and by age group. OD_Totalperiod_3H_Age

• The volume of trips made between origin and destination areas by day type calculated over the full year 2019 and by gender. OD_Totalperiod_3H_Gender

• The volume of trips made between origin and destination areas by day type calculated over the full year 2019 and by mode of transportation used. OD_Totalperiod_3H_TransportMode

• The volume of trips made between origin and destination areas by type of day calculated over the full year 2019 and by nationality. OD_Totalperiod_3H_Nationality

• The volume of trips made between origin and destination areas by day type calculated over the full year 2019 and by area of residence (if in an origin or destination area). OD_Totalperiod_3H_Resident

• The volume of trips made between origin and destination zones by type of day calculated over the full year 2019 and by SPC Geolife (segmentation made by Orange) OD_Totalperiod_3H_SCPGeolife

• The volume of trips made between the zones of origin and destination by type of day calculated over the full year 2019 and by type of mobile contract (professional or private) OD_Totalperiod_3H_SubscriptionType

• The volume of trips made between origin and destination areas by day type calculated over the full year 2019 and by destination arrival time. OD_Totalperiod_Hours_3H

• The volume of trips made between origin and destination zones by day type calculated over typical periods of the year 2019 (out of holidays for example). OD_Typicalperiod_3H

• The volume of trips made between origin and destination zones by day type calculated over typical periods of the year 2019 (out of holidays for example) and by destination arrival time. OD_Typicalperiod_Hours_3H

• The volume of trips made between origin and destination areas by typical days of the week calculated over typical periods of the year 2019 (out of holidays for example) OD_Typicalperiod_Seasonalitydays_3H

• The volume of trips made between origin and destination zones by day averaged by 2019 months. OD_Typicalperiod_Seasonalitymonths_3H

Variable | Signification | Possible values | Comments ----|----- Period | Defined period to create average data |Total period / Holiday period (ZoneA/B/C, summer, Christmas…) / Typical period: excluding holiday period | Total Period : aggregates over the whole study period (2019) / Typical period : aggregates excluding holidays for example DayType |Weekday / Saturday / Sunday |Weekdays (average of labour days of the year) / Saturday (average of saturdays of the year) / Sunday (average of sundays of the year) |In the sample : Weekday / Saturday / Possibility to isolate weekdays in the field “SeasonnalityDays” VolumeofTrips | Total trips | Whole value | If the number of trips is >=20 Hour | Time band of arrival at destination | 01:00-07:00, 07:00-09:00,09:00-11:00,…23:00-01:00. | Operational field, 07:00-09:00 means [07:00-09 :00[ Origin | Origin O/D | Number of zone | Number of the origin area where the mobile observed the immobility time (3h) before starting its journey Destination | Destination OD | Number of zone | Number of the destination area where the mobile observed the immobility time (3h) before starting its journey TypeFlux | Travel in France or exchanges with foreign countries | Domestic, Exit | Domestic = flow O/D in France / Exit = exchanges flow with other countries ( optional) TravellersType |Travellers’ nationality |National / International | According to the travellers' SIM card nationality TransportMode |Transport mode | Train / High speed train(LGV) / Road / Air / NR | Optional field Nationality | Traveller’s nationaliy | Countries | Optional field, to group the countries is proposed SPC Geolife | Geolife SPC class of travellers (11 values) | Suburban wealthy family / Peri-urban growing / Popular / Second home / Rural growing / Rural working class / Rural traditional / Urban middle class / Urban underprivileged / Urban growing / Urban wealthy family | Optional field according a sociological clustering of the difference living area in France. Gender |Traveller’s gender | M / F / NR | Optional field Age | Age group |<18 ; 18-24 / … / >65 ; NR | Optional field SubscriptionType | Type of mobile phone subscription of travellers | Professional / Particular / NR | Optional field IQ1 | Quality index from 0 to 100 |% | Representiveness of population : 100 : good representativity of the population / 0 : poor representativity of the population IQ2 | Quality index from 0 to 100 | %| Sample size for modal assignment: 100 : good representativity of the transport mode / 0 : poor representativity of the transport mode IQ3 | Quality index from 0 to 100 | % | Frequency and distribution of location: 100 : high accuracy of the location / 0 : low accuracy of the location IQ4 | Quality index from 0 to 100 |% | Train vs car mode estimation ambiguity: 100 : high confidence in the mode estimation / 0 : low confidence in the mode estimation

These data samples are accessible to have an example of Flux Vision Transport data, based on demand. If more local data or explanation are needed, please contact us for a free estimate quote.

Flux Vision team requires the name of the company, the full name of person In request, the position (job naming/ role) and the reason of the demand

• If you have questions about our products, contact us using the contact information below: fluxvision.support@orange.com 

• [Flux Vision by Orange Business Services] (https://www.orange-business.com/en/products/flux-vision )