Fetch a set of indicators that give an overview of the mobility trends per person in a region
Transport is a major contributor to Norway’s climate impact, both when considering consumptionbased and regional accounting. Additionally, side effects such as congestion, air pollution, and noise pollution plague urban mobility. This endpoint offers an overview of the indicators which have a major impact on the transport footprint of Norwegians. This enables municipalities, property planners, and others concerned with transport patterns to track the change in these indicators over time, and use them for policy formulation and mobility planning.
All inputs, outputs and defaults can be seen in the Track Transport technical API docs. Data can also be delivered as a CSV file, but please note there will be some differences when compared to API output.
Inputs
Required:
 areaID: a single basic statistical unit or municipality for which you wish to retrieve indicators. Documentation on valid areaIDs can be found here
Optional:
 dataFromDate: the date for which you wish to receive data. Defaults to the current date, which will yield the most recent dataset we have available. Data from the proceeding month will become available by the 15th of the current month (e.g. January data becomes available on February 15th)
 timeRange: the time range the indicators should be returned for. The default is for a single calendar month, with a single calendar year as an option. Year returns the average values for the whole year (which for kilometres travelled is the monthly average)
Outputs
For the given area, the user receives:
 areaID and name of the area
 A list of all available indicators for the area, each containing
 An indicator name
 An indicator value
 Unit of indicator value

originAreaId: The areaID of the origin of the indicator. If we don’t have data available for the area you requested (e.g. a basic statistical unit), we will return data from the closest parent (e.g. the municipality or country which the basic statistical unit is contained in) containing the missing indicator.
The following indicators are returned:
Indicator Name 
Description 
Resolution 
kmFlownDomestic 
kilometres flown, domestically, per person 
Basic statistical unit 
kmFlownContinental 
kilometres flown within Europe, per person 
National 
kmFlownIntercontinental 
kilometres flown, outside of Europe, per person 
National 
kmDrivenPerPersonForWork 
kilometres driven by car per person for work 
Basic statistical unit 
kmDrivenPerPersonForOther 
kilometres driven by car per person for leisure 
Basic statistical unit 
kmByBoatPerPersonForWork 
kilometres travelled on commercial boat lines per person for work 
Basic statistical unit 
kmByBoatPerPersonForOther 
kilometres travelled on commercial boat lines per person for leisure purposes 
Basic statistical unit 
kmByBusPerPersonForWork 
kilometres travelled by bus per person for work purposes 
Basic statistical unit 
kmByBusPerPersonForOther 
kilometres travelled by bus per person for leisure purposes 
Basic statistical unit 
kmByTrainPerPersonForWork 
kilometres travelled by train per person for work 
Basic statistical unit 
kmByTrainPerPersonForOther 
kilometres travelled by train per person for leisure purposes 
Basic statistical unit 
fossilCarsPerCapita 
The number of fossil (diesel + gasoline) cars owned per capita 
Municipality 
electricCarsPerCapita 
The number of electric cars owned per capita 
Municipality 
hybridCarsPerCapita 
The number of hybrid (both plugin and otherwise) cars owned per capita 
Municipality 
adultPopulation 
The number of adult residents (above 18 years of age) 
Basic statistical unit 
childPopulation 
The number of child residents (below 18 years of age) 
Basic statistical unit 
It is important to note that the resolution influences the accuracy of an indicator: BSU level indicators would be the most accurate, followed by Municipality level indicators, and lastly, National level indicators.
Calculations and data sources
Telia uses aggregated cell phone position data to estimate mobility patterns classified by mode of transport, which creates a valuable data set of mobility streams in an area. The classification is based on the speed at which the user is moving, in combination with the infrastructure they are using. This means that the data does not distinguish between cars, buses and bicycles in the case that they are all moving at the same speed, on the same road. Walking is also difficult to distinguish with confidence based on this method.
Mobility patterns are tracked on a 250x250m grid, which is then mapped to basic statistical units for Ducky’s use. Since the position of a cell phone user is tracked using a cell tower (rather than GPS), the data has limited spatial resolution, and errors in positional allocation may occur e.g. if cell tower signals reflect off bodies of water.
Due to GDPR, any trips with less than 5 users will not be included in the data set. This means that not all mobility streams are included in the data. Additionally, tracking only lasts one day at a time, so wherever a person wakes up will be assumed to be their home location. This means that there are likely miscounts due to e.g. tourism, where the trips of a person who is visiting a place are attributed to people living in that area.
In addition to being classified by infrastructure, trips are divided by purpose as either work or other, work being a commute from a home location to an assumed place of work and viceversa. The place of work is assumed to be wherever a cell phone user travels to from where they woke up, and spend more than one hour before 4 pm on the day in question. The trips that don’t fall into the above category are classified as leisure/nonwork trips.
As highlighted earlier, the Telia data doesn’t distinguish between the different modes of transport using the same infrastructure system (cars, buses and heavy motor vehicles all use road networks). To address this, we've developed a method attributing total road distances from Telia to car and bus travel. By utilising SSB statistics on road traffic volumes, we estimate the contribution of different modes of road transport to the total traffic volume. This information combined with occupancy rates and average seat numbers for cars and buses, is used to get an estimate of the respective distances travelled by cars and buses.
Data from Statistics Norway (SSB) is used to calibrate the Telia data, under the assumption that the total traffic volumes recorded by SSB are more reliable than Telia. Many tables from SSB give numbers on a national or municipal level for e.g. total passenger kilometres travelled in a year. Additionally, population and vehicle ownership statistics are extracted from SSB.
Data from Avinor’s Flight travellers’ habit report (2019) is used to estimate an average Norwegian’s international flight habits in combination with data on outbound leisure trips from SSB.
Below is a detailed list of all the tables we use from SSB for our calculations:
SSB Table Number 
Name of Table 
What data do we extract 
06673 
Public transport by bus. City area routes 
Utilisation capacity and average number of seats for buses in all city areas 
06669 
Public transport by bus. Intracounty routes 
Utilisation capacity and average number of seats for buses in all city areas 
06921 
Trips, by mode of transport and type of trip 
International holiday flight trips in a month 
12579 
Road traffic volumes, by home municipality of vehicle owner 
Road traffic volumes (in million km) for different types of road transport: all road traffic, cars and buses 
12575 
Road traffic volumes, by type of vehicle and age of vehicle 
Vehicle kilometres of passenger cars and all road transport 
04362 
Population, by sex and age 
Population per BSU, segregated between adults and children 
07459 
Population, by sex and age 
Population per Municipality, segregated between adults and children 
11823 
Registered vehicles, by type of transport and type of fuel 
Registered vehicles per municipality: cars and motorbikes (gasoline, diesel, hybrid and electric) 
04780 
Goods and passenger transport by rail, by type of transport 
Yearly domestic passenger kilometres from train travel 
03982 
Domestic passenger transport, by mode of transport 
Domestic passenger kms for boats, aeroplanes and different modes of road transport (private cars, taxis, rental cars etc.) 
301 
Classification of municipalities 
Municipality area codes and names (note that we currently use the 20202023 classifications) 
1 
Classification of Basic Statistical Units 
BSU area codes and names (note that we currently use the 20202023 classifications) 