Public transport

Train kilometre

Distance in kilometres for trains and trams that are made up of more than one linked carriage.

Vehicle kilometre

Total distance in kilometres (i.e. including positioning run and other out of route journeys).

Passenger kilometre

Passenger kilometre is an inter modal transport statistic. It is found by mutliplying the number of passengers by their travelled distance.

Place kilometre

Total seating capacity and standing room multiplied by distance in route (i.e. excluding positioning runs and other out of route journeys).

Seat kilometer

A measure of of capacity in passenger transport. Found by multiplying the number of seats by the distance that these seats are beeing offered to passengers.

Vehicle hours

Total vehicle hours (including control times, positioning runs and other out of route journeys).

Ticket revenue

The traffic establishments’ revenues from public transport users’ purchase of travel tickets.

Fare (passenger)

One boarding, i.e. part journey.

Total expenditure

All ordinary costs related to running public transport, i.e. the sum of administration costs, operating costs and capital expenditure.

Subsidies

The authorities’ net subsidy for transport services, i.e. the total service subsidy (including remuneration for school bus services) where the operators run on net contracts and retain ticket revenues. Where the operators run on gross contracts, the authorities’ subsidy shall correspond to the sum of the contract minus the ticket revenues.

Utilisation of capacity

Passenger kilometres as a percentage of seat kilometres.

Cost absorption

Ticket revenues as a percentage of total costs. (out from 2015)

Statistics Norway’s current Standard Industrial Classification 2007 (SIC2007) is based on the EU’s NACE standard.

Name: Public transport
Topic: Transport and tourism

Division for Energy, Environmental and Transport Statistics

Regions, counties and urban areas.

As a result of the merging of municipialities in 2020, several time series were joined together. The following former municipialities were included in defining urban areas in 2020: Røyken, Hurum, Fet, Sørum, Svelvik, Finnøy, Forsand, Fusa, Ørskog, Skodje, Haram and Sandøy. Haram will be included in Ålesund urban area from year 2024.

Urban areas

Nedre Glommen – Sarpsborg and Fredrikstad

Oslo – Oslo, Asker, Bærum, Nittedal, Nordre Follo, Lørenskog and Lillestrøm

Drammen – Drammen and Lier

Tønsberg – Tønsberg and Færder

Grenland – Porsgrunn, Skien, Siljan and Bamble

Arendal – Arendal and Grimstad

Kristiansand – Kristiansand and Vennesla

Stavanger – Stavanger, Sandnes, Sola and Randaberg

Haugesund – Haugesund and Karmøy

Bergen – Bergen, Askøy, Bjørnafjorden and Øygarden

Ålesund – Ålesund, Sula and Giske

Trondheim – Trondheim and Malvik

Bodø – Bodø

Tromsø – Tromsø

Regions

Northern Norway - Nordland, Troms and Finnmark

Central Norway - Trøndelag, Møre og Romsdal

Western Norway - Rogaland og Vestland

Southern Norway - Agder

Eastern Norway - Oslo, Viken, Innlandet, Vestfold og Telemark

Frequency: Quarterly and annually

Timeliness (under normal conditions):

Quarterly: Will normally be published within 3 months of the end of the quarter.

Annually: Will normally be published within 6 months of the end of the year.

Some main figures are reported on voluntary bases to Eurostat and UNECE.

Collected and revised data are stored securely by Statistics Norway in compliance with applicable legislation on data processing.

Statistics Norway can grant access to the source data (de-identified or anonymised microdata) on which the statistics are based, for researchers and public authorities for the purposes of preparing statistical results and analyses. Access can be granted upon application and subject to conditions. Refer to the details about this at Access to data from Statistics Norway.

Statistics Norway's previous statistics on scheduled road transport were last published for the financial year 1997, while the Ministry of Transport and Communications' data capture based on the N-016 set of forms was concluded in 2000. This quickly led to an incomplete statistics base on public transport. The Ministry therefore took the initiative to establish new statistics on public transport, which would cover bus, boat, suburban railway, tram and railway.

The purpose of the statistics is to provide continuous, up-to-date, strategic key figures on public transport, which can be used by the transport industry itself, local and central authorities, as well as public transport planners at different levels. The statistics are financed by the Ministry of Transport and Communications.

The statistics are primarily used by public authorities and research institutes in connection with transport planning. Transport establishments and transport planners are also important user groups. Internally in Statistics Norway, the material is included in calculations of domestic transport capacity, and provides a good basis for analysing the competitive situation, amongst other things.

No external users have access to statistics before they are released at 8 a.m. on ssb.no after at least three months’ advance notice in the release calendar. This is one of the most important principles in Statistics Norway for ensuring the equal treatment of users.

The results for scheduled road transport are, for some variables, comparable to a certain extent with the previous Statistics on scheduled road transport, which were last published in 1997. The comparison is complicated by the fact that major structural changes have taken place in the industry in recent years.

Transport capacity figures are collected in KOSTRA for the part of the scheduled road transport in receipt of subsidies, the liners and rail system, but the new public transport statistics also cover transport that does not qualify for subsidies. In direct comparisons at both county and establishment level, substantial deviations were discovered for a number of pupil fares. KOSTRA often has higher figures in this connection. Registration of pupil fares is generally poor, whereby the figures are on the whole administrative estimates. The greatest deviations are investigated and there is every reason to believe that the KOSTRA figures are too high and public transport figures are too low. The transition to electronic ticketing revealed that the scope of fares using a pupil pass is considerably lower than previously assumed.

The structure survey provides some key financial figures for establishments and enterprises for the same industries that are covered by the public transport statistics. However, the cost and income concepts are rather different since the structure survey covers all revenues and costs of the establishments and enterprises, while the public transport statistics only cover revenues and costs linked to the running of the public transport service.

The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act).

The population is all public transport establishments within the scheduled road transport industry (excluding those exclusively providing school services), the liner industry (excluding Hurtigruta and ferry routes), trams, the underground and railway in Norway. Tour buses, tour boats and other tourism businesses involved solely with experience, sightseeing and such are not regarded as public transport.

All counties are included from reporting year 2015 and Q1 2016.

Questionnaires completed by all establishments in the various industries. New questionnaries to county authority from 2016.

Full count.

Collection of data

All information is reported online via the reporting channel Altinn. Groups/parent companies/head offices may report for branches upon agreement with Statistics Norway.

Editing

Editing is defined here as checking, examining and amending data.

Statistical and logical controls form part of the data registration routine for checking registration errors or other data errors. Establishments are contacted in the event of a lack of information or other significant errors. Certain types of incomplete responses are estimated by using the data from other responses for the same variable.

Estimations

The survey is based on a full count of the relevant enterprises. Partial withdrawals may occur, and values for certain variables can be estimated by Statistics Norway based on previous answers, or by using information on correlations between other answers in the questionnaire. The data are not seasonally adjusted.

For monthly, term and quarterly figures, there are often significant seasonal variations which make it difficult to directly interpret the development from period to period.To facilitate the direct interpretation of such time series, we seasonally adjust many number series.

Seasonally adjusted figures will, however, be somewhat more uncertain, so that when reading the calculated development from seasonally adjusted figures, one should take into account the increased uncertainty in the interpretation of the results.

Seasonally adjusted figures are calculated for the total number of passengers broken down by mode of transport.

Employees of Statistics Norway have a duty of confidentiality.

Statistics Norway does not publish figures if there is a risk of the respondent’s contribution being identified. This means that, as a general rule, figures are not published if fewer than three units form the basis of a cell in a table or if the contribution of one or two respondents constitutes a very large part of the cell total.

Statistics Norway can make exceptions to the general rule if deemed necessary to meet the requirements of the EEA agreement, if the respondent is a public authority, if the respondent has consented to this, or when the information disclosed is openly accessible to the public.

More information can be found on Statistics Norway’s website under Methods in official statistics, in the ‘Confidentiality’ section.

The principal rule of the Statistics Act is that Information shall under no circumstances be published in such a way that it may be traced back to the supplier of any data or to any other identifiable individual. Figures broken down by urban area for bus are therefore previously not published. Late 2007 publishing of non-economical figures from counties was authorised.

Considerations to confidentiality mean that financial figures cannot be published, with the exception of total ticket revenues for rail services.

The public transport survey was first published on 31 October 2006, with annual data from 2004 and quarterly data for the first quarter of 2005 to the first quarter of 2006 in November 2006.

NSB uses a new method for calculating passenger numbers. Figures are included from the 1st quarter of 2012 in the new method, which means there is a slight break in the statistics from 2011 to 2012.

Questionnaires are reviewed thoroughly, and unclear wording of questions and other obscurities are resolved during this process. However, it is possible for establishments to misunderstand one or more questions. Misinterpretations and misunderstandings in relation to the questions on school bus services and line kilometres have resulted in this data being incomplete.

Non-response units in the public transport survey will be either establishments we have not had contact with due to addressee errors etc. but who are not registered as exempt, or establishments that refuse to answer and are given a compulsory fine.

Non-response of units is approximately 2 per cent. All major units are included. Non-response of units leads to skewed results.

The Central Register of Establishments and Enterprises is used both to define the population and to retrieve information such as industry codes. This can form the basis for register errors that can affect the uncertainty in the statistics. The most common errors are those that result from time lags in the registrations. Such lags can be due to delayed reporting to the registers or the fact that changes are normally registered some time after they have occurred. The consequence is that the registers are not entirely up-to-date at any given time, which can lead to out-of-date information being used in the statistics.

Passenger kilometres is a key unit of measurement in all transport statistics. As a rule, the data must be estimated by the establishments, and are often based on averages. This figures are the statistics least reliable.

The distinction between &“city and densely populated area´´ and &“other intra-county traffic´´ has a different interpretation in the bus industry. Traffic in local centres in rural Norway is normally reported under city and densely populated area, while there is a tendency in areas close to cities for everything to be regarded as other intra-county traffic.

Separating passenger figures and ticket revenues, by type of ticket, has proved to be difficult &– particularly in densely populated areas with a number of different tick types. In the Oslo area, for instance, the same ticket can be used on boats, buses, trams and the underground.

A revision is a planned change to figures that have already been published, for example when releasing final figures as a follow-up to published preliminary figures. See also Statistics Norway’s principles for revisions.

Quarterly figures are recalculates for the last 2 years. Large changes are qouted.

For the annual statistics, also smaller changes are qouted.

Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

For more information on seasonal adjustment: ”Generelt om sesongjustering”.

Seasonally adjusted series

Seasonally adjusted figures are calculated for the total number of passengers broken down by mode of transport.

Due to variable public holidays, holiday arrangements and seasonal variations in weather, the use of public transport varies throughout the year. This makes a direct comparison from one quarter to the next difficult. In order to be able to follow this underlying development from quarter to quarter without being affected by such fixed variations, the figures are seasonally adjusted.

Pre-treatment routines/schemes

Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.

Calendar adjustment

To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals). The regression variables for the calendar adjustment are adapted to reflect the working days, public holidays and so forth specific to Norway.

Methods for trading/working day adjustment

No correction.

Correction for moving holidays

Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.

National and EU/euro area calendars

Depending on what suits best, either a calendar based on Norwegian holidays and public holidays or a calendar based on an average number of working days for the various countries within the EU/EU area is used.

Treatment of outliers

The series are checked for outliers of different types. Once identified, outliers are explained/modelled using all available information. Outliers for which a clear interpretation exists (strikes, consequences of (government) policy changes etc.) are included as regressors in the model.

Model selection

Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not. Model selection is primarily automatic, but in some cases models are selected manually.

Decomposition scheme

The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.

Manual decomposition scheme selection after graphical inspection of the series.

Choice of seasonal adjustment approach

X-13-ARIMA

Consistency between raw and seasonally adjusted data

Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

Do not apply any constraint.

Direct versus indirect approach

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

Direct approach where the raw data are aggregated and the aggregates and components are then directly seasonally adjusted using the same approach and software. Any discrepancies across the aggregation structure are not removed.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.

The whole time series is used to estimate the model and the correction factors.

General revision policy

Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.

Seasonally adjusted data are revised when data for a new quarter is published.

Concurrent versus current adjustment

The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.

Horizon for published revisions

The whole series is revised in cases of major revisions of raw data.The revision period for the seasonally adjusted results is limited to 3-4 years (preferably 4) prior to the revision period of the unadjusted data, while older data are frozen.

Evaluation of seasonally adjustment data

Evaluation of quality based only on graphical inspection and descriptive statistics.

Quality measures for seasonal adjustment

No quality measures for seasonal adjustment assessment are used.

Seasonal adjustment of short time series

All series are sufficiently long to perform an optimal seasonal adjustment.

Treatment of problematic series

None of the published series are viewed as problematic.

Data availability

Raw and seasonally adjusted data are available.

Press releases

In addition to raw data, seasonally adjusted data are presented.

”Generelt om sesongjustering”