Freight Trucking Statistics (FTS)
Detailed information for 2019
The purpose of the Freight Trucking Statistics Program (FTS) is to measure commodity movements done by Canadian road carriers.
Data release - TBD
Information from this program is used for transportation activity analysis and economic studies. As well, it represents a key input to the Canadian Freight Analysis Framework (CFAF), which is used to assess highway capacity, forecast traffic, evaluate investments in infrastructure, examine trade flows, and analyze policies such as road pricing and multimodal freight programs.
The survey data are used by federal and provincial governments, trucking associations, members of the industry, universities, and research institutions.
Collection period: Quarterly
- Q1 - January to March
- Q2 - April to June
- Q3 - July to September
- Q4 - October to December
- Transportation by road
Data sources and methodology
The Freight Trucking Statistics (FTS) target population represents all road motor carriers that transport any type of goods, including commodities, parcels, construction material and tools, used household and office furniture, gravel and top soil, heating oil, etc.
FTS is not an industry-based survey, but an activity-based survey. The main condition of inclusion is to have powered units (trucks, tractors, vans, etc.) that transports goods whether they generate revenue or not. For Hire (e.g., public) and private carriers (e.g., firms that haul their own goods) are included. Movers and courier businesses, as well as businesses from any industrial sectors that operate commercial trucks are in scope. For example, a construction company that operate a small fleet of trucks to haul their own machinery from site to site is included.
FTS collects data using electronic data reporting. During the initial call, the respondent will be informed that they will be receiving an email from Statistics Canada aiming at providing them with instructions on how to access a dedicated E-File Transfer (EFT) vault. Once they access their EFT vault, they will be asked to upload their electronic files of shipments data for every reporting period. As much as possible, the respondent is asked to comply with the suggested template provided in the EFT vault. When needed, a Statistics Canada interviewer will visit the business to inform the respondent on how to report their data using the EFT Vault.
This is a sample survey with a cross-sectional design.
The purposive panel of carriers is selected by analysts according to pre-set criteria and does not feature sample rotation. A non-probabilistic selection, commonly used with administrative data and other alternative sources, is better suited for maintaining a long-term relationship with electronic data providers.
Data collection for this reference period: 2019-01-01 to 2020-12-30
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
FTS uses Electronic File Transfer (EFT): All companies send their data electronically.
View the Questionnaire(s) and reporting guide(s).
Once all necessary information for the survey is collected, a series of verifications takes place to ensure that the records are consistent and that collection and capture of the data do not introduce errors. Reported data are examined for completeness and consistency using automated edits coupled with manual review. Outliers, i.e., respondents reporting extremely large values, are processed manually.
Missing values and data found in error are imputed by an automated system. The system imputes the data using different imputation rules depending on the shipment, available information and the type of data to be imputed. The imputed data are again examined for completeness and consistency.
Given the non-probabilistic selection, the estimation is based on a data integration strategy aiming at reducing the bias by calibrating the panel totals to estimated totals from the Annual For-Hire Trucking Survey.
The relationships between the Freight Trucking Statistics variables and the auxiliary variables from the annual survey are modeled from the non-probability panel. The result is an inference conditional on panel inclusion and the auxiliary variables.
The survey results are analyzed before dissemination. In general this includes a detailed review of the individual responses (especially for the largest enterprises), a review of general economic conditions as well as historic trends and comparisons with industry averages and ratios.
Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential.
If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data. Data for a specific industry or variable may be suppressed (along with that of a second industry or variable) if the number of enterprises in the population is too low.
Revisions and seasonal adjustment
Annual estimates are provided for the reference year. The data for the previous reference year are revised if necessary. As this is an annual survey, seasonal adjustments are not applicable.
While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.
Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.
Non-sampling errors are controlled through a careful design of the questionnaire, the use of a minimal number of simple concepts and consistency checks. Coverage error was minimized by using multiple sources to update the frame. Measures such as response rates are used as indicators of the possible extent of non-sampling errors.
Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.