Bibliographic record
Abstract
Saskatchewan is in the midst of a population and economic boom, driven by resources such as oil and gas. At the same time, the distribution of other industries, such as agriculture, is changing, as the consolidation of rail transfer points forces farmers to truck their wheat longer distance; and the catchment areas of the Province's two largest urban activity centres – Regina and Saskatoon – continue to expand. One result is that the Province must consider rapidly growing demands for private and commercial travel on its highways, and the resultant impacts for improvements, maintenance and rehabilitation. To help in the planning decision-making, the Saskatchewan Ministry of Highways and Infrastructure (MHI) is considering the development of a Province-wide travel demand forecasting model. The model would focus on highway traffic, and would be integrated with the existing urban models. As a first step, MHI studied the data requirements for a Province-wide model. The study began by defining potential modelling approaches. Based on this definition, an inventory was prepared of existing data sources among Provincial, Federal and local agencies that potentially could be used to develop the model. Over 30 sources were found, comprising both travel data, such as traffic counts and origin-destination surveys, and demographic and socio-economic data. The inventory considered completeness, geographical coverage, representativeness, levels of spatial and temporal detail and ease of use, among other attributes. It also examined the ability to forecast the data at the necessary geographies. Next, the available data were assessed against the needs of the potential modelling approaches. From this, data gaps were identified as the basis for the specification of several data collection options. The options, and combinations of these options, are designed to allow the Province-wide model to evolve as different needs arise and as funding allows. The paper describes the inventory and the proposed data collection options. Importantly, the options focused on the feasibility of emerging electronic data collection technologies, including cellphones, GPS traces and loggers. The paper also speaks to the practical challenges and opportunities from the use of these data, especially the electronic sources. Although the paper describes a rural / inter-urban application, it should be of interest to urban as well provincial transportation planners, many of whom are faced with similar data needs and opportunities in developing forecasts for their own jurisdictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".