Strategic Planning and Decision Making in State Departments of Transportation
Bibliographic record
Abstract
This report examines the experience of state and provincial departments of transportation (DOTs) with strategic planning and synthesizes current approaches to linking strategic planning with other decision-making processes, including operational and tactical planning, resource allocation, performance management, and performance measurement. It will be of interest primarily to chief executive officers, executive team members, and other officials who are responsible for developing, supporting, and using strategic management systems in state and provincial DOTs. The report is intended to help these industry leaders strengthen the overall performance of their organizations by examining exemplary practices in various DOTs. Case studies are also provided documenting one transportation agency that has used strategic planning over an extended period and one that recently implemented strategic planning. This synthesis report contains information drawn from survey responses from U.S. state and Canadian provincial transportation agencies. Follow-up telephone interviews were conducted with relevant personnel from many of those agencies that responded to the survey and some that did not to clarify responses and probe additional issues. A review of the relevant literature was also undertaken to provide background on the topic, help define the overall approach, and discuss the limitations of strategic planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".