What Happened to Speed?: Scheduled Speeds and Travel Times of North American Passenger Trains, 1965 to 2015
Bibliographic record
Abstract
Most larger North American railroads provided intercity passenger service until the 1970s, when the industry experienced a difficult restructuring period. During that decade, the U.S. and Canadian federal governments took responsibility for intercity passenger trains while financially troubled railroads (especially in the U.S. Northeast and Midwest) were downgrading lines and deferring maintenance. Although railroads returned to financial health in the 1980s and 1990s largely because of deregulation, track capacity was substantially reduced compared with the 1960s level, and most lines were optimized for freight service rather than passenger service. To assess the effects of these changes on scheduled travel times and speeds, intercity passenger timetables for specific routes were examined for selected years between 1965 and 2015. Although certain long-distance routes were scheduled for noticeably slower speeds in 2015 than in 1965, other scheduled speeds decreased only marginally. On long-distance routes and in corridors outside the northeastern United States, track capacity reductions, freight train interference, or both seem to have placed downward pressure on scheduled speeds. Scheduled speeds have increased, or at worst, decreased slightly on lines that have received substantial investment. Massive federal investment in the Northeast Corridor since the 1970s has resulted in considerably faster schedules.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".