Planning Capacity Improvements in the Chicago – Milwaukee – Madison Rail Corridor Using the Rail Traffic Controller (RTC) Rail Operations Simulation Model
Bibliographic record
Abstract
The Wisconsin Department of Transportation (WisDOT) contracted HNTB Corporation (HNTB) to utilize the Rail Traffic Controller™ (RTC™) computer simulation software developed by Berkeley Simulation Software to analyze the rail capacity requirements for high speed (110 mph maximum) Chicago to Milwaukee to Madison passenger rail service. The purpose of this study was to determine whether sufficient capacity exists in the corridor to accommodate the projected growth in intercity passenger rail service as well as growth in freight and commuter rail service. Where capacity constraints were identified, the model was also used to evaluate the benefits of proposed infrastructure improvements. HNTB and WisDOT worked with Illinois DOT, the Canadian Pacific Railway, Metra and Amtrak to identify and test rail improvements that will provide sufficient capacity for projected future high speed, commuter and freight rail services in the corridor. The modeling results are shown through string lines and tables comparing the impacts of each case on the performance of each type of train. Metrics used include: • Average speeds. • Delay minutes per 100 miles. • On Time Performance (for passenger trains). RTC is a very useful tool for the simulation of current and proposed rail operations. It has helped identify bottlenecks and analyze the effectiveness of proposed improvements. The model results from this study are a critical component in WisDOT’s negotiations with CP over the extent and location of capacity improvements for high speed operations. The RTC model output also supported WisDOT’s application for federal stimulus funding for the corridor improvements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".