Strategic Station Access Planning for Commuter Rail
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At most suburban rail stations, park-and-ride is the dominant use and the preferred access mode for most riders. Many transit agencies are trying to reduce their reliance on park-and-ride facilities and to encourage greater access by more sustainable modes. The recently released TCRP Report 153: Guidelines for Providing Access to Public Transportation Stations outlines a process to identify multimodal access priorities at high-capacity transit stations, and to weigh the benefits and trade-offs. This paper presents a case study analysis of how this station access planning process could be adapted and applied to a commuter rail network. The analysis considered the GO Transit rail system, which at the time of the study operated more than 65,000 park-and-ride spaces across 62 stations in the Greater Toronto and Hamilton area of Ontario, Canada. In general, the TCRP process provided an effective approach to develop a strategic station access plan. However, several ways in which the process could be improved were identified. The paper recommends policy scenario analysis as a consultative and analytical approach to prepare a systemwide station access policy. The paper also presents a decision-making framework to assess parking needs at the individual station level and provides an example of how this framework was used to make trade-offs during the station access planning process, with balanced investment in park-and-ride and other access modes. Overall, station access planning exercises should attempt to build recommendations from the top down (i.e., station access policy) and the bottom up (i.e., decision-making framework) to ensure that proposed solutions support the overall policy direction while they respond to the individual station context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it