Bibliographic record
Abstract
We present a new urban transit investment model, integrating transport economic theory regarding optimal investment with transport modeling, planning, and network design. The model expands on the theory of optimal transit network planning and investment, accounting for the effects of the investment on accessibility, level of service, and speed. The model seeks long-term optimal transit investment and optimal road pricing simultaneously in an integrated, unified model. To illustrate the advantages of our approach, we applied our empirical model to two case studies, Tel Aviv and Toronto, integrating our theoretical contribution into practice. Our results demonstrate the model’s ability to indicate the optimal transit mode and investment on a corridor level and the total investment required for the city transit network. The model results were compared to the actual and planned transit networks of Tel Aviv and Toronto and showed the model’s capability to produce a good balance of strategic design and network details. The research concludes that applying the right toll with the applicable transit investment is crucial for obtaining an efficient network and performance. This research can direct planners and policymakers in planning urban transport and provide a comprehensive set of guidelines for optimizing the simultaneous investment in mass transit and the congestion toll toward more sustainable cities and transportation systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".