Mobility-as-a-Service and Demand-Responsive Transport: Practical Implementation in Traditional Forecasting Models
Bibliographic record
Abstract
The increasing effect of Transport Network Companies (TNCs) in major US cities, allied to the expectation that connected and autonomous vehicles (CAVs) will become the prevailing type of automobile on the streets in the coming decades, requires such a trend to be reflected in our forecasting models. Yet, most of the efforts undertaken in the United States and elsewhere are largely focused on understanding the demand for such types of transportation and have left aside the analysis of crucial differences in envisaged systems, such as pooled versus single-occupancy vehicles, use of CAVs as access modes to mass transit, and the cost corresponding to different levels of service in citywide systems. In this paper we introduce a new algorithm for modeling pooled CAVs and a framework for integrating this into traditional forecasting models. We also present the preliminary results of an application of the proposed methodology to the metropolitan region of Vancouver, British Columbia. The results are promising, although a few implementation choices made for this study have resulted in poor computational performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".